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Intelligent Control of Compensating Devices for Power Quality Enhancement in Radial Network

  • Imran Ahmad Quadri 1,*,   
  • Nayan Kumar 2,   
  • Shahzad Ahsan 3

Received: 05 Sep 2025 | Revised: 19 Sep 2025 | Accepted: 22 Sep 2025 | Published: 28 Sep 2025

Abstract

In order to enhance the operational efficacy of distributionnetworks (DPNs) across technical, economic and environmental aspects within a real-time operational framework, meticulous regulation of reactive power is imperative. In the present study, a Comprehensive Teaching-Learning based Optimization (CTLBO) algorithm is employed for network reconfiguration (NR) and optimal allocation of Distribution Static Synchronous Compensators (DSTATCOMs) for single-objective along with 24-hour practical load profiles in the IEEE 33-bus and 69-bus radial distribution systems (RDSs). Several case studies demonstrate that simultaneous NR and DSTATCOM allocation is the most effective solution for reduction of system losses, operational costs and emission. The results further demonstrates the superiority in terms of convergence characteristics, solution robustness and global optimality of the CTLBO algorithm under complex , multi-criteria constraints for NR and DSTATCOM allocation in RDS against established bio-inspired metaheuristics such as the Immune Algorithm (IA), Bat Algorithm (BA) and Bacterial Foraging Optimization Algorithm (BFOA).

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Quadri, I. A.; Kumar, N.; Ahsan, S. Intelligent Control of Compensating Devices for Power Quality Enhancement in Radial Network. Smart Energy Systems 2025, 1 (1), 2.
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