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

Abstract

A PV system’s operation highly depends on weather conditions. In case of varying irradiances or load changes, there is a power mismatch between various modules of the PV array. This power mismatch causes instability in the output of the PV system and deteriorates the overall system efficiency. To overcome instability and lower efficiency problems, and to extract maximum power from the PV system, various maximum power point tracking (MPPT) techniques are employed. The success of these techniques depends on the identification of the actual operating conditions of the system. This article proposes a hybrid maximum power point tracking (MPPT) technique that is capable of efficiently differentiating between uniform irradiance, non-uniform irradiance, and load variations on the PV system. Based on the identified operating conditions, the proposed method uses modified perturb and observe (Modified P&O) to cope with uniform irradiance variations and chimp optimization algorithms (ChOA) for non-uniform conditions to track the oscillation free maximum power-point. The proposed method is implemented and verified using a 4×3 PV array model in MATLAB Simulink software. Different cases of uniformly changing irradiance and non-uniformly changing irradiance are applied to test the performance of the proposed hybrid technique. The load varying conditions are performed by applying a variable load resistor. The authenticity of the proposed hybrid technique is critically evaluated against the well-known and most widely used optimization techniques of modified perturb and observe (Modified P&O), particle swarm optimization (PSO), flower pollination algorithm (FPA), and grey wolf optimization (GWO). The results demonstrate the superiority of the proposed technique in oscillation-free tracking of global maximum power point (GMPP) in a minimum tracking time of 0.4 s and 0.15 s, and steady-state MPPT efficiency of 96.92% and 99.54% under uniform and non-uniform irradiance conditions, respectively.

A novel technique using stretching, repulsion, and chimp optimization algorithm to find global maximum power point considering complex partial shading conditions

“…The GMPP tracked values of voltage, current, and power during PSC‐1, PSC‐2, and PSC‐3 are 768 V, 6.06 A, and 4649 W; 657 V, 5.15 A, and 3390 W; and 680 V, 5.35 A, and 3625 W, respectively. The same scenario has been considered to compare the proposed algorithm results in Figure 7 with the results of GMPP tracked by the falcon optimization algorithm (FOA), 28 enhanced gray wolf optimization algorithm (EGWO), 29 multidimensional cuckoo search algorithm (MDC), 31 and chimp optimization algorithm (ChOA) 46 in Figure 8. It is clear from Figure 8 that all algorithms take more time for tracking GMPP with large initial fluctuations causing more power loss.…”

“…The searching agents, tuning parameters, and their specific goals have been discussed in the literature. 46,47 The main chimp variables and their position update are given in (7) and (8) as follows:…”

“…The steady-state and tracking oscillations are less observed in case of off-grid analysis as compared with the literature. 28,29,46 3. The tracking time efficiency is boosted in on-grid case which proves that SRCA performance is increased with the increased complexity of the system.…”

“…ChOA has almost the same features as that GWO, but ChOA has more searching agents with unique features. The searching agents, tuning parameters, and their specific goals have been discussed in the literature 46,47 . The main chimp variables and their position update are given in (7) and (8) as follows: …”

Sliding Mode Control based MPPT and Output Voltage Regulation of a Two-stage Stand-alone PV System

“…An MPPT technique is proposed in [7], where the InC method is used to improve the convergence speed and the particle swarm optimization (PSO) algorithm is used to reduce ripples in the output power of a PV system. A hybrid technique for MPPT that combines a modified version of P&O and the chimp optimization algorithm (ChOA) is proposed in [8]. The Owl search algorithm is used together with the InC method to reduce the convergence time to the maximum power point and mitigate the inherent limitations of the conventional InC method in [9].…”