摘要
随着分布式能源的快速发展,准确预测分布式能源的出力成为了配电网可靠性评估的重要组成部分,为提高配电网可靠性评估准确性,本文提出了一种融合VMD-QRCNN-BiLSTM预测与DFT-MP-DBN建模的主动配电网可靠性评估方法。首先通过变分模态分解将原始风光荷时间序列分解为固有模态分量,并采用分位数回归卷积神经网络对风光出力以及负荷进行特征提取;而后使用双向长短期记忆相结合建模各变量的时间序列特征,并生成预测值;其次预测值作为动态故障树的输入,并采用连续时间马尔可夫链,并获取状态转移率矩阵;最后采用动态贝叶斯网络刻画状态的时序依赖,并加入观测或控制变量。以IEEE RBTS Bus 2系统为例,实验结果表明,所提方法的SAIFI、SAIDI、AENS和ASAI指标分别为0.231次/户/年、3.496小时/户/年、17.465 kWh/年和99.943%,显著优于传统方法,验证了其在提高配电网可靠性评估精度和效率方面的有效性。
With the rapid development of distributed energy resources,accurate prediction of their output has become a critical component in the reliability assessment of distribution networks.To enhance the accuracy of such assessments,this paper proposes a reliability evaluation method for active distribution networks that integrates VMD-QRCNN-BiLSTM-based forecasting with DFT-MP-DBN modeling.First,the original time series data of wind power,solar power,and load are decomposed into intrinsic mode components using Variational Mode Decomposition(VMD).Then,a Quantile Regression Convolutional Neural Network(QRCNN)is employed to extract the temporal features,and a Bidirectional Long Short-Term Memory(BiLSTM)network is used to model each variable and generate accurate forecasts.These predicted values are then input into a Dynamic Fault Tree(DFT),where a Continuous-Time Markov Process(MP)is used to compute the state transition rate matrix.Finally,a Dynamic Bayesian Network(DBN)is applied to capture the temporal dependencies among system states and incorporate observed or control variables.Case studies based on the IEEE RBTS Bus 2 system show that the proposed method achieves superior reliability performance,with SAIFI,SAIDI,AENS,and ASAI values of 0.231 times/customer/year,3.496 hours/customer/year,17.465 kWh/year,and 99.943%,respectively—significantly outperforming traditional approaches.These results validate the effectiveness and advantages of the proposed method in improving the precision and efficiency of distribution network reliability assessments.
作者
牟晋麟
杨超
Mou Jinlin;Yang Chao(Department of Electrical Engineering,Guizhou University,Guiyang 550025,China)
出处
《电子测量技术》
北大核心
2025年第24期148-158,共11页
Electronic Measurement Technology
基金
2025年科技成果转化联合基金(黔科合成果LH[2025]重点014)项目资助。
