Abstract

Future energy descent systems will be expected to be controlled by the using of renewable power sources of which wind energy is one of the favorable sources. This paper treats with the implantation of genetic algorithms for making the parameters needed for PID applied to interconnected thermal and hydraulic power systems at best use and most effective. Two-areas of hydraulic and thermal power systems with wind connected parallel to each one are considered to exemplify the effective parameter investigation. First hydraulic and thermal are connected with tie line with the wind connected parallel to hydraulic or thermal, and then disturbance was made at thermal power plant, then to hydraulic power plant. Simulations are performed aided by the integrated Simulink/Matlab environment taking into consideration the genetic optimization process. Multiple integral representations variables with different cost functions were considered in the search for the effective AGC parameters. The outcomes established by this paper shows the impact of the genetic algorithms for LFC about multiple areas connected power systems based on different wind power using in the tuning of such a process.

Highlights

  • Wind energy is the fastest growing and the most widely utilized renewable energy source for the purpose of electric energy descents

  • The outcomes established by this paper shows the impact of the genetic algorithms for LFC about multiple areas connected power systems based on different wind power using in the tuning of such a process

  • This paper present work achieved by GRMOGA algorithm to solve the LFC problem of two area interconnected power system

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Summary

Introduction

Wind energy is the fastest growing and the most widely utilized renewable energy source for the purpose of electric energy descents. Based on the kinetic energy extract from DFIG wind turbines, there are some research works that have been reported for load frequency control [7] [8] [9]. The optimum adjustment of the LFCs used in connected hydraulic-power system investigated with the genetic optimization algorithms [10], and a set of performance indices which are various functions of error and time [11]. In this way, the various performances that the power system might have can be observed when different performance parameters were used

DFIG-Based Wind Turbine
Cost Functions
Simulation Result and Discussion
Disturbance Area 1
Disturbance Area 2
Conclusion
Full Text
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