Data-Driven Observability Enhancement in Active Distribution Networks via Optimal Co-Deployment of DGs and D-PMUs Using Hybrid APSO-ASDA

• Mela I. L. LASHIRU

  Author

• Ganiyu A. BAKARE

  Author

• Sabo M. HASSAN

  Author

• Kamilu O. LAWAL

  Author

The increasing deployment of renewable Distributed Generators (DGs) introduces operational uncertainty and observability challenges in Active Distribution Networks (ADNs). Traditional Supervisory Control and Data Acquisition (SCADA) systems lack the temporal resolution required for real-time monitoring. This paper proposes a data-driven framework for enhancing network observability through optimal co-deployment of DGs and Distribution Phasor Measurement Units (D-PMUs) using a hybrid Accelerated Particle Swarm Optimization-Adaptive Spiral Dynamic Algorithm (APSO-ASDA). The objective is to maximize network observability with minimal measurement redundancy for improved Distribution System State Estimation (DSSE). Simulation results on the IEEE 13-bus ADN, modeled in MATLAB Simulink R2023b, showed ASDA optimal DG -D-PMU placement at buses 1, 4, with corresponding sizes of 340Kw(solar), and 445kW(wind) with D-PMU at 6, 7, 10, and 12.   APSO DG placement is at bus 3 and 4 with a size of 60kW(solar), 400kW(wind), D-PMU at buses 6, 8, 9. The hybrid approach places DG at buses 1 and 3 with corresponding capacities of 110kW (solar) and 250kW (wind), and D-PMU allocation at buses 4, 6, 7, 8, 11 and 12. The hybrid algorithm achieved a rapid reduction in the cost function from 278.7 to 81.14 within the first 36 iterations, highlighting its effectiveness for real-time, data-driven applications.

• FJET_21_78_78

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