A Novel MPPT Technique based on Hybrid Radial Movement Optimization with Teaching Learning Based Optimization for PV system

Because of its pure and plentiful accessibility, solar power is a remarkable resource of energy for the generation of electrical power. The solar photovoltaic mechanism transforms sunlight striking the photovoltaic solar panel or array of photovoltaic panels directly into non-linear DC power. Due to the nonlinear characteristics of solar photovoltaic panels, power must be tracked for their effective usage. When the photovoltaic arrays are shaded, the problem of nonlinearity becomes more pronounced, resulting in large power loss and intensive heating in a few areas of the photovoltaic arrangement. The tracking challenge is made more difficult by the fact that bypass diodes, which are used to completely eradicate the shading effect, generate numerous power peak levels on the power vs. voltage (P-V) curve. Traditional methods for tracing the global peak point are unable to examine the entire P-V curve as they frequently get stuck at the local peak point. Recently, machine learning or optimization algorithms have been used to determine the global peak point. Because these algorithms are random, they search the entire search area, reducing the possibility of being caught in the local maximum value. This article proposes a hybrid of two optimization approaches: radial movement optimization and teaching-learning optimization (HRMOTLBO). The proposed MPPT method was thoroughly investigated and tested in a wide range of photovoltaic partial shading combinations. The recommended HRMOTLBO MPPT approach outperforms and is more reliable than a recent Jaya-based MPPT approach in terms of tracing time and power variation under dynamic and static partial shading conditions. Experimental as well as simulation outcomes demonstrate that the proposed MPPT successfully traces the global peak point in less time and with fewer fluctuations during various partial shading conditions