Hierarchical control - smart transmission grids = - prototipo de redes inteligentes para control jerárquico de sistemas interconectados

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(1)HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS. HAROLD RENÉ CHAMORRO VERA. UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA BOGOTÁ D.C. 2012.

(2) HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS. HAROLD RENÉ CHAMORRO VERA. TESIS DE MAESTRÍA PARA OPTAR AL TÍTULO DE MAGÍSTER EN INGENIERÍA ÁREA: INGENIERÍA ELÉCTRICA. ASESOR Ing. MARIO ALBERTO RÍOS MESÍAS. PhD. UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA BOGOTÁ D.C. 2012.

(3) PROTOTIPO DE REDES INTELIGENTES PARA CONTROL JERÁRQUICO DE SISTEMAS INTERCONECTADOS. HAROLD RENÉ CHAMORRO VERA. TESIS DE MAESTRÍA PARA OPTAR AL TÍTULO DE MAGÍSTER EN INGENIERÍA ÁREA: INGENIERÍA ELÉCTRICA. ASESOR Ing. MARIO ALBERTO RÍOS MESIAS PhD. UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERÍA ELÉCTRICA Y ELECTRÓNICA BOGOTÁ D.C. 2012.

(4) HIERARCHICAL CONTROL - SMART TRANSMISSION GRIDS. Harold René Chamorro Vera Universidad de Los Andes. (Abstract). The technological development and enhancement of new strategies related with Wide Area Measurement and Control Systems (WAMCS) is playing a key role with large interconnected power systems in order to assure stability under Low Frequency Oscillations (LFO). In this thesis a hierarchical intelligent controller scheme for multi-machine systems is presented with the purpose to maximise the damping factor of intra-area and inter-area oscillations combining the principles of Fuzzy Logic Control and aMulti-Agent Systems (MAS) architecture. The controller designing takes advantage of the controllability and observability definitions to obtain the optimal location of the local Power System Stabilizers (PSS) and the Phasor Measurement Units (PMU) where the local PSS has been modelled with fuzzy logic as well. An algorithm to measure the damping of any signal is developed and used as PMU measurement in the tie-lines. The time-domain response of the designed controller is tested in a power benchmark system demonstrating its adaptability and performance..

(5) To my mom.

(6) Research is to see what everybody else has seen, and to think what nobody else has thought. ~Albert Szent-Gyorgyi.

(7) ACKNOWLEDGEMENTS. First of all and the most important thing is that I am deeply indebted to my supervisor Prof. Mario Ríos for his support, motivation and inspiring discussions throughout the years of my studies. He always had suggestions, answered my numerous questions, and put forth the effort to help me progress. I benefited a lot from his great research experience, technical advices and all courses he taught.. I am grateful to Dr. Mauricio Guerrero for helping me in my initial phase and for getting me started in working on powersystems in the university.. It has been an honour to be associated with the Department of Electrical and Electronics Engineering at Universidad de los Andes.. I would also like to extend my gratitude to Dr.Gustavo Ramos for his continuous help and support. I believe that I have been truly blissful by working with him over the past two years. I have benefited not only from his knowledge in power quality area, but also from his keen personality. I wish him well in all his future endeavours.. I would also like to thank my colleagues, for their valuable comments and assistance, and wish them all the best of luck and brightest future. Special thanks to Mr.CamiloOrdoñez who was not just a colleague but a brother who I will always remember. His deep insights in the field of power systems and our long.

(8) and creative discussions have made this collaboration particularly fruitful. Without him, many results couldn’t have been obtained and the part on power systems would be significantly shorter and weaker.. Thanks and appreciation is given to Dr. Ricardo Moreno whose advice and encouragement was always of great help. He helped me significantly with all my questions about practically everything related to power systems and optimization. Indeed, I have discussed most of my ideas first with him due to his invaluable feedback.. Thanks go also to Dr. Oscar “Simon Dice” Gomez, who is not only the group’s PSAT guru, but also for having time to answer my numerous questions about power systems in general towards the study case.. I would like to send special thanks to Ms. Gloria Martinez, for helping me with any administrative procedure or power system stability questions I had. She was more than supportive all of the time.. I have been fortunate to come across many funny and good friends, without whom life would be bleak and I cannot forget, – in particular Mr. Alfredo “Degenerati” Tobón, Mr. Juan Alberto “Pulu”Ramirez, Mr.Juan David “Poli” Beltran, Mr.Juan Carlos “Totuma” Díaz, Mr. Nelson “Gala” Barreraand Mr.JulioMonroy..

(9) I would like to express my deepest appreciation to Ms. Manuela Medina, Ms. Alejandra “LiaLios” Fajardo and Ms.Dayana Herrera. Their endless love, support and encouragement were emotionally critical, without which my pursuit of a master’s degree would not have been possible.. I present my appreciation to all of my professors and teachers from the department and those from all other segments of life: Prof.Fernando Jimenez, Prof.Angela Cadena, Prof. Alvaro Torres, Prof. NicanorQuijano and Prof. Alain Gauthier for their help, discussions and advice.. For this research, some details are essential. Many people helped with this, for which I would like to thank them whole heartedly, special thanks to Mr. Andres “Iguazo” Leal and Ms. Ana MaríaOspina. Without their generosity there would be nothing to work with.. I would also like to acknowledge Mr.Efren Martinez, Mr. Elkin “El-king” Cantor, Mr. Diego “Fosforito” Salamanca, Mr. Jose Daza, Mr. Jaime Osorio, Mr. Cesar Rodríguez, Mr. Daniel Blandón and Mr. Victor Melo for taking over the main work load on power systems and collaborating with me on this topic.. I also express my gratitude to everyone in the IEEE Uniandes Student Branch. These include, just tomention a few: Mr. Juan Sebastian Moya, Mr. Carlos Quintero, Ms. Jessica Buritica, Mr. Jorge MarioGarzon, Ms. Diana Pardo, Ms. Paula Florez, Mr. Gabriel Sanchez, Mr. Diego Campo and Mr. Nicolas Velasquez..

(10) Looking back, the productive phase of my master’s degree was triggered by Mr. Carlos Macana and Ms. Liz Catherine Hernandez, who suggested me to study and introduced me to the field of Power Systems, affected and inspired me with their creativity, taught me many things, and showed me how to focus on the important aspects of research. I am deeply grateful for that.. Furthermore, I am grateful to collaborate and work with Mr. Andres Ovalle, Mr. Andres Puentes and Mr. Jose Calderónachieving excellent results that got later awarded and published.. I am also glad to have had the opportunity to work with the Power and Energy Group in the University and to know their members who provided a great and pleasant atmosphere and helped me achieve my goal.. Last but not least, I would also like to thank my mom for her never-ending support, love, encouragement and prayers that helped me complete this work. She gave me the opportunity to be here and pursue this degree and reminded me that there is a life beyond the university. Nothing I can say, can thank her enough.. I would like to acknowledge that there is a greater power than me that made all of this possible. Thanks God for all his wonderful blessings, without Him, none of what I haveachieved would exist..

