【摘要】 针对稳态电能质量指标数据的时序性和非线性,提出一种基于遗传算法(GA)和BP神经网络的电能质量预测方法,将电能质量历史数据和负荷数据、气象数据作为网络输入,使用GA优化BP神经网络的初始权值、阈值,通过该网络完成数据集的训练与预测。最后使用某线路电能质量检测点获得的真实数据验证了模型的准确性、可行性。更多还原

【Abstract】 Aiming at the time series and nonlinearity of steady-state power quality index data, a power quality prediction method based on genetic algorithm(GA) and BP neural network is proposed. The power quality historical data, load data, and meteorological data are used as the network input, and GA is used to optimize the initial weights and thresholds of the BP neural network, and the training and prediction of the data set is completed through the network. Finally, the accuracy and feasibility of the model are verified by using the real data obtained from the power quality inspection point of a certain line.更多还原