Identification of Power Network Branch Parameters Based on State Space Transformation

Deviations or errors in power system branch parameters will seriously affect the effectiveness of power system state estimation and subsequent advanced applications. In this paper, a local estimation algorithm for the suspicious parameters of power network branches is proposed based on the state space transformation. After the parameter detection process, a local estimation network is formed by setting the suspicious branch as the searching center. According to the measurement type, the measurement equations are established with branch parameters as state vector. Then, add the non-suspicious branch parameters in the region to the measurement equation as pseudo-measurements to improve the measurement redundancy. Finally, all suspicious parameters, including closely related ones, are identified simultaneously, and the estimated correction values are worked out by multi-section statistical analysis. The identification method is illustrated in the PMU and SCADA measurement systems. Considering that the voltage measurement in the SCADA system cannot be directly applied, a fixed-point iteration scheme is proposed. The identification process of the suspicious branch parameters is decomposed into two nested loop iterations. The validity of the algorithm is verified on the IEEE 118-bus and 300-bus systems and the influence of measurement error on the identification result is also discussed.