1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Baoding 071003, China;2.State Grid Shanghai Municipal Electric Power Company, Shanghai 200437, China

As the scale of market transactions becomes larger and the amount of transaction data increases, it becomes possible to conduct collusion analysis with data. Therefore, combining with the collusion early-warning indicator system of the power generation enterprises and the unsupervised variational autoencoding Gaussian mixture model (VAEGMM), the intelligent early-warning of the collusion between power generation enterprises is realized. Firstly, a complete indicator system for the collusion early-warning and a detailed indicator calculation method are proposed. Secondly, in view of the high-dimensional data characteristics of the index set and the imbalance of positive and negative samples, the VAEGMM is proposed based on the idea of anomaly detection. Then, the network structure of VAEGMM is described in detail, and the joint loss function is reconstructed, making the network better learn the low dimensional expression of the original data. Thus it is helpful for more accurate density estimation. Finally, the actual case study shows that compared with other traditional unsupervised learning models, VAEGMM can warn the risk of collusion more efficiently and accurately.

This work is supported by State Grid Corporation of China (No. SGSHDK00HZJS2000254).