On-line phase-splitting intelligent analysis of line loss in low-voltage platform based on genetic algorithm

Pengfei Lv, Dapeng Wang, H. Sun, B. Guo, Rila Sachu
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引用次数: 0

Abstract

When the power connection between the station area and the user changes, it is necessary to realize the user's return journey and return journey confirmation as soon as possible, which can improve the level of line loss management. Based on RBF neural adaptive genetic algorithm, the "intelligent analysis system of line Loss online phase division in low-voltage substation" can automatically control the phase reversal without manual intervention of base station users. Intelligent analysis of line loss and phase division of low voltage transformer is carried out by integrating various data of instrument. Based on the analysis of traditional circuit theory, a complete theoretical line loss calculation strategy is developed by using depth-first node determination method, supplemented by advanced artificial intelligence algorithm, which can achieve fast and accurate calculation under the condition of limited measurement data. To develop and build intelligent tools for substation regional line loss management, provide technical tools for substation regional line loss monitoring and control for line loss managers, improve the level of line information, damage calculation and analysis.
基于遗传算法的低压平台线损在线分相智能分析
当站区与用户之间的电源连接发生变化时,需要尽快实现用户的回程和回程确认,可以提高线损管理水平。基于RBF神经自适应遗传算法的“低压变电站线损在线分相智能分析系统”可以在不需要基站用户人工干预的情况下自动控制换相。通过综合各种仪器数据,实现了低压变压器线损分相的智能分析。在分析传统电路理论的基础上,采用深度优先节点确定方法,辅以先进的人工智能算法,制定了完整的理论线损计算策略,可以在测量数据有限的情况下实现快速准确的计算。开发建设变电站区域线损管理智能工具,为线损管理人员提供变电站区域线损监测与控制的技术工具,提高线路信息、损伤计算与分析水平。
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