(11) TABLE OF CONTENTS I.. INTRODUCTION .................................................................................................................... 7. II.. OBJECTIVES ......................................................................................................................... 11. 1.. 2.. 3.. 4.1. General Objective ........................................................................................................ 11. 4.2. Specific Objectives ....................................................................................................... 11. SMART TRANSMISSION GRIDS ............................................................................................ 12 1.1. Background .................................................................................................................. 12. 1.2. Intelligence Requirement ............................................................................................ 13. 1.3. Multi- Agents in Smart Transmission Grids ................................................................. 14. 1.4. Thesis Statement ......................................................................................................... 15. 1.6. Organisation of the thesis ............................................................................................ 16. PMU AND PSS LOCATION .................................................................................................... 17 2.1. Power System Proposed .............................................................................................. 18. 2.2. Eigenvalue Analysis ...................................................................................................... 19. 2.3. Participation Factors .................................................................................................... 20. 2.4. Mode Shapes ............................................................................................................... 20. 2.5. Controllability Analysis ................................................................................................ 21. 2.6. Voltage Stability Analysis ............................................................................................. 23. ON-LINE MEASUREMENTS .................................................................................................. 26 3.1. Damping Algorithm Proposed ..................................................................................... 27. 3.2. Relative Amplitude and Frequency Measurements .................................................... 30. 3.3. Signal Dynamic Deviation ............................................................................................ 31. 3.4. Algorithms Tests .......................................................................................................... 32. 3.4.1 Damping Factor Measurement .................................................................................... 32 3.4.2 Frequency Measurement ............................................................................................ 33 3.4.3 Relative Amplitude Measurement ............................................................................... 34 4. MULTI-AGENT SYSTEM DESIGN ........................................................................................... 36 4.1. Application Design ....................................................................................................... 37. 4.1.1 Knowledge Modelling .................................................................................................. 37 4.1.1.1 Local Control Model ..................................................................................................... 38 4.1.1.2 Supervisor Control Model ............................................................................................ 40 4.1.2 Tasks Roles ................................................................................................................... 43 4.1.2.1 User Access Task .......................................................................................................... 44 1.

(12) 4.1.2.2 Control task .................................................................................................................. 44 4.1.2.3 Measurement Task ...................................................................................................... 44 4.1.2.4 Coordination Task ........................................................................................................ 44 4.1.2.5 Bulletin Board Tasks ..................................................................................................... 46 4.1.3 Agent Specification ...................................................................................................... 46 4.1.3.1 Control Agent ............................................................................................................... 46 4.1.3.2 User Agent ................................................................................................................... 47 4.1.3.3 Monitor Agent ............................................................................................................. 47 4.2 5.. Constraints ................................................................................................................... 47. MULTIAGENT SYSTEM APPLICATION AND STUDY CASE ..................................................... 48 5.1. Transient Stability ........................................................................................................ 49. 5.2. Results and Discussion ................................................................................................. 53. 6.. CONCLUSIONS ..................................................................................................................... 55. 7.. FUTURE WORK .................................................................................................................... 56. 8.. REFERENCES ........................................................................................................................ 56. 2.

(13) LIST OF FIGURES Fig. 1 Derived Power System [42] ............................................................................. 18 Fig. 2 Power System Proposed................................................................................... 19 Fig. 3 Mode Shape Mode 4 ......................................................................................... 21 Fig. 4 Mode Shape Mode 5 ......................................................................................... 21 Fig. 5 Controllability Mode 4 ..................................................................................... 22 Fig. 6 Controllability Mode 5 ..................................................................................... 22 Fig. 7 PSS and PMU Location .................................................................................... 23 Fig. 8 Voltage Stability Analysis ................................................................................ 23 Fig. 9 Stability Plane .................................................................................................... 24 Fig. 10 Damping Algorithm ....................................................................................... 27 Fig. 11 Damping Algorithm Embedded ................................................................... 28 Fig. 12 Damping and Relative Amplitude Measurement Block ........................... 29 Fig. 13 Damping Measurement ................................................................................. 29 Fig. 14 Frequency Measurement Simulink Diagram .............................................. 30 Fig. 15 Frequency Measurement Block ..................................................................... 31 Fig. 16 Signal Deviation Implementation ................................................................. 31 Fig. 17 Damping Measurement between areas 1 and 2.......................................... 32 Fig. 18 Damping Measurement between areas 1 and 3.......................................... 33 Fig. 19 Frequency Measurement between areas 1 and 2 ........................................ 33 Fig. 20 Frequency Measurement between areas 1 and 3 ........................................ 34 Fig. 21 Relative Amplitude Measurement between areas 1 and 2 ....................... 34 Fig. 22 Relative Amplitude Measurement between areas 1 and 3 ....................... 35 Fig. 23 Hierarchical Controller Concept ................................................................... 36 Fig. 24 Proposed Controller Scheme ......................................................................... 37 Fig. 25 Speed deviation and Power Active deviation Input Memberships functions representation ............................................................................................. 39 Fig. 26 Output Membership Functions..................................................................... 39 Fig. 27 Takagi Sugeno Control Proposed ................................................................. 41 Fig. 28 Input Membership Functions of the Hierarchical Controller ................... 41. 3.

(14) Fig. 29 Input Membership Functions of the Hierarchical Controller (Deviation) ........................................................................................................................................ 42 Fig. 30 Surface Control Associated............................................................................ 42 Fig. 32 Multi – Agent Framework ............................................................................. 48 Fig. 33 Simpower System Implementation .............................................................. 49 Fig. 34 Electrical Power Time Response of Generators (Local) ............................. 50 Fig. 35 Electrical Power Time Response of a Tie-line (Remote) ............................ 50 Fig. 36 Electrical Time Response in the Tie-lines with LC (Local Controllers) and HC (hierarchical controllers) ...................................................................................... 51 Fig. 37 Electrical Time Response in the Tie-lines with LC (Local Controllers) and HC (hierarchical controllers) ...................................................................................... 51 Fig. 38 Electrical Power Time Response of Generators with conventional PSS as long as it is applied the Hierarchical Control .......................................................... 52 Fig. 39 Electrical Power Time response of Generators with the two fuzzy controllers ..................................................................................................................... 52. 4.

(15) LIST OF TABLES Table. I System Eigenvalues ....................................................................................... 19 Table. II Participation Factors .................................................................................... 20 Table. III Eigenvalue Analysis in Buses ................................................................... 24 Table. IV Decision Rules ............................................................................................. 40 Table. V Decision Rules .............................................................................................. 43 Table. VI Controller Comparison .............................................................................. 53. 5.

(16) GLOSSARY AI: Artificial Intelligence FACTS: Flexible AC Transmission Systems FLC: Fuzzy Logic Control LFO: Low Frequency Oscillations MAS: Multi-Agent Systems PMU: Phasor Measurement Unit PSS: Power System Stabilizer RFS: Remote Feedback Signals SMT: Synchronised Measurement Technology STG: Smart Transmission Grid TSO: Transmission System Operator WAMS: Wide Area Measurement Systems WACS: Wide Area Control System WAMCS: Wide Area Measurement and Control System. 6.

