Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting

Abstract

In view of the problems of insufficient real-time measurements in active distribution networks, a state estimation method for active distribution networks is proposed based on the forecasting of photovoltaic (PV) power generation. First, the extreme learning machine (ELM) enhanced by the genetic algorithm (GA) is used to forecast the PV power generation. Second, the Gaussian mixture model (GMM) is used to model the forecasting error. The weighted mean of the forecasting error is used to correct the forecasting value of the PV power generation, and the weighted variance of the forecasting error is used as the basis for setting the pseudo measurement weight. Finally, the real-time measurements collected by the supervisory control and data acquisition (SCADA) system, the forecasted pseudo measurements, and the virtual measurements are used to estimate the state of the active distribution network using the weighted least square (WLS) algorithm. Through simulations in the IEEE 33-bus system, it is shown that the proposed model provides accurate and reliable pseudo measurements for the active distribution network, improves the redundancy of the system, and thus further improves the accuracy of the state estimation and the capability of detecting and identifying bad data in active distribution systems without adding measurement devices.