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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Under voltage load shedding using hybrid ABC-PSO algorithm for voltage stability enhancement.

Susan Mumbi Kisengeu1, Christopher Maina Muriithi2, George Nyauma Nyakoe1

  • 1Electrical Engineering Department, Pan African University Institute for Basic Sciences, Technology and Innovation, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

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Summary

This study introduces a novel hybrid ABC-PSO algorithm for optimal under-voltage load shedding (UVLS) to prevent voltage collapse. The new method significantly improves voltage recovery and load shedding efficiency in power systems.

Keywords:
Artificial bee colonyFast voltage stability indexingHybrid metaheuristic algorithmsParticle swarm optimizationUnder-voltage load sheddingVoltage stability

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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Computational Intelligence

Background:

  • Voltage collapse is a critical issue in power systems, often triggered by large faults and leading to voltage instability.
  • Under-voltage load shedding (UVLS) is a last-resort protective measure when other control mechanisms fail.
  • Traditional load shedding methods are suboptimal; computational intelligence and hybrid algorithms offer more effective solutions.

Purpose of the Study:

  • To propose and evaluate a novel hybrid Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithm for optimal UVLS.
  • To enhance voltage stability and power system reliability through intelligent load shedding.
  • To compare the performance of the hybrid ABC-PSO algorithm against other computational intelligence techniques.

Main Methods:

  • A hybrid ABC-PSO algorithm was developed and applied to a modified IEEE 14-bus system for UVLS.
  • The system was subjected to overload conditions (105%–140%), and weak buses were identified using the Fast Voltage Stability Index (FVSI).
  • Decentralized relay settings (3.5s, 5s, 8s) were implemented, and simulations were performed using MATLAB and the Power System Analysis Toolbox (PSAT).

Main Results:

  • The proposed hybrid ABC-PSO algorithm achieved an 89.56% post-contingency load, outperforming Genetic Algorithm (GA), ABC-Artificial Neural Network (ANN), and PSO-ANN.
  • An overall voltage profile recovery of 99.32% was achieved.
  • The algorithm demonstrated superior capability in shedding optimal amounts of load compared to other methods.

Conclusions:

  • The hybrid ABC-PSO algorithm is a highly effective method for optimal UVLS, significantly improving voltage stability and system resilience.
  • This approach offers a more efficient and optimal solution for managing voltage instability during system contingencies.
  • The study validates the potential of hybrid computational intelligence algorithms in advanced power system protection and control.