(17) I. INTRODUCTION. As a result to the migration to the new power systems concept known as “Smart Grid” and conceived as the intelligent automation of electrical transmission and distribution networks, many different initiatives have been proposed, especially in small-signal stability area with the purpose to damp the electromechanical oscillations to assure stable operation of interconnected systems[1]-[3].. With the development of Wide Area Measurements and Control Systems (WAMCS) and the technological improvements in the past three decades, the use of Phasor Measure-ment Units (PMU) has become a reality [4][5], providing of on-line measurements and bringing the opportunity to design real-time controllers and algorithms with more accuracy and precision to get the correspondent supervision of any power system, so that as a general rule, the Smart Grid systems require the application of intelligent control systems in order to face and solve the imminent problems related and to give some autonomous decisions.. Intelligent control based on Artificial Intelligence (AI) and soft computing techniques have played an important part in different systems solving several problems in engineering. Fuzzy logic as a method of AI has been applied in many electrical systems control-related with success [6][7][8].. The use of fuzzy logic in power systems is based on its inherent advantages like its tolerance with imprecise data, its flexibility and adaptability and the behavioural abstraction model of large systems capacity without mathematical complex equations. 7.

(18) Concerning to Smart Grids, the applicability of Fuzzy Logic Control (FLC) has been extended to multiple applications such as fault management [9], selfhealing and diagnose [10], load forecasting [11], and reconfiguration or restoration [12]. Therefore, there is a high interaction between fuzzy systems and Smart Grid systems, in which is required some kind of autonomous decisions under disturbances or operation conditions.. In the framework of the Smart Transmission Grids it has contemplated some control challenges, which refers to the control centres, their methodology and the intelligence that can be provided to them and the smart measurements involved [13].. One of the main problems in transmission levels concerns to the power fluctuations of small magnitude and Low Frequency Oscillations (LOF) which can limit the amount of power able to transfer [14], and producing instability that can provoke outages or several damages along the interconnected systems.. The problem of LOF presented in large interconnected systems concerns in general to different operative regions and involves different Transmission System Operators (TSO). A disturbance event should be monitored by different TSO supported by a communication infrastructure established [15], however, at the moment the TSO are operated almost uncoordinated based on a CentralTSO with the logistic actions to solve.. In order to mitigate these oscillations, the Power Systems Stabilizers were developed as supplementary device that manipulate the injected excitation to the synchronous machine [16][17].. 8.

(19) Even though the conventional PSS has been tested and shown the attenuation of the undesired LOF with good results, some improvements are necessary to be done due the load variability and the different operation conditions [18]. In that sense, different works related with PSS improvements using FLC have been proposed with the objective to do some enhancements as diverse as selftunning[19], self-learning [20], comparing different control techniques [21] or even comparing other defuzzification methods [22].. Consequently, many supervisory and hierarchical control architectures have been presented in order to increase the performance of conventional PSS under severe disturbances or high oscillatory systems [23] or coordinate them with Flexible AC Transmission Systems (FACTS) in large systems where are required [24].. Some new studies in supervisory-hierarchical controllers based FLC have been proposed [25][26], two of them include PMU to measure remote signals [1], [27].. In addition, some current perspectives of WAMCS have shown the requirement of coordinated layers or decision systems with different objectives and process priorities, executing actions in the local controllers or process therein, which implies changes in set-points or settings [28].. Along these lines, Muli-agent systems (MAS) have provided of cooperative, coordination and communication features in different applications in power systems [29] -[30], nevertheless these kinds of architectures have not been investigated at all and require more research.. 9.

(20) This document appears with the purpose to contribute to the evolution and smartness initiative of power systems, especially in transmission systems, proposing a Hierarchical Multi-Agent System based Fuzzy Logic Control (HMASFLC) to increase the damping in the tie-lines and reducing the LOF at minimum, involving Remote Feedback Signals (RFS), on-line measurements and, local fuzzy controllers and measurements as well.. 10.

(21) II. OBJECTIVES 4.1 General Objective To develop an appropriate hierarchical control methodology for the use in smart grids with the purpose to assure the power system integrity associated with the interconnections.. 4.2 Specific Objectives. To establish and characterise a suitable power system for the action control execution.. To plan the required action control according to the contingency and stability analysis.. To study and compare different control strategies (classical, modern or intelligent) that can be applied to the studiedpowersystem.. To determine a control technique and evaluate its performance in the system before different goals.. To emulate the monitoring by PMU (Phasor Measurement Unit) and the WAMCS concept to analyse whether the controller decisions area appropriate.. 11.

(22) 1. SMART TRANSMISSION GRIDS. 1.1 Background. Different energy programs related with Smart Transmission Grids (STG) are being developed around the world, some of them are briefly mentioned and summarised as follows.. The IntelliGrid program, initiated by the Electric Power Research Institution (EPRI), is to create the technical foundation for a smart power grid that links electricity with communications and computer control to achieve tremendous gains in the enhancements of reliability, capacity, and customer service [31][32]. This program provides methodologies, tools, and recommendations for open standards and requirement-based technologies with the implementation of advanced metering, distribution automation, demand response, and wide-area measurement. The interoperability is expected to be enabled between advanced technologies and the power system.. The SmartGrids program, formed by the European Technology Platform (ETP) in 2005, created a joint vision for the European networks of 2020 and beyond [33][34]. Its objective features were identified for Europe’s electricity networks as flexible to customers’ requests, accessible to network users and renewable power sources, reliable for security and quality of power supply, and economic to provide the best value and efficient energy management.. A Federal Smart Grid Task Force was established by the U.S. Department of Energy (DoE) under Title XIII of the Energy Independence and Security Act of 2007. In its 2030 Grid vision, the objectives are to construct a 21st-century 12.

(23) electric system to provide abundant, affordable, clean, efficient, and reliable electric power anytime, anywhere [35]. The expected achievements, through smart grid development, will not merely enhance the reliability, efficiency, and security of the nation’s electric grid, but also contribute to the strategic goalof reducing carbon emissions.. Remarkable research and development activities are also ongoing in both industry and academia. References [36] and [37] present smart grids for future power delivery. Reference [38] discusses the integration issue in the smart grid. Specific technologies, such as smart metering infrastructure, are presented in [39].. 1.2 Intelligence Requirement. As a general requirement to improve the current transmission grid to the new “Smart Grid” concept some developments are necessary to do in order to achieve that smartness.. Intelligent technologies and human expertise will be incorporated and embedded in the smart transmission grid. Self-awareness of the system operation state will be available with the aidof online time-domain analysis such as voltage/angular stabilityand security analysis. Self-healing will be achieved to enhancethe security of transmission grid via coordinated protection andcontrol schemes.. Smart sensing and measurementand advanced instrumentation technologies will serveas the basis for communications, computing, control, andintelligence. 13.

