CAPITULO 4 PROPUESTAS PARA BACHILLERATO
4.10 Actividad10 El Jardín
der PSC
This section will address techniques which have been specifically designed to handle GMPPT. The techniques considered include line search methods such as Dividing Rectangles (DIRECT) and Fibonacci search, artificial intelligence approaches and Chaos Search.
4.5.1
Line Search
Several line search MPPT methods have been proposed to deal with PSC including DIRECT [20], and Fibonacci Search [21, 198]. Line search algorithms work by restricting and shifting an interval to locate the optimal interval within which the optimum point lines and then converging to this point [21].
The DIRECT method is based on progressively making the searching interval smaller based on the values of samples within the interval and a condition used to determine which interval is the most promising to hold the optimum value [20]. As the P-V characteristics of the PV module can be verified to be a Lipschitz function, the DIRECT method can be used and in most cases will lead to GMPPT.
For the Fibonacci search method, the length of the interval considered is determined based on the numbers in the Fibonacci sequence [21, 198]. Generally, to make this method suitable for PSC, it is necessary to define a condition to detect when PSC has occurred [21] to enable the method to reinitialise.
Line search methods can enable GMPPT under most conditions with relatively simple test conditions. However, in some cases the techniques will converge to a local MPP and the techniques may depend on the use of special conditions to detect when PSC have occurred. Depending on the initial parameters selected, the method may not converge to the GMPP.
4.5.2
Artificial Intelligence
The main artificial intelligence approach explored in the literature for dealing with PSC is Particle Swarm Optimisation (PSO) [19, 151, 199–202]. PSO approaches are modelled on the basis of birds flocking and fish schooling and use a collection of particles to collectively solve a problem. Each particle’s position is updated based on its best position and the best overall position enabling the particles to converge to a global solution. Typically, PSO is applied to time-invariant problems, so mechanisms to detect a change in environmental conditions and reinitialise the global tracking are essential for PV systems [201]. PSO search requires several parameters to be defined including the momentum factor, speed determining constants and the number of agents [19]. In [202], the randomness in the method is reduced by removing the random parameters leading to fewer parameters and improved performance.
The PSO technique is combined with a Gravitational Search Algorithm to min- imise oscillations in steady-state for uniform operating conditions where a change in irradiance occurs [203]. This method exhibits superior performance in tracking to the MPP, with the absence of oscillations in steady-state, when compared to each of the algorithms applied independently. While the change in irradiance is considered in the paper and the difference between local and global maxima identified, there is little information to support the PSC simulated for this system.
A technique based on a colony of flashing fireflies has been proposed for GMPPT which exhibits superior performance to PSO [204]. The firefly algorithm is inspired by the behaviour of flashing fireflies when attracting a mate. The algorithm is based on the idea that the fireflies will move towards the brightest firefly where brightness corresponds to the power on the P-V curve corresponding
to that operating point. Performance of the algorithm shows improved capability to track to the GMPP and the PSO technique is simply a special category of the firefly algorithm.
PSO generally has good performance in tracking the GMPP under PSC. However, in some cases if the initial particle positions are not selected ap- propriately, the method may converge to a local solution [19]. Additionally, the performance is strongly related to the values selected for the system constants.
4.5.3
Chaos Search
A chaotic search approach is defined in [205], which uses dual-carriers to improve the performance. In the search process a logistic mapping is selected and an additional function is used to map the chaos generators. The chaotic search can operate well under PSC and unpredictable environmental conditions. As chaotic behaviour appears random, but can be shown to be deterministic, a chaotic search will have superior performance to a completely random search which makes this technique appropriate for improving MPPT performance under PSC. The implementation requires no extra circuit components, however is more complex than conventional MPPT techniques.
The chaotic search process is combined with PSO for flexible PV modules to develop the Hybrid Chaotic Particle Swarm Optimisation (HCPSO) tech- nique [206]. Flexible PV modules can be applied to curved surfaces which results in the appearance of local MPPs. The HCPSO technique is proposed as an efficient way to track to the GMPP for cells where geometric placement is a significant factor for modifying the P-V characteristics. When the particles (only two used in this implementation) are stuck in a local MPP, the chaotic search will reinitialise their positions. The HCPSO technique is applied every few minutes and then a conventional MPPT method is used to remain at the GMPPT before the next global tracking cycle is initiated.