{"title":"Compendium of Computational Tools for Power Systems Harmonic Analysis","authors":"A. Amoo, U. Aliyu, G. Bakare","doi":"10.5772/INTECHOPEN.77182","DOIUrl":null,"url":null,"abstract":"Harmonic analysis comes into limelight at this contemporary world as a result of pro- liferation of non-linear loads producing waveform distortions in power systems. It has apparently outshined other important phrases such as power outage, power factor and so on which are known for their devastating impacts. The emergence of distorted waveform has adverse effects which could be slow or rapid damage of key apparatus and equipment, namely power transformers, electric motors and other sensitive computer as well as communication facilities. In fact, it is very easy to assess the menace of power outage or power factor since both the utility and consumers keep watchdog on their bill -ings/operating costs in case of power factor or the economic losses when there is outage. Unfortunately, the detection of harmonics could only be analysed using high-tech power systems harmonic analysers and there is a need to provide stakeholders in the industry compendium of computational tools for fast harmonic analysis. Thus, the harmonic data acquired were used to train an artificial neural network (ANN) implemented on MATrix LABoratory (MATLAB 8) software platform to facilitate accurate prediction of harmonic distortions.","PeriodicalId":313869,"journal":{"name":"Compendium of New Techniques in Harmonic Analysis","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Compendium of New Techniques in Harmonic Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.77182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Harmonic analysis comes into limelight at this contemporary world as a result of pro- liferation of non-linear loads producing waveform distortions in power systems. It has apparently outshined other important phrases such as power outage, power factor and so on which are known for their devastating impacts. The emergence of distorted waveform has adverse effects which could be slow or rapid damage of key apparatus and equipment, namely power transformers, electric motors and other sensitive computer as well as communication facilities. In fact, it is very easy to assess the menace of power outage or power factor since both the utility and consumers keep watchdog on their bill -ings/operating costs in case of power factor or the economic losses when there is outage. Unfortunately, the detection of harmonics could only be analysed using high-tech power systems harmonic analysers and there is a need to provide stakeholders in the industry compendium of computational tools for fast harmonic analysis. Thus, the harmonic data acquired were used to train an artificial neural network (ANN) implemented on MATrix LABoratory (MATLAB 8) software platform to facilitate accurate prediction of harmonic distortions.