(24) Intelligent technologies willenable fuzzy logic reasoning, knowledge discovery, andself-learning,. which. are. important. ingredients. integratedin. the. implementation of the above advanced technologiesto build a smarter transmission grid[13].. In the future controlcentre, the system-level information will be obtained fromthe state measurement modules based on phasor measurementunits (PMUs) [40],[41]. The PMU-based state measurement isexpected to be more efficient than the present state estimationsince synchronized phasor signals provide the state variables,in particular, voltage angles.. 1.3 Multi- Agents in Smart Transmission Grids. The intelligent agents at transmission network devices or substations may interact with neighbour agents to achieve broader information in order to make improved decisions without extensive communication back to the control center. In short, the actual control action will be a combination of local decisions from the distributed intelligent agents, central decisions from the smart control center, and the “regional” decision based on the information exchange among peer substations and network devices. Each type of action shall have a different response time and it’s the most efficient for a particular type of work. The actual control process may require a few iterations among the three types of actions.. The objective is to contribute to the development of future monitoring, state estimation. and. control. applications. based. on. synchronized. phasor. measurements to improve power system security and increase utilization of the transmission grid. The main focus will be on dynamic phenomena like voltage 14.

(25) stability and the damping of power oscillations over wide geographical areas, where to locate sensors to improve the situational awareness (e.g. power oscillations) as well as design and placement of actuators for improved performance (e.g. stabilizing the oscillations)[13].. 1.4 Thesis Statement. The objective of this research is to design, develop and implement a hierarchical control system that assures stability in a power system interconnected. These include the management and control algorithms withon-line measurements.. 1.5 Research Goals. The focus of this thesis is the design of a hierarchical control applying remote feedback signals used for the damping of LOF, inter-area oscillations in a power systems proposed as well which is composed by 6-machine/ 3-areas. A methodology based on eigenanalysis is derived to locate the local controllers in the test system.. Also demonstrated is the resulting tie-line power transfer gain due to the damped oscillations. Finally, time-domain simulations performed on the test system will be employed to study the nonlinear response following large disturbances.. 15.

(26) 1.6 Organisation of the thesis. The remainder of this document is organised as follows: in chapter2, it is analysed the study benchmark system and obtained the PSS and PMU location. In chapter3, the on-line measurements algorithms are described and tested. In chapter4 is shown the local controller and it is presented the hierarchical controller design based multi-agents. In chapter5, it is compared the action of the proposed hierarchical control strategy with the local controllers only. Finally, the conclusions are presented and a future work is given.. 16.

(27) 2. PMU AND PSS LOCATION. In the context of transmission grids, Small Signal Stability Analysis and in particular, the analysis of inter-area oscillations becomes more and more important. Many electric systems worldwide are experiencing increased loading on portions of their transmission systems that can, and sometimes do, lead too poorly damped, low frequency (0,2-0,8 Hz) inter-area oscillations. This topic has been extensively addressed for long time in conjunction with power systems for which the extension of the grid and the high level of power transfers led to stability problems. Inter-area oscillations can severely restrict system operations by requiring the curtailment of electric power transfers as an operational measure. These oscillations can also lead to widespread system disturbances if cascading outages of transmission lines due to oscillatory power swings.. The eigenvalue analysis investigates the dynamic behaviour of a power system under different characteristic frequencies (“modes”). In a power system, it is required that all modes to be stable. Moreover, it is desired that all electromechanical oscillations to be damped out as quickly as possible. The results of an eigenvalue analysis are given as frequency and relative damping for each oscillatory mode to make them easier to understand.. In addition, the modal analysis allows a much deeper view of a system by not only interpreting the eigenvalues but by analysing the eigenvectors of a system, which are automatically calculated during the modal analysis:. -. The right eigenvector gives information about the observability of oscillation. 17.

(28) -. The left eigenvector gives information about the controllability.. -. The combination of right and left eigenvectors (residues) indicates the controllers’ settings.. 2.1 Power System Proposed. In order to study the control structure proposed it is derived a power system from [42], with some modifications. The original system is presented inFig.1.. Fig.1 Derived Power System [42]. The system proposed is conformed by 3-areas; each of the areas has two generators, and links with weak tie-lines. The generators are identical and modelled with 6 state variables and the Automatic Voltage Regulators (AVR) are also identical and represented by 2 state variables. The system is shown inFig.2.. 18.

(29) Fig.2 Power System Proposed 2.2 Eigenvalue Analysis. There are many possibilities to locate the PSS along the AVR of the system and the PMU as well. To face the problem of knowing the optimum site, it is applied a well-known methodology based on the Small Signal Stabiliy Analysis (SSSA), particularly the controllability analysis [43][45]. The eigenvalues of weak damping influence in a high way the dynamic stability of power systems and the eigenvalues of lower frequencies deal with the interarea power oscillations. Table.I and Table.II show two dominant eigenvalues and participation factors given by the analysis. Table.I System Eigenvalues . No. R. Part Im. Part Frequency Damp. 1. -0.58. 6.99. 1.12. 8.3%. 2. -0.69. 6.89. 1.10. 8.5%. 3. -0.61. 6.64. 1.06. 9.1%. 4. -0.26. 4.75. 0.76. 5.5%. 5. -0.23. 4.17. 0.66. 5.4%. 19.

(30) 2.3 Participation Factors The participation factor element gives a measure of the kth state variable in aith mode, and vice versa. Table.II Participation Factors No. G1. G2. G3. G4. G5. 1. 33% 6%. 2%. 1%. 47% 3%. 2. 2%. 42% 21% 6%. 2%. 18%. 3. 7%. 10% 35% 27% 3%. 10%. 4. 0%. 13% 12% 31% 0%. 38%. 5. 36% 5%. 5%. G6. 12% 27% 10%. FromTable.I, it can be seen that the oscillatory modes are mainly 4 and 5, however from Table.II it is not totally clear where the PSS might be located.. 2.4 Mode Shapes. The mode shape is the response of a particular oscillatory mode in the right eigenvector.. When the Mode Shapes (MS) of the critical modes (4 and 5) are plotted (Fig.3 and Fig.4), it can be inferred that there is an inter-area oscillation due to generators G2 and G6 against G3 and G4; in other words, area 1 vs. area 3. In the other critical mode, generators G1 and G5 are oscillating against G4 and G6 mainly, so there is another inter-area oscillation (area 2 vs areas 1&3).. 20.

(31) Mode 4: 0.75658 Hz 0.08. Axis x Axis y Gen1 Gen2 Gen3 Gen4 Gen5 Gen6. 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.2. -0.15. -0.1. -0.05. 0. 0.05. 0.1. 0.15. Fig.3 Mode Shape Mode 4. Mode 5: 0.66342 Hz 0.08. Axis x Axis y Gen1 Gen2 Gen3 Gen4 Gen5 Gen6. 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.2. -0.15. -0.1. -0.05. 0. 0.05. 0.1. 0.15. Fig.4 Mode Shape Mode 5. To guarantee the observation of those modes in the mentioned areas the PMUs are located in the correspondent tie-lines, especially in the buses 5 and 12.. 2.5 Controllability Analysis. The controllability index of the system shows that the PSS might be located in G1, G4 and G6 as it is depicted in Fig.5 and Fig.6.. 21.

