Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Differential evolution algorithm based load frequency control in a two-area conventional and renewable energy based nonlinear power system

Authors: Muhammad Ahsan Zamee; Ashik Ahmed; Kazi Khairul Islam; Kazi Rehnuma Zafreen;

Differential evolution algorithm based load frequency control in a two-area conventional and renewable energy based nonlinear power system

Abstract

Load Frequency Control (LFC) for any power generating station is a subject of great concern for power system researchers. With the changes of load demand, frequency starts fluctuating which results in deviation in tie line power flow and frequency deviation at consumer end. To overcome this problem, many control techniques have been adopted. In early days fixed value integral/proportional-integral control, Optimal Control, Quantitative feedback theory, pole placement etc. methods were applied. In recent times, neural network, fuzzy logic, genetic algorithm controllers are replacing the conventional techniques. All the control techniques are used to find the optimal values of the PID/PI controller gain parameters (Kp, Ki, Kd) for which system stability is confirmed with minimum of Area Control Error (ACE). Differential Evolution (DE) which is a newer branch of genetic algorithms has been successfully applied in this problem. In this paper DE based PI controller has been implemented for Hydro-Thermal power plants to find out the optimal value of gain parameters for system stability. Nonlinearity has been considered in governor part of the thermal area for practical scenario. 1% step load changes have been applied to both areas simultaneously and individually to confirm its performance. Desired set of controller gain parameters (Kp, Ki) are selected based on eigenvalue and minimum value of Objective Function. All simulations are done in the MATLAB/SIMULINK environment.

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    7
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Average