基于深度神经网络和内外部因素的大电网安全态势感知研究

随着电网结构的日益复杂,运行调度变得更加困难,大停电事故发生的风险也日益增加,因此能够及时有效地对大电网的安全态势进行感知显得尤为重要。在态势要素提取阶段,从内部因素与外部因素两个方面出发,构建大电网安全态势评价体系,其中外部因素通过统计分析1981年~2015年全国电网的大停电事故得出;在态势理解阶段,通过层次分析法与改进的熵权法获得各指标的综合权重,加权平均得到大电网的安全态势评估值,实现对大电网安全态势的综合评价;在态势预测阶段,构建深度神经网络模型,完成对大电网安全态势的预测。为进一步验证预测模型的有效性,将其与BP神经网络和RBF神经网络对比分析,验证了深度神经网络模型可以有效地对大电网的安全态势进行预测,且预测精度高于传统的神经网络模型。

Research on Security Situation Awareness of Large Power Grid Based on Deep Neural Network and Internal and External Factors

With the increasingly complex power grid structure,the operation scheduling becomes more difficult,and the risk of large blackout accidents is increasing.Therefore,it is particularly important to be able to timely and effectively observe the security situation of the large power grid.In the extraction stage of situational factors,the security situation evaluation system of large power grid is constructed from two aspects of internal and external factors.The external factors are obtained through statistical analysis of the large blackout accidents of the national power grid from 1981 to 2015.In the stage of situational understanding,the comprehensive weights of each index are obtained by analytic hierarchy process and the improved entropy weight method,the weighted average is obtained from the safety situation assessment value of the large power grid to achieve comprehensive evaluation of the security situation of large power grids.In the stage of situation prediction,a deep neural network model is built to complete the prediction of the security situation of the large grid.In order to further verify the validity of the prediction model,it is compared with BP neural network and RBF neural network to verify that the model of deep neural network can effectively predict the security situation of large power grid,and the prediction accuracy is higher than the traditional neural network model.