(32) Controlability Mode 4 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0. 1. 2. 3. 4. 5. 6. Machines. Fig.5 Controllability Mode 4 Controlability Mode 5 1.4 1.2 1 0.8 0.6 0.4 0.2 0. 1. 2. 3. 4. 5. 6. Machines. Fig.6 Controllability Mode 5. According to the analysis shown above, Fig.7 shows which of the PSS are activated and where the PMU are placed.. 22.

(33) Fig.7 PSS and PMU Location. 2.6 Voltage Stability Analysis Once it is located the PMU and PSS, it is necessary to establish how it can be critical a fault in any line in the system. In order to identify the most critical line(s) of the power system, all possible single contingencies (n-1) are simulated, obtaining that the outage of all lines caused the system instability. The voltage stability shows that any disturbance in any line in the system can generate instability (Fig.8). 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0. 0.1. 0.2. 0.3. 0.4. 0.5 Loading Parameter. 0.6 (p.u.). 0.7. 0.8. 0.9. 1. Fig.8 Voltage Stability Analysis 23.

(34) What is more thesystem shows a critical mode in bus 6 as it can be seen in Fig.9. andTable.III.. 1 0.8 0.6 0.4. Imag. 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -200. 0. 200. 400 Real. 600. 800. 1000. Fig.9 Stability Plane. Table.III Eigenvalue Analysis in Buses Eigevalue Most Associated Bus Real part EigJlfr # 1 Bus 05. 148,9455. EigJlfr # 2 Bus 12. 96,48864. EigJlfr # 3 Bus 06. 3,66359. EigJlfr # 4 Bus 07. 56,24301. EigJlfr # 5 Bus 04. 912,2962. EigJlfr # 6 Bus 13. 766,825. EigJlfr # 7 Bus 14. 630,2312. EigJlfr # 8 Bus 02. 650,041. EigJlfr # 9 Bus 08. 721,5046. EigJlfr #10 Bus 10. 622,1418. EigJlfr #11 Bus 01. 999. EigJlfr #12 Bus 03. 999. EigJlfr #13 Bus 09. 999 24.

(35) Eigevalue Most Associated Bus Real part EigJlfr #14 Bus 11. 999. EigJlfr #15 Bus 15. 999. EigJlfr #16 Bus 16. 999. In the remainder of this document the fault considered is going to be located in bus number 6 in order to analyse the system transient stability. Thus, a 500 ms fault is simulated at in bus 6 (between areas 1 and 3), obtaining oscillations of important magnitude in voltage and power.. 25.

(36) 3. ON-LINE MEASUREMENTS. Real-time monitoring and identification of the characteristics of inter-area oscillations, including damping factors and frequency oscillations, is a prerequisite for applying corrective measures for system stabilisation in large power systems. A wide area measurement (WAM) approach based on the use of Synchronised Measurement Technology (SMT) leads to more efficient damping of inter-area oscillations as well as two main functions: the first is to collect, monitor, manage, and maintain the real-time synchronised phasor data and real-time switching information uploads from the PMU measurement substations; the second is to diagnose wide area faults, judge protection logic, and issue control orders (such as tripping or blocking) to the control substations[46].. In the last two decades, a number of solutions for online monitoring and identification of power system oscillation modes were presented in scientific literature. After detecting inter-area oscillations, damping and frequency components are commonly determined by applying methods based techniques, or approaches based on different parameter estimation methods.. Phasor estimation is an important issue that has been in continuous development and some important advances have been done concerning measuring power oscillations [47].. In this document are proposed and developed three real-time measurements bearing in mind the implementation of control actions. These measurement algorithms are presented in detail below.. 26.

(37) 3.1 Damping Algorithm Proposed. Taking into account that this approach looks for a maximum damping in the tie-lines, an algorithm is developed to measure the damping factor of any signal, not only a power signal but supposing that the signal is already filtered, as it can be seen in Fig.10.. Fig.10 Damping Algorithm. In order to test the algorithm it is simulated a typical signal in Simulink® with the next characteristic equation (1), knowing the constant parameters. (1). where, 27.

(38) k= amplitude, w= angular frequency,. This algorithm is embedded in Simulink blocks as it is shown in Fig.11.where it is added a zero block crossing detector of the derivative signal knowing the signal peaks and a the exact time when it happens.In addition, some delays are added as well, in order to give some initial conditions to the whole variables in the simulation.. This algorithm is able to measure the damping factor and the relative amplitude (overshoot) however the last is not applied in this document.. Embedded MATLAB Function. Scope1 Clock 1 Signal. du/dt Derivative. Zero Cnt Crossing Zero Crossing. 0. t ov er. maxmin. damp. senal. start_out. start promdatos. promdatos_out t1_out. t1. t2_out. t2. b_out. b. amp1_out. amp1. amp2_out. amp2 delta1. 1 Damping. ndatos_out. ndatos. ov. Overshoot 2. ov _out. dampingdelta1_out dt1_out. dt1. amp3_out. amp3. amp4_out. amp4. t3_out. t3. t4_out. t4. delta2_out. delta2. c_out. c. f _out. f. dt2_out. dt2. h_out. h. amort_out. amort. Out1. In1. Out2. In2. Out3. In3. Out4. In4. Out5. In5. Out6. In6. Out7. In7. Out8. In8. Out9. In9. Out10. In10. Out11. In11. Out12. In12. Out13. In13. Out14. In14. Out15. In15. Out16. In16. Out17. In17. Out18. In18. Out19. In19. Out20. In20. Out21. In21. Subsystem. Fig.11 Damping Algorithm Embedded 28.

(39) The final simulink block implemented is presented in Fig.12 where there is an input which is the signal and two outputs: the damping factor and the overshoot.. if { } Action Port Out1 1 2. Damping. 1. Signal. In1. Ov ershoot. Out2. Subsystem1. Fig.12 Damping and Relative Amplitude Measurement Block. Fig.13 shows the signal introduced and the damping factor obtained which corresponds with the data assigned. 14 12 10. Signal. 8 6 4 2 0 -2 -4 0. 1. 2. 3. 4. 5 Time(s). 6. 7. 1. 2. 3. 4. 5 Time(s). 6. 7. 8. 9. 10. 0.1 0. Damping. -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 0. 8. 9. 10. Fig.13 Damping Measurement 29.

(40) 3.2 Relative Amplitude and Frequency Measurements. During the search of real-time measurements, other algorithms are developed in order to study the most suitable measure keeping in mind the controller action and objectives.. As it is mentioned and shown above, it is developed an algorithm able to measure. the. relative. amplitude,. moreover,. another. measurement. is. implemented and tested in simulink.. Taking into account the necessity to measure the frequency of a signal, especially the low frequencies it is designed another embedded algorithm and presented in simulink blocks (Fig.14).. Again this algorithm, takes advantage of the signal derivation and the zero crossing to detect the peaks. The clock input plays a key role as well, giving the time footprint in real time.. Display1 Embedded MATLAB Function. Clock 0 1 Signals. du/dt Derivative. 1. f. t. f. Zero Cnt Crossing. dato t1. Zero Crossing. t1s. conteo t2s. t2 t3. Scope1. t3s. Scope. 1/z Unit Delay 2*pi 1/z. Constant. Product. Display2. Unit Delay1 1/z Unit Delay2 Scope2. Fig.14 Frequency Measurement Simulink Diagram 30.

