Overview

The main focus of this research work is to improve the forecasting accuracy of solar power generation using three novel approaches, Machine learning algorithm (extreme learning machine and optimized extreme learning machine), optimization algorithm (Teaching, learning-based optimization, and Modified Teaching, learning-based optimization), and predicted the meteorological parameters (solar irradiation and atmospheric temperature) utilized for forecasting the solar power generation, which enhances the prediction accuracy. Solar power generation varies due to the stochastic nature of solar irradiation and atmospheric temperature. In addition, the floating nature of solar power generation makes its regulation complex and unpredictable. So, forecasting algorithms are designed and employed to predict the power generation from solar energy sources to minimize the deviation between the actual and predicted values. Initially, the conventional PID controller and Fuzzy PID (FPID) controller-based MPPT algorithm are designed. Then, Sine Cosine Algorithm (SCA), Teaching Learning Based Optimization (TLBO), and Modified Teaching Learning Based Optimization (MTLBO) are used to design optimal PID controller and FPID controller for Solar power generation (SPG). Finally, results obtained in terms of undershoot, overshoot, and settling time of SPG are compared with the traditional P & O technique is demonstrated as the better approach in terms of settling time, undershoot and overshoot of the responses. SPG is an infrequent, complex control strategy dependent on weather conditions, and it creates the challenges of integrating SES with the grid. SPG forecasting is essential for power systems with a significant PV penetration. In this work, ELM with optimized weights, biases, and hidden neurons is segregated with the proposed MTLBO algorithm to improve the accuracy of the SPG forecasting of the Chhattisgarh state of India

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