Sensitivity analysis-based optimal PMU placement for fault observability

This paper proposes an algorithm that finds the optimal sets of phasor measurement units (PMUs) to achieve a fault observable system, while it addresses the multi-estimation issue. The optimal PMU placement (OPP) problem here is to find a set of PMU locations with minimum number of members that enables fault observability in a system and satisfies a defined minimum sensitivity requirement in measurements. The proposed algorithm generalizes the impedance method in fault analysis and optimizes PMU utilization to maintain a required minimum sensitivity in each set of measurements, given the required fault detection accuracy. Also, the set of measurements is unique and distinctive for each fault scenario, preventing multi-estimation. A fault is referred to as a set of affected faulty line, fault location, and fault impedance. A sensitivity analysis is performed and sensitivity indices are derived to evaluate measurements quality to detect changes in fault line, location, or resistance. The algorithm is executed on IEEE 7-bus, 14-bus, and 30-bus test systems. Subsequently, artificial neural networks (ANN) are employed to build fault locators through offline training. ANN use an optimal PMU set obtained by the proposed algorithm to uniquely map between the corresponding measurements set and the faults.