Systematic analysis and multi-objective optimization of integrated power generation cycle for a thermal power plant using Genetic algorithm

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

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Power generation from fluegas waste heat in a 500 MWe subcritical coal-fired thermal power plant using solar assisted Kalina Cycle System 11

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2018.03.096 ◽

2018 ◽

Vol 138 ◽

pp. 235-245 ◽

Cited By ~ 7

Author(s):

Goutam Khankari ◽

Sujit Karmakar

Keyword(s):

Power Generation ◽

Power Plant ◽

Thermal Power Plant ◽

Waste Heat ◽

Thermal Power ◽

Kalina Cycle ◽

Cycle System

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RELIABILITY BASED OPTIMIZATION OF TECHNICAL SPECIFICATION OF FRONTLINE SYSTEMS OF NUCLEAR POWER PLANTS USING MULTI-OBJECTIVE APPROACH

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539312500027 ◽

2012 ◽

Vol 19 (01) ◽

pp. 1250002

Author(s):

G. SRINIVAS ◽

A. K. VERMA ◽

A. SRIVIDYA ◽

SANJAY KUMAR KHATTRI

Keyword(s):

Genetic Algorithm ◽

Power Plant ◽

Nuclear Power Plant ◽

Nuclear Power ◽

Technical Specification ◽

Multi Objective Optimization ◽

Potential Core ◽

Multi Objective ◽

Genetic Algorithm Approach ◽

Technical Specifications

Technical Specifications define the limiting conditions of operation, maintenance and surveillance test requirements for the various Nuclear Power plant systems in order to meet the safety requirements to fulfill regulatory criteria. These specifications impact even the economics of the plant. The regulatory approach addresses only the safety criteria, while the plant operators would like to balance the cost criteria too. The attempt to optimize both the conflicting requirements presents a case to use Multi-objective optimization. Evolutionary algorithms (EAs) mimic natural evolutionary principles to constitute search and optimization procedures. Genetic algorithms are a particular class of EA's that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection and recombination (or cross-over). In this paper we have used the plant insights obtained through a detailed Probabilistic Safety Assessment with the Genetic Algorithm approach for Multi-objective optimization of Surveillance test intervals. The optimization of Technical Specifications of three front line systems is performed using the Genetic Algorithm Approach. The selection of these systems is based on their importance to the mitigation of possible accident sequences which are significant to potential core damage of the nuclear power plant.

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