(41) The implemented block in simulink has one input (the signal) and one output (the frequency) (Fig.15).. if { } Action Port. 1. Signals. In1. 1. f. Out1. Subsystem. Fig.15 Frequency Measurement Block. Even though the algorithm provides a correct frequency measurement; in general terms it does not give enough information to do a control action because the frequencies associated to the electromechanical modes are too similar and in a similar range.. 3.3 Signal Dynamic Deviation. As it is shown below, another measurement is going to be included to the correspondent control action that measures the signal deviation, and in this case the damping factor deviation (Fig.16).. To achieve a measurement deviation it is built a simple block diagram to carry out this achievement as a discrete function expressed as follows: (2). 1. 1. In1. Out1 1/z Deviation. Fig.16 Signal Deviation Implementation 31.

(42) 3.4 Algorithms Tests. All the algorithms developed are tested in the power system implemented in the PMU locations mentioned in Section II. The simulation is run including the selected PSS. The next figures are organised following aspecialorder, in the first row is presented the main measurement (damping, relative amplitude or frequency), in the second row is shown their deviation and finally, the original signal is presented.. 3.4.1. Damping Factor Measurement. The damping algorithm recognises the negative damping as the increasing of signal, for that reason appears different steps ofmeasurement. In other cases even the signal seems to be in steady state the measurer indicates the minimal changes in the damping and continuous changing as it can be seen in Fig.17 and. Damping Factor. Fig.18.. 1. Electrical Power. Damping Factor Deviation. 0 -1 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 2 0 -2 0. 500. 0 0. Fig.17 Damping Measurement between areas 1 and 2 32.

(43) Electrical Power Damping Factor Damping Factor Deviation. 1 0 -1 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 2 0 -2 0. 600 400 200 0. Fig.18 Damping Measurement between areas 1 and 3. 3.4.2. Frequency Measurement. Due to variations mentioned above, there are a lot of high frequency components which the algorithm measure. It is necessary to remember that the frequency measurements related with the electromechanical oscillations are in the range of 0.1 to 2 Hz which are not clear because of the higher values of frequency. This effect is shown Fig.19 and Fig.20.. Electrical Power Frequency Deviation. Frequency. 100 50 0 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 100 0. -100 0 500. 0 0. Fig.19 Frequency Measurement between areas 1 and 2 33.

(44) Electrical Power Frequency Deviation. Frequency. 100 50 0 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 100 0. -100 0 600 400 200 0. Fig.20 Frequency Measurement between areas 1 and 3. 3.4.3. Relative Amplitude Measurement. Finally, the relative amplitude measurement shows an adequate response even. Relavite Amplitude. 400 200 0 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 500 0 -500 0 Electrical Power. Relavite Amplitude Deviation. though the oscillations presented in the tie-lines.. 500. 0 0. Fig.21 Relative Amplitude Measurement between areas 1 and 2. 34.

(45) Relavite Amplitude Deviation Relavite Amplitude Electrical Power. 1000 500 0 0. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 5. 10. 15. 20. 25 Time(s). 30. 35. 40. 45. 50. 500 0. -500 0 600 400 200 0. Fig.22 Relative Amplitude Measurement between areas 1 and 3. 35.

(46) 4. MULTI-AGENT SYSTEM DESIGN. A Multi-Agent System (MAS) is a system that consists of several coordinating and computing entities called "agents". There are many definitions for an agent. The agents may be software agents, such as computer programs or they may be people like us. An agent might be working alone in an environment or it may communicate, coordinate and share with other agents to achieve its assigned goals[48].. The. hierarchical. control. proposal. based. on. MAS,. use. a. mixed. centralized/distributed planning coordination scheme due to each agent has its inner actions and besides, there is a central planner which conceives the organization actions of the local agents [49]. As it can be seen in Fig.23, the identified processes are associated to the local PSS which are governed by their local supervisors and the upper layer commands the local controllers.. Fig.23 Hierarchical Controller Concept 36.

(47) 4.1 Application Design. In this step is defined the design process as refinement responsibilities identified. Refinement is performed by mapping responsibilities to generalised problems and choosing the most appropriate solution, refinement process involves two steps:. 1. Knowledge Modelling, and 2. Task Roles. 4.1.1. Knowledge Modelling. Local Measurement. FL-PSSn. Hierarchical Takagi-Sugeno Fuzzy Logic (HTSFL) PSS. Remote Measurement. Vstab. AVRn. Gn. AVRi. Gi. Local Measurement. FL-PSSi. Vstab. Coeffient weights. Power System Network Fig.24 Proposed Controller Scheme. The first stage in the application design process is the knowledge modelling. This knowledge uses the agent roles defined by fuzzy control rules. 37.

(48) The proposed structure of control is based on a centralised TSO which receives the Remote Feedback Signals (RMS) from a PMU located in the power network and sends back some command signals to the correspondent local controller in charge of the generators. In this case, the hierarchical control is based on Takagi Sugeno Fuzzy control. Fig.24 depicts the proposed hierarchical controller scheme.. 4.1.1.1. Local Control Model. The fuzzy design is based on a previous design presented in detail in [50][51], where it is explained and justified the membership functions, the fuzzy inference and defuzzification method, so it is summarised and validated now in order to test it with the hierarchical controller exposed below.. Input variables are the speed deviation and active power deviation. The meanings of abbreviations are BN=Big Negative, MN=Medium Negative, LN=Low Negative, Z=Zero, LP=Low Positive, MP=Medium Positive and BP=Big Positive and all of them are trapezoidal functions. Fig.25shows the input membership functions. These inputs have the same functions because are dependant as it is expressed in (3):. (3). 38.

(49) BN. 1. LN LP Zero MP. MN. BP. Degree of membership. 0.8. 0.6. 0.4. 0.2. 0 -5. -4. -3. -2. -1. 0 dw. 1. 2. 3. 4. 5. Fig.25 Speed deviation and Power Active deviation Input Memberships functions representation. The output membership function which is the AVR input is depicted in Fig.26.. BN. MN. LN. BZ LZ. 1. LP. MP. BP. Degree of membership. 0.8. 0.6. 0.4. 0.2. 0 -1. -0.8. -0.6. -0.4. -0.2. 0 Vstab. 0.2. 0.4. 0.6. 0.8. 1. Fig.26 Output Membership Functions. 39.

