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IAESCOREIAESCORE

Journal International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE) Abstract

In wind energy conversion systems, maximum power point tracking (MPPT) performance is crucial, as it is directly related to wind speed variability and the characteristics of the equipment used. Maximum power point tracking controllers are essential for optimizing the efficiency of wind power generation. This paper presents the development of three distinct approaches to maximum power point tracking: the classical perturb and observe (P&O) method, and two other techniques based on artificial intelligence, namely long short-term memory (LSTM) networks and deep neural networks (DNNs). Rather than focusing solely on the development of an intelligent neural network-based maximum power point tracking model, our work emphasizes the design of a deep neural network controller with an optimized architecture and a reduced number of layers and neurons per layer, thereby simplifying its implementation in embedded process control units while maintaining high maximum power point tracking performance. The results obtained show that our optimized deep neural network model identifies the point of maximum power more effectively than other techniques, demonstrating remarkable performance in terms of response time, accuracy, and the quality of the generated power.

Conclusion

This study successfully developed and evaluated an optimized deep neural network (DNN) model for maximum power point tracking (MPPT) in wind turbine systems.The results demonstrate that the optimized DNN model outperforms both the classical perturb and observe (P&O) method and the long short-term memory (LSTM) network in terms of tracking speed, accuracy, and power quality.The optimized architecture of the DNN, with a reduced number of layers and neurons, facilitates its implementation in embedded systems, making it a practical solution for real-time MPPT control.

Future Research

Further research should investigate the application of the optimized DNN model to different types of wind turbines and generator configurations to assess its generalizability. Exploring the integration of advanced forecasting techniques, such as wavelet transforms or hybrid models, could enhance the DNNs ability to anticipate wind speed variations and proactively adjust the MPPT strategy, leading to improved energy capture. Additionally, investigating the robustness of the optimized DNN model under various grid conditions and fault scenarios is crucial for ensuring reliable and stable operation of wind energy conversion systems, and could involve developing adaptive control strategies that dynamically adjust the DNN parameters based on real-time grid feedback. These investigations will contribute to the development of more efficient, reliable, and intelligent wind energy systems, ultimately accelerating the transition towards sustainable energy sources.

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Pages	11
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