State Estimation based Fault Analysis and Diagnosis in a Receiving-end Transmission System

In east China, a huge amount of electrical power is imported from other areas in China, and hence the power system in east China is a receiving-end one. In this work, the fault analysis and diagnosis of a receiving-end transmission system are addressed. To clear fault and restore power supply quickly, accurate fault analysis and diagnosis are essential. State estimation-based fault location diagnosis methods utilize a series of errors between the estimated and measured outputs to estimate fault distance. However, these methods rely on prior knowledge and high-dimensional of state variables. A fault location diagnosis method for a receiving-end transmission system is presented based on the well-established Ensemble Kalman Filter (EnKF). Two π-section equivalent models are utilized to represent the line segments on the left and right of the fault point. The location of fault as well as the parameters of π-section equivalent models are estimated and updated during the EnKF process. Simulation results of a sample transmission system indicate that the presented method does not rely on prior knowledge like the fault type and initial values of the system operation, and can estimate the fault distance accurately and efficiently. Moreover, the presented method is applicable for different scenarios.