Hybrid ITLBO-DE Optimized Fuzzy PI Controller for Multi-area Automatic Generation Control with Generation Rate Constrain
The paper projects the gains of a fuzzy controller with its parameter being tuned by the hybrid improved teaching learning based optimization and differential evolution (hITLBO-DE). The foremost apprehension with the operation of AGC is satisfying equival
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Keywords Automatic generation control (AGC) Fuzzy PI controller Hybrid improved teaching learning based optimization and differential evolution (hITLBO-DE)
A. Behera (&) T. K. Panigrahi A. K. Sahoo P. K. Ray Department of Electrical and Electronics Engineering, International Institute of Information Technology Bhubaneswar (IIIT BBSR), Bhubaneswar, India e-mail: [email protected] T. K. Panigrahi e-mail: [email protected] A. K. Sahoo e-mail: [email protected] P. K. Ray e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S. C. Satapathy et al. (eds.), Smart Computing and Informatics, Smart Innovation, Systems and Technologies 77, https://doi.org/10.1007/978-981-10-5544-7_70
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1 Introduction In current scenario power system automatic generation control (AGC) introduced a vital role to sustain the steadiness between generation and demand of load with reducing the variation of frequency [1, 2]. Tie-line power exchange is used here to regulate and facilitate contracts between covering all control areas for confirming reliable and quality process of the interconnected transmission system. AGC observes and controls frequency of the system and tie-line power flows, try to match demand variation and required generation considering average time of the area control error (ACE) with short value. Various control and optimization processes have been suggested for AGC of power system in the last decade such as genetic algorithm (GA) and nature-inspired techniques such as particle swarm optimization (PSO), pattern search (PS), bacteria foraging optimization algorithm (BFOA), teaching learning based optimization (TLBO), improved teaching learning based optimization (ITLBO), differential evolution (DE) and fuzzy logic controller (FLC). Fuzzy logic controller (FLC) is used to increase the ability of PI controller which is able to operate even with the alterations in functional point. It applies a dynamic modification of the parameters involved with the functioning of the controller. Fuzzy logic centred PI controller was used effectively for nonlinear system using precise mathematical formulation to choose suitable fuzzy parameters (i.e. rule base, input, membership functions, scaling factors, etc.). Certain pragmatic rules are applied for selection of fuzzy parameters. Proper choice of input and output scaling factors has been taken so as not to affect FLC performance. The performance of AGC depends on the heuristic techniques as well as the controller structure. The adequate regulation by the applied technique for exact factors can escalate computational work or produce results with optimum values. Any algorithm-specific parameter is not required by TLBO but only involves mutual monitoring parameters (i.e. size of the population and generators number), which are common in running any population-based optimization algorithms. Certain developments of elementary TLBO technique are presented, which improve its search and exploitation capacities.
2 System Specification and De
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