Abstract
Phasor Measurement Unit (PMU) is a smart measuring device commonly used in wide-area monitoring systems. It provides the synchronized phasor values and the magnitudes of voltages and currents in real-time for the proper state calculation of the electrical network in a common time reference frame. But in order to avoid unnecessary placements, minimize installation cost, and due to the lack of communication facilities at the substations, the placing of PMUs at every location is not possible. Therefore several optimization techniques have been developed to solve the Optimal PMU Placement (OPP) problem. The OPP problem aims to reduce the number of PMUs by achieving a completely observable network. Many solutions to the OPP problem have been proposed for the transmission networks with the use of conventional and heuristic-based approaches, but very few for distribution networks. In this paper, the Binary Grey Wolf Optimization (BGWO) algorithm is proposed to solve the OPP problem considering the measurement redundancy (MR) to achieve complete observability of the distribution network. Finally, case studies have been done by implementing the proposed algorithm on different IEEE test feeders such as the IEEE -13, -33, -37, and -123 node feeder systems. The obtained results are compared with previous studies to verify the feasibility and efficiency of the proposed technique.
Highlights
Proper monitoring, control, and protection of a power system can only be possible with complete observability of the entire network
The proposed Binary Grey Wolf Optimization (BGWO) was implemented on several IEEE test node feeders including IEEE -13, -33, -37, and -123 node systems to solve the Phasor Measurement Unit (PMU) placement problem using the MATLAB 2019 version
The results show that a total of 6 PMUs are required for the IEEE 13 system
Summary
Control, and protection of a power system can only be possible with complete observability of the entire network. The installation of PMU at different bus locations monitors the whole network by providing an accurate state estimation of each bus, but it is not possible to place PMUs at each bus as the installation includes the high cost of PMU devices along with its communication infrastructure and the data handling capacity This leads to the optimal PMU placement problem. Teaching-learning based optimization has been suggested in [15] to find the minimum PMUs required for the monitoring of the transmission network All these traditional and heuristic approaches have been implemented in transmission networks. A binary-based grey wolf optimization technique is developed to minimize the PMUs with complete observability and to maximize the measurement redundancy for distribution networks
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