An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

Abstract

The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.

An effective maximum power point tracking technique for partially shaded photovoltaic system under rapidly changing atmospheric conditions

“…Simulation studies of the APSO‐NM hybrid method are performed under three different shading conditions. The proposed algorithm is compared with the DMJaya, 43 CSA, 16 PSO, 44 APSO, 31 and NM 32 under similar shadings to validate the results. Figures 6–11 depict the simulation results of all the six algorithms under three different shading patterns.…”

“…The metaheuristic approach avoids trapping in local peaks, and the most widely used in the literature is particle swarm optimization (PSO) [13][14][15] to extract the global power under PSC. In Houam et al, 16 the crow search algorithm (CSA) is simulated for tracking maximum power under changing weather conditions. Ant colony optimization is employed to extract global maximum power under PSC 17 with more number of tuning parameters.…”

An Improved Partial Shading Detection Strategy Based on Chimp Optimization Algorithm to Find Global Maximum Power Point of Solar Array System

“…The key advantage is improved efficiency and reduced computational burden. The author in [17] proposed a crow search algorithm (CSA)-based optimization technique for MPPT and compared it with particle swarm optimization (PSO) and P&O. The analysis shows improved performance of CSA as compared to traditional PSO and P&O methods.…”

“…These issues can be resolved by combining the benefits of conventional and metaheuristic methods to design a hybrid method [17]. To avoid unnecessarily triggering between algorithms, it is extremely important to identify the occurrence of partial shading and differentiate it from the uniform change of irradiance [18].…”

Development and experimental validation of novel robust MPPT controller based on bond graph and fuzzy logic for PV system under variable weather conditions

“…With regard to modernized hybrid techniques, they are highly efficient in tracking and reducing the computational burden to a very large extent, and, the recent complicated applications are controlled with these Hybrid techniques due to their fast and utmost precision. [35][36][37] The advancement of power electronics technology and the advent of digital technology in the area of renewables energies have enabled the rapid development of PV applications. 38 The DC-DC converter of SEPIC type has been suggested in our work; this converter has many advantages and very few disadvantages compared to others.…”