(50) Fuzzy rules describe the controller output based on the speed deviation (Δw), and active power deviation (ΔPa) inputs; there are (7x7) 49 rules according to the ranges of the variables of the multimachine system [50]. The decision table is shown in Table IV. Table.IV Decision Rules ∆w/∆Pa BP. MP LP. BN. BZ. MN. LP. LN. MN BN. LN. MN MN BN. MN BN. BZ. LN. MN MN BN. LN. MP LP. BZ. LN. LN. MN BN. Z. BP. MP LP. LZ. LN. MN BN. LP. BP. MP LP. LP. BZ. LN. MN. MP. BP. BP. MP. MP. LP. BZ. LN. BP. BP. BP. BP. MP. MP. LP. BZ. 4.1.1.2. Z. BN. SupervisorControl Model. Keeping in mind that the idea of the hierarchical controller is to obtain the maximum damping as it is possible and taking the damping algorithm measurement presented above, it is designed a Takagi Sugeno approach which commands the local PSS signals (Fig. 27).. The fuzzy inference system uses as antecedent the selected measurement of the damping in the tie-lines and the damping deviation in order to get qualitative information of the signal and to give more robustness to the controller. In addition, dynamic pre-filters are used to obtain the damping deviation. 40.

(51) The linguistic labels associated to the damping are similar to the membership functions of the local controllers, however for the damping deviation there is a high variation with the next fuzzy subsets: BNC=Big Negative Change, LNC=Low Negative Change, LPC=Low Positive Change, BPC= Big Positive Change. The membership functions are presented in Fig.29 and Fig.29.. f(u). TSdpc. Damping Factor (5). PSS1 (2). (sugeno). 20 rules. f(u). Damping Factor Deviation (4) PSS2 (2). System TS p : 2 inputs, 2 outputs, 20 rules Fig. 27TakagiSugeno Control Proposed d c. BN. LN. Z. LP. BP. 1. Degree of membership. 0.8. 0.6. 0.4. 0.2. 0 -1. -0.8. -0.6. -0.4. -0.2. 0 Dp. 0.2. 0.4. 0.6. 0.8. 1. Fig.28 Input Membership Functions of the Hierarchical Controller. 41.

(52) BNC. LNC. LPC. BPC. 1. Degree of membership. 0.8. 0.6. 0.4. 0.2. 0 -1. -0.8. -0.6. -0.4. -0.2. 0 DpDev. 0.2. 0.4. 0.6. 0.8. 1. Fig.29 Input Membership Functions of the Hierarchical Controller(Deviation). The control surface associated is shown in Fig.30.. 1. PSS1. 0.8 0.6 0.4 0.2 0 1. -1 0.5. -0.5 0. 0 -0.5. 0.5 -1. 1 Damping Factor Deviation. Damping Factor. Fig.30 Surface Control Associated. The output membership functions are adjusted finding the minimum error in steady state and the minimum oscillation in transient state by trial and error method using the toolbox FIS of Matlab®.. The rule base that represents the knowledge obtained from the behaviour of the system is summarised in Table.V. The rule base of the controller is proposed 42.

(53) after getting a previous knowledge about the dynamic and steady state behaviour of the system. The output of the fuzzy controller is to give weighted factors with different scales according to the damping measurements which indicates what local fuzzy controller should be adjusted, and based on that a damping factor near to the unit is the best option and it happens when the oscillations are mitigated. In the case that the damping measurement gives a low factor, the hierarchical controller orders the most weighted action to the local controllers.. A negative damping factor can appear, and it is presented when there are low oscillations and change to high oscillations, which is the case of a fault and the action is contemplated with the same high weight. In that order, the linguistic labels are only positive in three scales: Small (S), Medium (M) and Large (L).. Table.V Decision Rules. 4.1.2. ∆dp/dp BN. LN. Z. BNC. PL1 PL2. PM1 PS2. PL1 PL2 PL1 PL2. LNC. PL1 PM2 PM1 PM2 PL1 PL2 PM1 PM2. LPC. PM1 PS2 PL1 PM2. PL1 PL2 PM1 PL2. BPC. PM1 PS2 PS1 PS2. PL1 PL2 PL1 PM2. LP. Tasks Roles. Designing a multi-agent system for any system is based on certain rules, and requires classification of component agents, their characteristics, extent of influence and limitations. The idea behind any multi-agent system is to break down a complex problem that being handled by a single entity into smaller simpler problems handled by several entities. Based on the goals a multi-agent 43.

(54) system designed for WAMCS should be able to accomplish the following three tasks. 4.1.2.1. User Access Task. To provide user gateway that make features of the Smart grid accessible to humans. It includes responsibility of providing users with real-time information of entities residing in the system, by displaying active power measurements and the remote/local feedback signals and their status.. 4.1.2.2. Control task. This task includes responsibilities of monitoring the active power in selected buses of the main grid in ordert to detect their fluctuations and apply the correspondant control actions.. 4.1.2.3. Measurement Task. This task has the responsibility of monitoring active power buses, speed rotors or frequency and controlling their (on-off) status.. 4.1.2.4. Coordination Task. While task distribution is one of the important rules played by a multi-agent system, there is no-need to distribute the activities as widely as possible. The application of multi-agent systems can be more efficient if some agent's activities are centralised. That provides efficient by: 44.

(55) - Reducing the number of messages exchanged among agents and - Simplifyng the overall complexity of multi-agent system implementation. The central control has a typical hierarchical fuzzy structure based on the observation system and derived from simulations to form knowledge base consisting of if – then rules[52]. One advantage of using fuzzy rules is the linguistic variables applied which is a straightforward way to describe a behaviour system. A fuzzy rule base is expressed as:. if<fuzzy proposition>, then<fuzzy proposition>. (4). The propositions are combinations of conditional, unconditional and assigned statements, the relationship of these sets of rules, the nested loops and the priority composition form the central control.. The fuzzy control levels specify three actions listed as follows:. 1.. The regulation of steady – state power error: an important aspect proposed, is the requirement in the coordination of the voltage stabilisation references changes when is required and involves the primary control level explained above. Based on this desired behaviour, the corresponding fuzzy rule is: -. if the speed deviation and active power deviation changed in a. PSSn, then change the voltage stabilisation and apply the fuzzy local control rules. 45.

(56) 2.. The secondary control target has to coordinate the whole PSS involved at one set point or to adjust different set points.The overall power and the comparison with the reference set point are measured and then is assigned a weight according to the error. difference. measurements in each PSS, determining how close are (over or down) to the global speed set point and finally apply the local control. In terms of fuzzy logic this rule can be written as:. -. if the power measurement p in a unit tie-line is not damped then assign a weight wd to increase or decrease the local settings.. 4.1.2.5. Bulletin Board Tasks. This is a special task which represents a dynamic contact point through which all entities share and retrieve information. This requires a decision support system that would enable look-ahead optimal settings during both emergency (when there is fault) and non-emergency conditions.. 4.1.3. Agent Specification. In this step, specifications regarding agents belonging to the multi-agent system are defined.. 4.1.3.1. Control Agent. 46.

(57) Control agent receives the power measurement from the measurement agent and applies the most optimal control action possible and is responsible to redistribute the local PSS actions.. 4.1.3.2. User Agent. Provides the interface, monitor the power measurements and acctions. User agent can re-define the control actions.. 4.1.3.3. Monitor Agent. Gives the information to all the other agents and. 4.2 Constraints Constraints mean restricting the values of concepts to subsets of legal values. No constraints need to be ascribed to concepts for the applications. Default values for all concepts have been identified. The application does not require any types to be related to the concepts.. 47.

