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There are several methods to control power flow in power systems:
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Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
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Investigation of rank order centroid method for optimal generation control.

T Varshney1, A V Waghmare2, V P Singh2

  • 1Department of EECE, Sharda University, Greater Noida, Uttar Pradesh, India.

Scientific Reports
|May 17, 2024
PubMed
Summary

This study introduces the Rank Order Centroid (ROC) method for optimizing automatic generation control (AGC) in power systems. The Jaya Optimization Algorithm (JOA) enhances PID controller performance for stable power grid operations.

Keywords:
AGCJaya optimizationMCDMPID controllerRank order centroid

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Area of Science:

  • Electrical Engineering
  • Control Systems Engineering
  • Optimization Techniques

Background:

  • Multi-criteria decision-making (MCDM) is complex, especially in power system control.
  • Automatic Generation Control (AGC) in interconnected power systems requires balancing multiple performance objectives.

Purpose of the Study:

  • To propose the Rank Order Centroid (ROC) method for weighting sub-objective functions in MCDM for AGC.
  • To design and optimize a Proportional-Integral-Derivative (PID) controller for a two-area interconnected power system (TAIPS) using ROC and optimization algorithms.

Main Methods:

  • The Rank Order Centroid (ROC) method was used to determine weights for Integral Time Absolute Errors (ITAE) of frequency deviations, control errors, and tie-line power fluctuations.
  • A PID controller was designed based on the weighted objective function.
  • The Jaya Optimization Algorithm (JOA) was implemented to optimize the objective function, compared against TLBOA, LJA, NMSA, EHOA, and DEA.
  • Six case studies were performed with statistical analysis, including the Friedman rank test, to validate performance.

Main Results:

  • The JOA-based PID controller demonstrated superior performance in managing frequency and tie-line power fluctuations under various load conditions.
  • Systematic weighting of sub-objective functions using ROC improved controller design.
  • Comparative analysis confirmed the efficacy of JOA over other tested optimization algorithms.

Conclusions:

  • The proposed ROC method combined with JOA offers an effective approach for designing robust PID controllers for AGC in TAIPS.
  • This methodology enhances system stability and performance by optimally balancing multiple control criteria.
  • The findings provide a valuable framework for future research in intelligent power system control.