An Optimization-Based Topology Error Detection Method for Power System State Estimation.

• Keeping in mind the challenges in the practical implementation of the computational intelligence-based optimization methods, a conventional optimization-based topology error detection method is proposed in this paper. • Proposing a conventional optimization-based topology error detection method for systems with hybrid measurements, i.e., measurement set consisting of both supervisory control and data acquisition (SCADA) and PMU measurements. • Designing a computationally efficient optimization-based topology error detection method by application of the matrix inverse lemma. The proposed optimization-based topology error detection method is implemented in TOMLAB optimization platform using glcDirect solver. The paper presents an optimization-based method for topology error detection in power systems. The method utilizes the residual analysis in state estimation and minimization of normalized measurement residual, with the application of matrix inverse lemma. The work considers a hybrid measurement configuration, i.e., both SCADA and PMU measurements, for the test systems studied. The proposed method is implemented on the TOMLAB optimization platform under the mixed integer nonlinear programming category. The proposed method has been applied and tested on standard IEEE 14-bus and IEEE 118-bus test systems. The method is designed to be computationally efficient and produces accurate results for single topology error detection. The results from the IEEE 14-bus and IEEE 118-bus test systems have shown that the proposed method produces 100% and 94% accurate results for single topology error detection, respectively. The proposed method performs robustly with the increased measurement uncertainties and inclusion of bad data or gross errors in the measurements. The method has superiority in practical implementation over the meta-heuristics-based optimization methods. The proposed method can be easily implemented and could have potential application in the energy management systems of the power system control center. [ABSTRACT FROM AUTHOR]