(58) 5. MULTIAGENT SYSTEM APPLICATION AND STUDY CASE. In this thesis, the smart transmission grid is developed in Matlab/Simulink environment to demonstrate the proposed multi-agent system functionality. The implementation of multi-agent systems is of core importance.. This chapter addresses the study case conducted to analyse the multi-agent systems functionality of reducing the damping in the tie-lines of the power system. studied. applying. on-line. measurements. under. a. wide. area. measurement and control framework (Fig.31).. Fig.31 Multi – Agent Framework. The system is implemented in Simpower® and simulated without the PSS signal in any machine, a three phase fault point as a disturbance is inserted in bus 6, which is in the middle of the areas 1 and 2, with duration of five cycles and then it is cleared as it is shown inFig.32 .. 48.

(59) The case studies conducted for the demonstration of multi-agent system functionalities. The results and description of the tests are presented and discussed at next.. B1. B2. A. aA. A. a. A. a. aA. A. B. bB. B. b. B. b. bB. B. C. cC. C. c. C. c. cC. C. Line 1a (110 km). A B. Line 1b (110 km). E. Brk2. D. C. Brk1. D. E. Fault. F. F. Area 1. Area 2 Line 2-3 (110 km)1. Line 2 (220 km). B4 aA. A B. bB. C. cC. B3 aA. D E. bB. F. cC. Control System Line 2-3 (110 km). Area 3. ?. Fig.32Simpower System Implementation. Double click here fo. Machines2. d_theta w. 5.1 Transient Stability d_theta v s M6 (deg) w (pu). d_theta1 Pe. Machines1. yellow=M1, magenta=M2, cyan=M3, red=M4 Machines Green=M5 Blue=M6. STOP. Vps P_B1->B2. System Data. Pos. Seq. V_B1 & V_B2 (pu) Activ e Power f rom B1 to B2 (MW). System. Fig.33 shows the time response of electrical power ofSelect each machine and can be a specific PSS model. Stop. Machine Signals. Stop simulation if loss of synchronism. Phasors. Electrical Power Yellow=M1 mag=M2 cyan=M3 red=M4 Green=M5 Blue=M6. Show Bode plot of PSS. by typing:. PSS) seen clearly that the system presents a critical 01 (No stability and inter-area (MB-PSS). oscillations.. 2 (Delta w PSS from Kundur) 3 (Delta Pa PSS) 2 PSS model. PSSmodel. Show results: Step on vref of M1. Show results: 3-phase fault. Goto. 49.

(60) 1.2 G1 G2 G3 G4 G5 G6. Electrical Power (MW). 1. 0.8. 0.6. 0.4. 0.2. 0 20. 25. 30. 35. 40. 45. Time(s). Fig.33 Electrical Power Time Response of Generators (Local). Fig.34 shows the oscillations in the tie-line which interconnects area 1 and 2.. 280. Electrical Power (MW). 260 240 220 200 180 160 140 20. 25. 30. 35 Time(s). 40. 45. 50. Fig.34 Electrical Power Time Response of a Tie-line (Remote). Now the system is simulated with only two local PSS and with the hierarchical control proposed and it is observed the power flow in the tie-lines which interconnect the area 1 with 2 and area 2 with 3 respectively. Even the oscillations are mitigated with the local PSS (in green and blue), the hierarchical 50.

(61) controller presented achieves a minimal oscillation after the first swing in both cases (in blue and red), and the HPSS is more effective for inter-area damping. 400. Electrical Power (MW). 350. 300. 250. 200. 150. 100 20. 25. 30. 35 Time(s). 40. 45. 50. Fig.35 Electrical Time Response in the Tie-lines with LC (Local Controllers) and HC (hierarchical controllers). 550. Electrical Power (MW). 500 450 400 350 300 250 200 150 20. 25. 30. 35 Time(s). 40. 45. Fig.36 Electrical Time Response in the Tie-lines with LC (Local Controllers) and HC (hierarchical controllers). The selection of local fuzzy controllers instead of conventional controllers is based on its adaptability. A test is done while the hierarchical controllers are used with both types of local PSS. 51.

(62) Fig.37 shows the power electrical measurements in generators when is used the hierarchical controller and Fig.38 shows the same signals when is used the fuzzy local controllers and the hierarchical control at the same time. It can be seen that, when are used the fuzzy local controllers case those fluctuations are reduced. G1 G2 G3 G4 G5 G6. 0.9. Electrical Power (MW). 0.8 0.7 0.6 0.5 0.4 0.3 0.2. 25. 30. 35 Time(s). 40. 45. 50. Fig.37 Electrical Power Time Response of Generators with conventional PSS as long as it is applied the Hierarchical Control. G1 G2 G2 G4 G5 G6. 0.9. Electrical Power(MW). 0.8 0.7 0.6 0.5 0.4 0.3 20. 25. 30. 35 Time(s). 40. 45. Fig.38 Electrical Power Time response of Generators with the two fuzzy controllers. 52.

(63) The zoom in shown in Fig.39 Power Response Zoom in with Fuzzy Local Control demonstrates the fuzzy controller performance reducing the power fluctuations. 0.8. G1 G2 G2 G4 G5 G6. 0.75. Electrical Power(W). 0.7 0.65 0.6 0.55 0.5 0.45 0.4 30. 31. 32. 33. 34. 35 Time(s). 36. 37. 38. 39. 40. Fig.39 Power Response Zoom in with Fuzzy Local Control. 5.2 Results and Discussion. In order to evaluate the dynamic response of the controller proposed, the damping factor (ζ) measured and the settling time (st), are compared with the local controller. Table IV the advantages of the hierarchical controller.. Table.VI Controller Comparison Local Controllers Hierarchical Controller ζ (%). 0.1. 0.65. st (s). 15. 8.33. Once the measurement agent detects the fault at t= 25s, the control agent informs the user agent and the control agent, both of which exchange 53.

(64) information and determine the optimal weight and the amount of power to stabiliser the power system. The control coordination agent sends a control signal to the local control agent to change the settings.. The use of the hierarchical control achieves to maximise the damping factor in a minimum time compared with the use of the local controllers only.. 54.

(65) 6. CONCLUSIONS. The eigenvalue analysis is a traditional method for offline analysis of dynamic properties of power systems. It is based on the assumed system model and the classical methods of linear systems control theory. With the help of eigenvalues, eigenvectors, and participation factors, the system characteristics can be predicted. However, this hardly meets the severe requirements for efficient monitoring of dynamically changed power systems possessing a high level of uncertainty. The system topology and the states are dynamically changed. The system parameters are not constant.. The hierarchical controller methodology based on Takagi Sugeno approach involving on-line measurements demonstrates a suitable time response obtaining a damping factor maximised.. The wide area on-line measurements proposed gives an advantage over the local measurements in order to reduce the LOF in the main tie-lines involved and can be easily embedded in software/hardware systems.. The adaptability of the fuzzy logic controllers in Smart Transmission Grids provides to be a good solution for the next generation of control systems involved.. 55.

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