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The Control Strategy for Optimization of Voltage and Reactive Power in Substation Based on Load Forecasting

机译:基于负荷预测的变电站电压无功优化控制策略

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In order to avoid some phenomena such as too many regulating times of on load tap changer, too frequent actions of capacitor switching, lower bus voltage qualification rate and higher network loss, it is presented that a control strategy for optimization of voltage and reactive power in substation based on load forecasting in this paper. The load and system voltage forecasting are realized by using radical basis function neural network. With the times of equipments' actions and the voltage quality as constraint conditions, the optimization objective function of the minimum system loss is established. The initial subsection load is based on apparent power fully compensated by the existing compensation capacitors. According to the subsection principle that voltage quality should be as high as possible, the tap position of transformer is determined. According to the initial optimization results, the reactive power is used as subsection load by the principle of as much as possible compensation. The best amount of groups of compensation capacitor is determined in the secondary subsection optimization. The application of 35kV distribution system in Yucheng County of Henan Province is analyzed in this paper. The voltage quality, the equipments' actions number limit, the maximum reducing system network loss can be achieved.
机译:为了避免诸如有载分接开关调节时间过多,电容器切换动作过于频繁,母线电压合格率较低以及网络损耗较高等现象,提出了一种优化电网电压和无功功率的控制策略。本文基于负荷预测的变电站。负荷和系统电压的预测是使用根基函数神经网络实现的。以设备动作次数和电压质量为约束条件,建立了最小系统损耗的优化目标函数。最初的分段负载基于视在功率,而视在功率则由现有的补偿电容器完全补偿。根据分段电压质量原则,确定变压器的抽头位置。根据最初的优化结果,无功功率将根据尽可能多的补偿原则用作分段负载。补偿电容器组的最佳数量在次级优化中确定。分析了35kV配电系统在河南禹城县的应用。可以实现电压质量,设备动作次数限制,最大程度减少系统网络损耗。

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