Identificación y caracterización del T-DNA de camote en Ipomoea batatas (L.) Lam (6x).

Fig. 3.12 presents the simulation result of voltage swell. The typical charac- teristic of voltage swell is that the amplitude of the voltage increases temporari- ly for a duration. Similarly, the magnitude of the signal envelope increases for a same period when voltage swell occurs. Furthermore, the start and end points of the swell, which are presented as two impulses in the extracted signal, are detected by MMG. With these two features, voltage swell is clearly identified.

0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 −0.5 0 0.5 1 Time (s) Voltage (kV) Input signal Result of MMG Envelope

Figure 3.11: The simulation result for a momentary interruption.

0.48 0.5 0.52 0.54 0.56 −2 −1 0 1 2 Time (s) Voltage (kV) Input signal Result of MMG Envelope

3.3.4

Oscillatory Transient

In Fig. 3.13, the simulation result of damped oscillatory transient is pre- sented. The damped oscillatory transient is easy to identify as the result of MMG changes synchronously with the transient. Meanwhile, the amplitude of the signal envelope increases during the period of the disturbance as well. With the synchronous fluctuation extracted signal and ascending signal envelope, the damped oscillatory transient is successfully recognised.

0.48 0.5 0.52 0.54 0.56 −1 −0.5 0 0.5 1 Time (s) Voltage (kV) Input signal Result of MMG Envelope

Figure 3.13: The simulation result for damped oscillatory transients.

Based on the features analysed above, the power quality disturbances can be classified according to the principles shown in Table 3.3. The feature ex-

Table 3.3: Classification methods of power quality disturbances

Disturbances MMG Envelope

Voltage Sag value changed constant

Momentary Interruption value changed at start and end zero Voltage Swell value changed at start and end increased Oscillatory Transient value changed frequently increased

the closing operator can be applied jointly for power disturbance classification. Furthermore, as mentioned in the previous section, MMG might be ineffective when the disturbance occurs around the zero-crossing points. Therefore, with- out the accurate power disturbance feature extraction, the classification fails. If the classification or the detection of a disturbance fails (extracted feature does not match with the descriptions in the table), this disturbance would be considered not to be accorded with the descriptions of typical power quality phenomena described in IEEE 1159 standard [44].

3.4

Conclusions

In this chapter, a recently developed MT, MMG algorithm, has been im- proved by using trapezoid SEs. MMG is applied to detect the location of several typical power quality disturbances. The extracted features can be used combining with a morphological operator, closing, generating signal envelopes to classify the types of the detected disturbances.

The simulation studies of the extraction on different typical power quali- ty disturbances, containing voltage sag, momentary interruption, voltage swell and damped oscillatory transient, have demonstrated that the proposed ap- proach is able to detect different types of power quality disturbances effectively and successfully in most cases. Moreover, the adoption of the morphological operator, closing, has been applied to obtain a signal envelope of the input signal and this amplitude information can be combined with the extracted sig- nal for accurately classifying the types of the detected disturbances. Although in some severe situations, MMG fails to extract the features of these distur- bances, the performance of the refined MMG in detection of power quality disturbances in noisy environments is still considerable to a large extent com- pared to other techniques. Furthermore, the proposed method is simple and easy to implemented. Through large amount of simulations, in an SNR of 30 dB environment, the proposed approach can reach above 90% accuracy in the identification of typical power quality disturbances.

Detection of Low Frequency

Oscillations

There exist Low frequency oscillations (LFO) in power systems with long distance and large capacity power stations, and severe power quality issues can be caused by weak interconnection, degrading the stability of power systems. With increasing amount of large-scale power systems, some of power system blackouts incidents related to LFO have occurred frequently in the past few years. Hence, in order to improve the dynamic stability, effective and accurate detection of LFO becomes more necessary in large-scale power systems.

This chapter firstly introduces a hybrid method, using an envelope extrac- tion morphological filter (MF) and group search optimiser (GSO) technique, for detecting continuous LFO in power systems. This proposed approach focuses on accurately extracting envelopes of the target signal by applying the MF based on two basic morphological operators, dilation − closing and erosion − opening and estimating the parameters of the extracted envelopes by using GSO. This algorithm is capable of on-line detecting continuous LFO components in severely noisy environments, which are mainly resulted from the imperfections of the generators caused by slight vibrations and impacts in the generators, and it can also provide the parameters for further analy- sis on LFO. However, the hybrid method using the GSO has not be applied

for detecting damped LFO since the computational time consumption increas- es dramatically when applying GSO to calculate the parameters of the damp ones. As a consequence, inspired by the idea of handling LFO directly from a new angle, another pre-designed MF is utilised to directly deal with damped LFO components, which are generally caused by sudden faults occurring in transmission lines or interconnected area in large-scale power systems. In this LFO extraction MF, another two morphological operators, opening − closing and closing − opening, are applied to directly extract the damped LFO com- ponents. In order to analyse these approaches, a generator fault system model and a four-machine two-area power system model are simulated to generate signals containing continuous and damped LFO components separately. Sev- eral simulation tests have been undertaken to assess the efficiency of these proposed methods. The simulation studies demonstrate that both proposed techniques are robust on detecting the presence of continuous and damped LFO respectively in large complex power grids.

4.1

Envelope Extraction Morphological Filter

with GSO for Detecting Continuous LFO

In this section, envelope extraction MF is introduced firstly and the GSO algorithm is then described.