Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9828
Title: Investigations on the applications of intelligent controllers for voltage and frequency control in power generating systems
Researcher: Soundarrajan A
Guide(s): Sumathi S
Keywords: Genetic Algorithm
Particle Swarm Optimization
Ant Colony Optimization
Hybrid Evolutionary Algorithms
Upload Date: 11-Jul-2013
University: Anna University
Completed Date: 09/12/2011
Abstract: Power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. The control of frequency is achieved primarily through speed governor mechanism aided by Load Frequency control (LFC) for precise control. The Automatic Voltage Regulator (AVR) senses the terminal voltage and adjusts the excitation to maintain a constant terminal voltage. The performance index adopted for problem formulation is settling time, overshoot and oscillations. The primary design goal is to obtain good load disturbance response by optimally selecting the PID controller parameters. To reduce the complexity in tuning PID parameters, Evolutionary computation techniques can be used to solve a wide range of practical problems including optimization and design of PID gains. Intelligent computing techniques like Fuzzy, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Hybrid Evolutionary Algorithms (EA) have been employed to efficiently search global optimum solutions. In this research, efficient optimization algorithms are proposed to tune the optimal gains of PID controllers used for LFC and AVR of Power generating systems. As evident from the graphs and empirical results, the suggested algorithms performed well under changing loads and regulations. The simulation results are found to be satisfactory when compared for settling time, overshoot and oscillations with conventional fixed gain controllers. The combined and synergic use of information yields a promising tool in solving power system control problems that requires optimization of more parameters. Hence, the balance between electric power generation and load demand is achieved satisfactorily, so the voltage magnitude and frequency are maintained at desired level. As a result the quality and reliability in the power system is improved by the application of Evolutionary Algorithms.
Pagination: xxiii, 281p.
URI: http://hdl.handle.net/10603/9828
Appears in Departments:Faculty of Electrical and Electronics Engineering

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01_title.pdfAttached File49.62 kBAdobe PDFView/Open
02_certificates.pdf913.67 kBAdobe PDFView/Open
03_abstract.pdf15.29 kBAdobe PDFView/Open
04_acknowledgement.pdf14.05 kBAdobe PDFView/Open
05_contents.pdf59.54 kBAdobe PDFView/Open
06_chapter 1.pdf448.96 kBAdobe PDFView/Open
07_chapter 2.pdf245.25 kBAdobe PDFView/Open
08_chapter 3.pdf811.09 kBAdobe PDFView/Open
09_chapter 4.pdf479.96 kBAdobe PDFView/Open
10_chapter 5.pdf588.62 kBAdobe PDFView/Open
11_chapter 6.pdf484.09 kBAdobe PDFView/Open
12_chapter 7.pdf30.6 kBAdobe PDFView/Open
13_appendices.pdf68.65 kBAdobe PDFView/Open
14_references.pdf52.11 kBAdobe PDFView/Open
15_publications.pdf18.84 kBAdobe PDFView/Open
16_vitae.pdf13.46 kBAdobe PDFView/Open


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