Robust adaptive learning control using spiking-based self-organizing emotional neural network for a class of nonlinear systems with uncertainties

Hou, Shixi; Qiu, Zhenyu; Chu, Yundi; Luo, Xujun; Fei, Juntao

doi:10.1016/j.engappai.2024.109039

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.109039

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Robust adaptive learning control using spiking-based self-organizing emotional neural network for a class of nonlinear systems with uncertainties

Shixi Hou,

Zhenyu Qiu,

Yundi Chu

et al.

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Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Chahine

2024

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Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems.

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Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Chahine

2024

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Robust adaptive learning control using spiking-based self-organizing emotional neural network for a class of nonlinear systems with uncertainties

Cited by 1 publication

References 50 publications

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

Machine Learning in Active Power Filters: Advantages, Limitations, and Future Directions

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