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A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis

A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis

Ahmad Mozaffari, Mehdi Emami, Nasser L. Azad, Alireza Fathi
Copyright: © 2015 |Volume: 6 |Issue: 1 |Pages: 29
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466677494|DOI: 10.4018/IJAEC.2015010101
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MLA

Mozaffari, Ahmad, et al. "A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis." IJAEC vol.6, no.1 2015: pp.1-29. http://doi.org/10.4018/IJAEC.2015010101

APA

Mozaffari, A., Emami, M., Azad, N. L., & Fathi, A. (2015). A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis. International Journal of Applied Evolutionary Computation (IJAEC), 6(1), 1-29. http://doi.org/10.4018/IJAEC.2015010101

Chicago

Mozaffari, Ahmad, et al. "A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant: A Comparative/Conceptual Analysis," International Journal of Applied Evolutionary Computation (IJAEC) 6, no.1: 1-29. http://doi.org/10.4018/IJAEC.2015010101

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Abstract

Metaheuristic techniques have successfully contributed to the development and optimization of large-scale distributed power systems. The archived literature demonstrate that the modification or tuning of the parameters of specific metaheuristics can provide powerful tools suited for optimization of power plants with different types of constraints. In spite of the high potential of metaheuristics in dealing with such systems, most of the conducted researches only address the optimization of the electrical aspects of power systems. In this research, the authors intend to attest the applicability of metaheuristics for optimizing the mechanical aspects of a real-world large-scale power plant, i.e. Neka power plant sited in Mazandaran, Iran. To do so, firstly, based on the laws of thermodynamics and the physics of the problem at hand, the authors implement a mathematical model to calculate the values of exergetic efficiency, energetic efficiency, and total cost of the Neka power plant as three main objective functions. Besides, a memetic supervised neural network and Bahadori's mathematical model are used to calculate the dynamic values of specific heat over the operating procedure of the power plant. At the second stage, a modified version of a recent spotlighted Pareto based multiobjective metaheuristic called synchronous self-learning Pareto strategy (SSLPS) is proposed. The proposed technique is based on embedding logistic chaotic map into the algorithmic architecture of SSLPS. In this context, the resulting optimizer, i.e. chaos-enhanced SSLPS (C-SSLPS), uses the response of time-discrete nonlinear logistic map to update the positions of heuristic agents over the optimization procedure. For the sake of comparison, strength Pareto evolutionary algorithm (SPEA 2), non-dominated sorting genetic algorithm (NSGA-II) and standard SSLPS are taken into account. The results of the numerical study confirm the superiority of the proposed technique as compared to the other rival optimizers. Besides, it is observed that metaheuristics can be successfully used for optimizing the mechanical/energetic parameters of Neka power plant.

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