Wang Fuhua, Zhang Zongdong, Wu Kai, Jian Dongxiang, Chen Qiang, Zhang Chao, Dong Yanling, He Xiaotong, Dong Lin
Turpan Power Supply Company, State Grid Xinjiang Electric Power Company, Turpan, 838000, China.
Shandong University, Jinan, 250102, China.
Math Biosci Eng. 2023 Jul 4;20(8):14518-14549. doi: 10.3934/mbe.2023650.
In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands.
在现代电力系统中，高效的接地故障选线对于维持配电网的稳定性和可靠性至关重要，特别是考虑到能源需求的不断增长以及可再生能源的整合。本系统综述旨在研究用于接地故障选线的各种人工智能（AI）技术，包括人工神经网络、支持向量机、决策树、模糊逻辑、遗传算法以及其他新兴方法。本综述分别讨论了每种技术的应用、优点、局限性和成功案例研究，为该领域的研究人员和专业人士提供了有价值的见解。此外，本综述还研究了当前人工智能方法所面临的挑战，如数据收集、算法性能和实时要求。最后，该综述突出了该领域未来的趋势和进一步研究的潜在途径，重点关注深度学习、大数据分析和边缘计算在进一步改善配电网接地故障选线方面的广阔潜力，最终提高其整体效率、恢复力和对不断变化的需求的适应性。
