Open AccessGenerating System Wellbeing Index Evaluation Using Genetic Algorithm
Author(s) -
Ahmed Alabdulwahab
Publication year - 2012
Publication title -
maǧalaẗ ǧameʼaẗ al-malīk abdul aziz. al-uʼlum al-handasiaẗ
Language(s) - English
Resource type - Journals
ISSN - 1319-1047
DOI - 10.4197/eng.23-2.3
Subject(s) - probabilistic logic , reliability (semiconductor) , computer science , genetic algorithm , task (project management) , reliability engineering , monte carlo method , index (typography) , risk analysis (engineering) , machine learning , mathematical optimization , power (physics) , artificial intelligence , engineering , mathematics , statistics , systems engineering , medicine , physics , quantum mechanics , world wide web
Reliability assessment of generation system is a crucial taskused to be done using deterministic approaches. However, due to thepractical limitations of these approaches, they have been graduallyreplaced by probabilistic techniques. Nevertheless, there is aconsiderable reluctance in many electric power utilities to completelyabandon deterministic considerations. To fulfill the industry need,wellbeing analysis has been developed to combine the deterministicand the probabilistic approaches in a single framework. Analyticaltechniques or Monte Carlo Simulation have been used to evaluatewellbeing indices. However, analytical approaches are complicatedand mathematically demanding and simulation technique requires ahuge amount of computing time, and large memory size. This stillprevents the utilities to benefit from the wellbeing framework. Thispaper presents a novel Genetic Algorithm (GA) based technique tocalculate the wellbeing indices. Hopefully, this will encourage theindustry to benefit from the wellbeing analysis. The features of theGA are utilized to collect and identify the health, marginal and at riskwellbeing states and to calculate the associated wellbeing indices. Theproposed technique is applied to the IEEE-RBTS and the resultingwellbeing indices are compared to those obtained using a conventionalanalytical technique. The results show that the outcome of bothtechniques is virtually identical. The effect of the GA parameters onthe wellbeing indices is examined. The proposed GA based techniquein the manner applied in this study is simple, practical and valid tocalculate the wellbeing indices.