Affiliations: [a]
Department of Electrical and Electronics Engineering, Anna University, Chennai, India
| [b]
Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, India
Correspondence:
[*]
Corresponding author. S. Joshibha Ponmalar, Department of Electrical and Electronics Engineering, Anna University, Chennai, 600025, India. E-mail: joshibha1998@gmail.com.
Abstract: A novel technique is presented for Maximum Power Point Tracking (MPPT) based photovoltaic (PV) system in partial shadow conditions for harvesting maximum power. In this paper, a hybrid technique is developed, which combines Black Widow Optimization (BWO) with Recurrent Neural Network (RNN). To train the data set and provide a control signal for the converter, an RNN is used. After fitting the training data sets, the suggested method achieved maximum power by utilizing BWO based on the control parameters. This proposed method minimizes the difference between actual and average power. Using an optimization technique, the main goal of this proposed strategy is to obtain peak power harvest under various conditions, including partial shading, while minimizing error function, With the help of MATLAB/Simulink software, the conclusions are revealed under various partial shading conditions. For each category, the observed results are evaluated at various time intervals. The proposed method is also compared to other techniques such as the Ant Colony Optimization (ACO)-RNN system, Particle Swarm Optimization (PSO)-RNN system, and Gravitational Search Algorithm (GSA)-RNN system. The proposed system is 36.11% faster than GSA with RNN, 39.47% faster than PSO, and 42.5% faster than ACO with RNN in terms of tracking speed. Significantly, the proposed work is 0.87% more efficient than the other models in terms of obtaining maximum power. In terms of obtaining maximum power, the proposed work BWOA-RNN is more effective than other methods.
Keywords: Partial shading, maximum power point tracking (MPPT), photovoltaic (PV), black widow optimization (BWO), recurrent neural network (RNN), gravitational search algorithm (GSA), ant colony optimization (ACO), and particle swarm optimization (PSO)
