基于极限学习机的配电网重构

摘要

为使配电网重构有功功率损耗最小,提出一种基于极限学习机的神经网络重构模型来反映配电网负荷模式与开关状态之间的非线性关系.将配电网负荷模式作为输入、网损最小时的开关状态作为输出,利用所提模型网络结构简单、学习速度快的优势进行配电网重构.引入统计学习理论中的结构风险最小化准则来改进基于经验风险最小化的极限学习机,使经验风险和置信范围最小,从而使实际风险最小,减小期望误差.通过2个典型算例对配电网重构进行仿真研究,并对基于支持向量机、BP神经网络和基于经验风险最小化的极限学习机重构模型进行比较,结果表明所提模型在保持学习速度快的同时,泛化性能更高.%To minimize the active power loss of distribution grid reconfiguration,a neural network reconfiguration model based on the extreme learning machine is proposed,which reflects the nonlinear relationship between the load pattern and the switch state of distribution grid. Having simple network structure and fast training speed,the model takes the load pattern as its input and outputs the switch states to reconfigure the distribution grid with minimum active power loss. The structural risk minimization rule of the statistical learning theory is introduced into the extreme learning machine based on the empirical risk minimization to minimize the empirical risk and confidence interval. The actual risk is thus minimized and the expectation error is decreased. Simulative research is carried out for two typical cases of distribution network reconfiguration with different reconfiguration models:support vector machine,BP neural network and extreme learning machine. Results show that the proposed model has both better generalization performance and faster training speed.