Hybrid TSA‐RBFNN based approach for MPPT of the solar PV panel under the effects of tilt angles variations and environmental effects

Abstract

This paper proposes a hybrid approach to improve the performance of photovoltaic (PV) system and track the maximum system power. The proposed hybrid approach is the combination of Tunicate Swarm algorithm (TSA) and Radial Basis Function Neural Network (RBFNN), hence it is called TSA-RBFNN. The main contribution of this paper is to "achieve the best output from solar system by tilt angles variations and environmental effects, like dust accumulation, water drops, partial shading, and maximum power point tracking (MPPT) of the solar PV panel. Here, tilt angle and orientation angles are important factor for obtaining the maximal power of the photovoltaic system with consequently the power fed to load in the PV system. The voltage, current, and PV system power are used to analyze the effect of any particle size and any weight of coal for the performance of PV modules. TSA is used to measure the PV module voltage, current, and power, then it generates a possible dataset in the offline way. The dataset has electrical parameters that are used to create a model by using RBFNN in the online way. In addition, the determination of cleaning frequency is also developed for dirty PV modules depending upon the dust deposition velocity, then the correlation among deposited dust density including power performance of photovoltaic module. The proposed method is activated in MATLAB/Simulink site under four different test cases and the efficiency is compared with other existing methods. Furthermore, optimum solutions for proposed technique, the current, voltage, power are also analyzed. In 100 iterations, the first order statistic evaluation for all the cases is obtained using proposed and existing techniques such as ANN, GBDT, SSA, and SSA-GBDT technique. In addition, the dependability, sensitivity, and accuracy of the proposed technique are also obtained. Computation time using various number of trails of proposed and existing techniques is analyzed for 100, 150, 200, 250, and 500 trails. The proposed technique achieves the trail number of 100, 150, 200, 250, and 500 trails is 48.1740 seconds, 51.2133 seconds, 71.0483 seconds, 60.00126 seconds, and 57.80132 seconds.

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

“…The module/array undergoes a faster and more dynamic solar insolation shift during cloudy days, which has a direct impact on its performance [ 37 ]. Under PSCs, unshaded modules receive a certain amount of solar irradiation, while shaded modules receive less than that amount [ 38 ]. This imbalance in irradiance causes a hotspot in the module that receives less irradiance [ 39 ].…”

Wind power output prediction: a comparative study of extreme learning machine

“…Considering extreme load growth, Kommula and Kota (2022) focused primarily on proposing a new method of TSA to optimize the integration of electrical distribution networks. Ganti et al (2021) improved TSA to control the speed and torque errors of brushless DC motors. Gharehchopogh (2022) used TSA to optimize the radial basis function neural network to improve maximum system power tracking in a photovoltaic system.…”

Formulation and Data-Driven Optimization for Maximizing the Photovoltaic Power with Tilt Angle Adjustment

“…This study will use this value as an input for the model during the simulation. Meanwhile, hourly extraterrestrial irradiance on the horizontal surface I 0 can be calculated through Equation (8) In this equation, G sc is the solar constant, which is 1367 W/m 2 , with up to 1% error. φ is the latitude and is related to the simulation location, and ω is the hour angle, the output of the solar position model.…”

“…Recent studies have shown that tilt angle optimization can significantly increase the solar irradiance incident on panels, which subsequently provides greater solar to electricity conversion [6][7][8]. Raina et al [6] found that a bifacial photovoltaic module installed at an optimum orientation could collect up to 84.76% more irradiance.…”