Optimal µ-PMU Placement and Voltage Estimation in Distribution Networks: Evaluation Through Multiple Case Studies

Micro-Phasor Measurement Units (µ-PMUs) have emerged as advanced devices for power system monitoring; nevertheless, their high-cost limits large-scale deployment. To address this, optimization algorithms can be employed for their optimal placement (OµPP). This research investigates two optimization techniques for OµPP, aiming to minimize µ-PMU placement costs while maintaining maximum observability. Three cases are analysed: Case 1 (OµPP under normal conditions), Case 2 (OµPP with a single µ-PMU outage), and Case 3 (OµPP considering Zero Injection Buses, ZIBs). Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used to solve this problem. MATPOWER version 8.0 is used to obtain the IEEE 33 and 69 distribution bus systems for the case studies. Results show that GWO outperforms PSO in reducing the number of µ-PMUs while ensuring observability, especially in larger systems, while PSO is more effective in smaller systems. Voltage estimation from µ-PMU data is conducted using the WLS algorithm, with three noise levels (0.01, 0.02, 0.04) considered to assess the impact of measurement noise. The findings indicate that increasing µ-PMU placement, as in Case 2, improves voltage estimation accuracy and stability, while a reduced number of µ-PMUs, as in Case 3, shows large deviations and instability under higher noise levels. This analysis highlights the trade-off between cost and reliability in achieving effective monitoring.