Identification of Bad Data from Phasor Measurement Units Using Evolutionary Algorithms

Phasor measurement units (PMUs) are power system devices placed at various locations in electrical power network that provide the measurement of phasors of voltages and currents from the respective meters or instruments placed at locations. Data provided by the PMUs are used for system analysis and also help to analyze the sequence of events that may have contributed to the failure of the power system. The presence of bad data in PMUs is increasing in today's power system due to the system complexity and associated issues. This will lead to inaccurate measurement of voltage magnitude and phase angle. Hence, it is important to detect the presence of bad data and identify the PMU which contains the error. In order to identify the bad data, state estimation is carried out to obtain the true state variables by the application of Load Flow Analysis (LFA). The application of Engineering Optimization is called into this work for improving the detection of bad data and its correction. Firefly optimization and particle swarm optimization techniques are developed for the above problem, and later results are compared with respect to the accuracy of results. The system is modeled in MATLAB platform.