Performance comparison of several classical controllers in AGC for multi-area interconnected thermal system

A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (KIi) for the secondary control, governor speed regulation parameters (Ri) for the primary control and frequency bias parameters (Bi), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of KIi, Ri, and Bi parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of Ri (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized KIi, Ri, and Bi to wide changes in inertia constant (H), reheat time constant (Tr), reheat coefficient (Kr), system loading condition, and size and position of step load perturbation.

Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control

Intelligent Power System Operation and Control: Japan Case Study Application of Intelligent Methods to Power Systems Application to Power System Planning Application to Power System Control and Restoration Future Implementations Automatic Generation Control (AGC): Fundamentals and Concepts AGC in a Modern Power System Power System Frequency Control Frequency Response Model and AGC Characteristics A Three-Control Area Power System Example Intelligent AGC: Past Achievements and New Perspectives Fuzzy Logic AGC Neuro-Fuzzy and Neural-Networks-Based AGC Genetic-Algorithm-Based AGC Multiagent-Based AGC Combined and Other Intelligent Techniques in AGC AGC in a Deregulated Environment AGC and Renewable Energy Options AGC and Microgrids Scope for Future Work AGC in Restructured Power Systems Control Area in New Environment AGC Configurations and Frameworks AGC Markets AGC Response and an Updated Model Neural-Network-Based AGC Design An Overview ANN-Based Control Systems Flexible Neural Network Bilateral AGC Scheme and Modeling FNN-Based AGC System Application Examples AGC Systems Concerning Renewable Energy Sources An Updated AGC Frequency Response Model Frequency Response Analysis Simulation Study Emergency Frequency Control and RESs Key Issues and New Perspectives AGC Design Using Multiagent Systems Multiagent System (MAS): An Introduction Multiagent Reinforcement-Learning-Based AGC Using GA to Determine Actions and States An Agent for ss Estimation Bayesian-Network-Based AGC Approach Bayesian Networks: An Overview AGC with Wind Farms Proposed Intelligent Control Scheme Implementation Methodology Application Results Fuzzy Logic and AGC Systems Study Systems Polar-Information-Based Fuzzy Logic AGC PSO-Based Fuzzy Logic AGC Frequency Regulation Using Energy Capacitor System Fundamentals of the Proposed Control Scheme Study System Simulation Results Evaluation of Frequency Regulation Performance Application of Genetic Algorithm in AGC Synthesis Genetic Algorithm: An Overview Optimal Tuning of Conventional Controllers Multiobjective GA GA for Tracking Robust Performance Index GA in Learning Process Frequency Regulation in Isolated Systems with Dispersed Power Sources Configuration of Multiagent-Based AGC System Configuration of Laboratory System Experimental Results

Intelligent Automatic Generation Control

Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller

Load-frequency control (LFC) in interconnected power systems is undergoing fundamental changes due to rapidly growing amount of wind turbines, and emerging of new types of power generation/consumption technologies. The infrastructure of modern LFC systems should be able to handle complex multiobjective regulation optimization problems characterized by a high degree of diversification in policies, and widely distribution in demand and supply sources to ensure that the LFC systems are capable to maintain generation-load balance, following serious disturbances. Wind power fluctuations impose additional power imbalance to the power system and cause frequency deviation from the nominal value. This paper addresses a new decentralized fuzzy logic-based LFC schemes for simultaneous minimization of system frequency deviation and tie-line power changes, which is required for successful operation of interconnected power systems in the presence of high-penetration wind power. In order to obtain an optimal performance, the particle swarm optimization technique is used to determine membership functions parameters. The physical and engineering aspects have been fully considered, and to demonstrate effectiveness of the proposed control scheme, a time domain simulation is performed on the standard 39-bus test system. The results are compared with conventional LFC design for serious load disturbance and various rates of wind power penetrations.

Fuzzy Logic-Based Load-Frequency Control Concerning High Penetration of Wind Turbines

This paper describes the design, implementation and operational performance of a fuzzy controller as part of the automatic generation control (AGC) system in Eskom's National Control Centre. The fuzzy controller was implemented in the control ACE (area control error) calculation, which determines the shortfall or surplus generation that has to be corrected. This paper sets out the problems associated with secondary frequency control and AGC. The difficulties associated with optimising the original standard AGC controller, the design, implementation and optimisation of the fuzzy controller and the operational performance of the new controller over two years, are discussed. The fuzzy controller was integrated into the existing off-the-shelf-AGC system with only a few modifications. The operational performance over two years showed an overall improvement of over 50% in the reduction of control compared to the original AGC controller, and an initial improvement of 20% in the quality of control of the optimised original controller.

R.C. Hartman

Papers

Design and experience with a fuzzy logic controller for automatic generation control (AGC)