配电网运行风险评估在线监测数据的对抗性特征学习
Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks
摘要：
随着配电网在线监测系统的部署，通过它们收集的大量数据包含了丰富的网络运行状态信息。通过利用这些数据，本文提出了一种基于双向生成对抗网络（BiGANs）的无监督方法，用于配电网的操作风险评估。该方法包括两个阶段：（1）对抗性特征学习。从在线监测数据中提取最具代表性的特征，并计算特征的统计指数$\mathcal{N}_ϕ$，在此期间，我们对真实数据不做任何假设或简化。(2)业务风险评估。将标准化$\mathcal{N}_ϕ$的总体均值的置信水平$1-α$与分为紧急、高风险、预防和正常的操作风险水平相结合，计算每个数据点的p值，并与$\fracα{2}$进行比较，以确定风险水平。该方法能够发现真实数据的潜在结构，并提供更准确的评估结果。合成数据来说明所提出的方法中涉及的参数的选择。实际在线监测数据的案例研究验证了该方法在风险评估中的有效性和优势。
英文摘要：
With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for operational risk assessment in distribution networks in this paper. The approach includes two stages: (1) adversarial feature learning. The most representative features are extracted from the online monitoring data and a statistical index $\mathcal{N}_ϕ$ is calculated for the features, during which we make no assumptions or simplifications on the real data. (2) operational risk assessment. The confidence level $1-α$ for the population mean of the standardized $\mathcal{N}_ϕ$ is combined with the operational risk levels which are divided into emergency, high risk, preventive and normal, and the p value for each data point is calculated and compared with $\fracα{2}$ to determine the risk levels. The proposed approach is capable of discovering the latent structure of the real data and providing more accurate assessment result. The synthetic data is employed to illustrate the selection of parameters involved in the proposed approach. Case studies on the real-world online monitoring data validate the effectiveness and advantages of the proposed approach in risk assessment.
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