Spatio-Temporal Generative Adversarial Network Based Power Distribution Network State Estimation With Multiple Time-Scale Measurements

The increasing penetration of distributed renewable generation has introduced significant uncertainties and randomness to the power distribution network operation. Accurate and timely awareness of the network operation is of paramount importance to ensure system safety and reliability and is considered nontrivial and costly as substantial network reinforcement with advanced measurement devices is generally required. Also, the existing state estimation methods, e.g., weighted least square, may not converge in the presence of incomplete and inaccurate measurements. This article proposes a spatio-temporal estimation generative adversarial network (ST-EGAN) consisting of feature extraction, information completion, data reconstruction, and fake data discrimination to generate high-resolution pseudo-measurements to promote the accuracy and robustness of state estimation. The task of high-resolution power distribution network state estimation is carried out based on the mixed dataset of multiple time-scale measurements obtained from supervisory control and data acquisition and phasor measurement units. The proposed solution is extensively assessed using the IEEE 33-bus test network compared with the existing solutions for a range of scenarios with different resolutions and noise intensities. The numerical results demonstrated that the proposed ST-EGAN can reduce the mean rmse by 4.78% compared to interpolation algorithms, and reduce the rmse by 0.14% and 0.21% compared with deep convolutional generative adversarial networks and super-resolution convolutional networks, respectively, in the presence of noises with different intensities. The proposed method can be generalized to cases with different topological structures and measurement assembly conditions.