基于灰色马尔科夫模型的配电网电压预测

Distribution Network Voltage Prediction Based on the Gray Markov Model

配电网监测平台只针对当前采集的数据量进行监测,无法及时提供预报警信息。通过对配电网监测平台中电压数据的分析,提出灰色马尔科夫电压预测模型。首先,建立灰色电压预测模型,并拟合变化趋势;然后,进行马尔科夫状态划分并计算状态转移概率矩阵,对下一时刻的电压变化趋势进行预测,并实时更新原始数据优化电压预测模型;最后,实例分析表明,灰色马尔科夫电压预测模型克服了电压数据随机波动造成预测结果不准确的问题,有效提高配电网电压预测的精度。

The distribution network monitoring platform only supervises the amount of data currently collected,and cannot provide pre-alarm information in time.Through an analysis of the voltage data in the distribution network monitoring platform,a gray Markov voltage prediction model was proposed.Firstly,a gray voltage prediction model was built,and variation tendency was fitted.Then,Markov state was divided and the state transition probability matrix was calculated to predict the voltage change trend at the next moment.In the meanwhile,original data were updated in real time to optimize the voltage prediction model.Finally,example analysis indicated that the gray Markov voltage prediction model overcame the problem of inaccurate prediction results caused by random fluctuation of voltage data,and improved the accuracy of voltage prediction for distribution networks.