MULTISTAGE HYBRID EVOLUTIONARY COMPUTING BASED OPTIMAL PMU PLACEMENT FOR LARGE SCALE POWER GRID NETWORK
industrial electrical power systems
The high pace rise in energy consumption across industrial-social horizon, the reliable and quality power provision have become inevitable need. Phasor measurement unit (PMU) is one of the most significant grid components that plays vital role in ensuring reliable power transmission and distribution. However, maintaining cost effective and efficient electrical grid network design have been the dominating motivations for researchers. The optimal PMU placement (OPP) in power system can not only ensure grid cost reduction, real-time monitoring and control, but can reduce the operational complexities and overheads significantly. This paper proposes a novel multistage hybrid evolutionary computing scheme for OPP solution. Our proposed model applied Adaptive Genetic Algorithm (AGA) for initial state point retrieval for OPP, which was then fed as input to the Pattern Search (PS) based PMU placement optimization. Our proposed AGA-PS scheme ensures OPP solution by retrieving minimum number of PMUs and its optimal location across grid network to make power system completely observable under varied cases such as single PMU loss and zero injection bus conditions. The simulation results with IEEE 14, IEEE 39, IEEE 118 and KPTCL 155 bus networks has exhibited that the proposed AGA-PS outperforms major existing approaches in terms of optimal OPP solutions. Performance with KPTCL 155 reveals that the AGA-PS can be useful for cost-effective large scale power grid design purpose.
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