Robust Framework for PMU Placement and Voltage Estimation of Power Distribution Network

This paper proposes a robust optimization methodology for phasor measurement unit (PMU) placement and voltage estimation in power distribution networks. The suggested method uses a hybrid Multi-Objective Particle Swarm Optimization and Differential Evolution (MOPSO-DE) algorithm to find the best PMU positions and the Weighted Least Squares (WLS) method to estimate voltage magnitude. The framework improves network observability with a minimum of PMUs, lowering deployment costs while retaining excellent estimation accuracy. The proposed approach reduces voltage estimation errors significantly by strategically placing PMUs in a distribution network. Furthermore, the method indicates high robustness in dealing with uncertainties associated with photovoltaic (PV) generation, ensuring accurate voltage estimation under different operational conditions. A Pareto front analysis yields key insights into the trade-off between system observability and estimation accuracy, allowing grid operators to make better decisions about sensor location. In addition, the computational efficiency of the MOPSO-DE method enables faster convergence and lower complexity than typical optimization techniques, making it a viable alternative for large-scale distribution networks. The findings support the proposed approach as a cost-effective, scalable, and practical solution for modern power networks with high renewable energy penetration. Future research will look into real-time adaptive state estimation utilizing machine learning-based predictive modelling, as well as expanding the framework to account for communication constraints and dynamic grid topology changes to improve its practical usefulness.