# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 2 Evolutionary-Based MPPT Algorithm for PV System Power Extraction  
### MPPTs and PV System Issues Under Dynamic Partial Shading  
PV generators are highly dependent on environmental conditions such as solar irradiance, temperature, and particular shading conditions. The natural variation of such environmental parameters combined with the nonlinear characteristics of the solar cells make power extraction optimization a critical task. To obtain the maximum conversion efficiency, the PV generator must operate at the higher point of its power-voltage curve, also called the Maximum Power Point (MPP). This control is known as the Maximum Power Point Tracking, and it is the typical operating condition of PV generators both in grid-connected and off-grid PV systems. Only in very special cases, the PV generator is regulated at a lower power than the MPP with the algorithm of Regulated Power Point Tracking (RPPT), as in the case of islanded hybrid micro-grids where the power production exceeds the load demand and storage systems are not available [4]. Various MPPT techniques have been developed over the last years that follow both direct and indirect approaches.  
When particular shading conditions occur on a PV generator, one or more bypass diodes can be forward biased to prevent localized power dissipation at the shaded cells. The bypass diodes forward or reverse bias state under partial shading depends on the PV generator output voltage, resulting in modified P-V curves with multiple local maxima. Thus, the MPPT algorithm running in the control unit must be adapted or modified when designing an algorithm suitable to operate in the case of partial shading and rapidly changing environmental conditions.  
Traditional Perturb and Observe methods (P&Os) simply compare the actual PV generator voltage and output power with respect to the previous ones, adapting the operating points accordingly to reach the MPP condition. However, when rapid environmental changes occur, even the most sophisticated variant of P&Os may lose efficiency, since these hill climbing techniques may not always reach the GMPP and the operating point may get stuck in a local maxima.Thus, it is possible to conceive a family of PSO-based MPPT algorithms, with a variable population size, in order to better find the proper MPP. On the other hand, by increasing the number of particles, the computational time increases and the conversion efficiency decreases. Thus, in the context of PSO-based MPPT algorithms it is better to keep the population size as small as possible. Moreover, in this particular application characterized by dynamic partial shading, a time-varying fitness function has to be considered. Among all other evolutionary techniques, Particle Swarm Optimization is characterized by easy implementation. In this particular context, the exploitation feature of this algorithm can guarantee to reach the MPP without oscillations. Knowing a priori the number of potential local maxima related to the specific system and number of diodes, it is possible to implement a simple PSO-based algorithm with a relatively small number of agents, from three to five in this case, in order to explore the one-dimensional solution space. The next subsection describes in detail the implementation of the proposed PSO-based MPPT algorithm.