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STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural

Hamed Atyia Soodi1, Ahmet Mete Vural1

  • 1Electrical and Electronics Engineering Department, University of Gaziantep, Şahinbey, 27310 Gaziantep, Turkey.

Computational Intelligence and Neuroscience
|June 2, 2018
PubMed
Summary

This study compares optimization techniques for training Artificial Neural Networks (ANNs) to estimate Static Synchronous Compensator (STATCOM) performance. The Shuffled Frog Leap Algorithm (SFLA) demonstrated superior efficiency in training ANNs for STATCOM voltage and reactive power estimation.

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Power Systems

Background:

  • Traditional power system analysis relies on iterative load flow methods like Newton-Raphson.
  • Static Synchronous Compensators (STATCOMs) are crucial for voltage regulation in power systems.
  • Artificial Neural Networks (ANNs) offer a potential alternative for estimating STATCOM parameters.

Purpose of the Study:

  • To evaluate various optimization algorithms for training ANNs to estimate STATCOM voltages and reactive powers.
  • To compare the efficiency of different ANN training methods against traditional iterative approaches.
  • To identify the most effective optimization technique for STATCOM parameter estimation.

Main Methods:

  • Implementing and comparing Back-Propagation, Particle Swarm Optimization (PSO), Shuffled Frog Leap Algorithm (SFLA), and Genetic Algorithm (GA) for ANN training.
  • Utilizing IEEE bus data for performance analysis of the optimization techniques.
  • Solving power flow equations using the Newton-Raphson method for baseline comparison.

Main Results:

  • The Shuffled Frog Leap Algorithm (SFLA) showed the highest efficiency in training ANNs for STATCOM parameter estimation.
  • Particle Swarm Optimization (PSO) was the second most effective method.
  • ANNs trained with SFLA and PSO provided accurate estimations of STATCOM voltages and reactive powers.

Conclusions:

  • SFLA and PSO are highly effective optimization techniques for training ANNs in power system applications.
  • ANN-based estimation offers a promising alternative to iterative methods for STATCOM analysis.
  • The study highlights the potential of advanced optimization algorithms in enhancing power system stability and control.