The power-voltage (P-V) characteristic curve of solar photovoltaic (PV) systems operating in partial shading conditions (PSC) is nonlinear and has multiple local maximum peak power (LMPP) points, rendering many of the maximum power point tracking (MPPT) algorithms ineffective at locating global maximum peak power (GMPP) points. This work proposes a novel slime mould algorithm- (SMA-) based MPPT controller to utilise maximum peak power (MPP) from solar PV systems during uniform irradiance conditions (UC) and nonuniform irradiance conditions (NUC). On the basis of the MPP they tracked, tracking time, and power efficiency, MPPT controller performance is assessed through MATLAB simulations and implemented experimentally with dSPACE MicroLabBox under various irradiance conditions. The effective performance of the proposed controller is validated and demonstrated in comparison to existing popular MPPT controllers.
The Solar PhotoVoltaic (SPV) systems are the trending and commercially reputable power source abundantly served by the nature to the mankind. Partial Shading Conditions (PSC) are one of the critical concepts in the SPV maximum power extraction. PSC’s are nonlinear and fuzzy in its attributes, as it is unpredictable. Hence, it has numerous Local Maximum Peak Power (LMPP) points. Although, a wide spread of Maximum Power Point Tracking (MPPT) algorithms are doing justice in locating the peak power points and stabilize the system, they are inadequate to locate the LMPP’s and the Global Maximum Peak Power (GMPP) point. This paper proposes a discrete time-based Slime Mould Optimization (SMO), providing an effective support to the buck converter based MPPT controller for SPV systems. The analysis and testament of buck converter in discrete domain alleviates the optimization in discrete samples, which accelerates the computation speed in locating the LMPP and GMPP. The proposed methodology is validated from the predominant parametric results like tracking time, power efficiency and the stability of the system under various PSC’s. The experimental implementations are performed in MATLAB simulations and experimented with dSPACE-MicroLabBox.
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