Optimal power quality monitor placement using genetic algorithm and Mallowâ€TMs Cp

This study presents a method to determine the optimal number and placement of power quality monitors (PQMs) in power systems by using genetic algorithm (GA) and Mallow’s Cp which is a statistical criterion for selecting among many alternative subset regressions. This procedure helps to avoid the dependency of set voltage sag threshold values of PQMs in the conventional monitor reach area based (MRA) method. In the proposed GACp method, the fitness function for problem modeling aims to minimize allocated monitors and minimize the difference between the Mallow’s Cp and the number of variables used for the multivariable regression model during estimation of unmonitored buses. After obtaining the optimal placements of PQMs by using the GACp method, the observability and redundancy of the monitors are tested to further reduce the redundant PQMs. The IEEE 30 bus test system is simulated using the DIGSILENT power factory software to validate the proposed method. The simulated results show that the GACp method requires only two PQMs to observe all voltage sags that may appear at each bus in the test system without redundancy.