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With the deployment of online monitoring systems in distribution networks,massive amounts of data collected through them contains rich information on theoperating states of the networks. By leveraging the data, an unsupervisedapproach based on bidirectional generative adversarial networks (BiGANs) isproposed for operational risk assessment in distribution networks in thispaper. The approach includes two stages: (1) adversarial feature learning. Themost representative features are extracted from the online monitoring data anda statistical index $\mathcal{N}_{\phi}$ is calculated for the features, duringwhich we make no assumptions or simplifications on the real data. (2)operational risk assessment. The confidence level $1-\alpha$ for the populationmean of the standardized $\mathcal{N}_{\phi}$ is combined with the operationalrisk levels which are divided into emergency, high risk, preventive and normal,and the p value for each data point is calculated and compared with$\frac{\alpha}{2}$ to determine the risk levels. The proposed approach iscapable of discovering the latent structure of the real data and providing moreaccurate assessment result. The synthetic data is employed to illustrate theselection of parameters involved in the proposed approach. Case studies on thereal-world online monitoring data validate the effectiveness and advantages ofthe proposed approach in risk assessment.

Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks