{"title":"基于Bootstrap MCDM方法的水电厂效率评价指标排序","authors":"Priyanka Majumder, A. K. Saha","doi":"10.4018/IJEOE.2019070104","DOIUrl":null,"url":null,"abstract":"The overall commitment of hydropower plants (HPP) in providing the interest for power is 1106 TWh. The issue with hydropower lies with the way that its proficiency relies upon numerous indicators which are elements of climatic, pressure driven and financial markets. Every one of these indicators again rely on pressure driven misfortune forced because of the time being used, change in energy requirements, locational interference and quality of the machine installed. As there are numerous indicators having diverse levels of impact on the execution productivity of HPP, a few indicators are exaggerated and some others stay under appraised which brings about incorrect basic leadership. The present study proposes another cross breed show in view of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Hierarchy Process (AHP). Also in the present investigation rank of each indicator determine by Statistical Process Control (SPC). The needs are dictated by hybrid technique in particular SPC-DEMATEL-AHP. As per the outcomes, effectiveness of turbine is the most noteworthy for impacting general productivity of HPP.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/IJEOE.2019070104","citationCount":"4","resultStr":"{\"title\":\"Ranking of Indicators for Estimation of Plant Efficiency in Hydropower Plants by a Bootstrap MCDM Approach\",\"authors\":\"Priyanka Majumder, A. K. Saha\",\"doi\":\"10.4018/IJEOE.2019070104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The overall commitment of hydropower plants (HPP) in providing the interest for power is 1106 TWh. The issue with hydropower lies with the way that its proficiency relies upon numerous indicators which are elements of climatic, pressure driven and financial markets. Every one of these indicators again rely on pressure driven misfortune forced because of the time being used, change in energy requirements, locational interference and quality of the machine installed. As there are numerous indicators having diverse levels of impact on the execution productivity of HPP, a few indicators are exaggerated and some others stay under appraised which brings about incorrect basic leadership. The present study proposes another cross breed show in view of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Hierarchy Process (AHP). Also in the present investigation rank of each indicator determine by Statistical Process Control (SPC). The needs are dictated by hybrid technique in particular SPC-DEMATEL-AHP. As per the outcomes, effectiveness of turbine is the most noteworthy for impacting general productivity of HPP.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4018/IJEOE.2019070104\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJEOE.2019070104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJEOE.2019070104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ranking of Indicators for Estimation of Plant Efficiency in Hydropower Plants by a Bootstrap MCDM Approach
The overall commitment of hydropower plants (HPP) in providing the interest for power is 1106 TWh. The issue with hydropower lies with the way that its proficiency relies upon numerous indicators which are elements of climatic, pressure driven and financial markets. Every one of these indicators again rely on pressure driven misfortune forced because of the time being used, change in energy requirements, locational interference and quality of the machine installed. As there are numerous indicators having diverse levels of impact on the execution productivity of HPP, a few indicators are exaggerated and some others stay under appraised which brings about incorrect basic leadership. The present study proposes another cross breed show in view of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Hierarchy Process (AHP). Also in the present investigation rank of each indicator determine by Statistical Process Control (SPC). The needs are dictated by hybrid technique in particular SPC-DEMATEL-AHP. As per the outcomes, effectiveness of turbine is the most noteworthy for impacting general productivity of HPP.