{"title":"具有资产绩效管理分析经验,为输配电资产提供决策支持","authors":"Ravinder Negi","doi":"10.1109/APPEEC45492.2019.8994622","DOIUrl":null,"url":null,"abstract":"As a response to the continuous pressure for reducing costs while maintaining or even improving the reliability of the network, the Asset Performance Management process (APM) brings concrete solutions by optimizing the maintenance and replacement decisions on assets.At the strategic level, there is usually a fair understanding that smarter maintenance and asset management strategies can generate significant added value. However, moving from the “time-based” to “condition-based” and “reliability centered” maintenance requires a systematic approach on asset decisions using asset condition data and asset criticality.Thanks to the recent advance in various data collection techniques, analytics and data science, the tools and methodology to build advanced analytics and support effectively the decisions are now available. With those analytics, long term asset replacement perspective and shorter-term interventions are now based on concrete figures. Links to the OPEX & CAPEX financials are also done so that the user can set the right priorities for the network.The paper presents the return on experience from the implementation of an APM decision support system on Transmission Assets, compliant with ISO 55000 recommendations.Finally, the paper shows that such APM methodology and solutions can generate significant Reliability improvements with an excellent Return on Investment.","PeriodicalId":241317,"journal":{"name":"2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Experience in Asset Performance Management Analytics for decision support on Transmission & Distribution Assets\",\"authors\":\"Ravinder Negi\",\"doi\":\"10.1109/APPEEC45492.2019.8994622\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a response to the continuous pressure for reducing costs while maintaining or even improving the reliability of the network, the Asset Performance Management process (APM) brings concrete solutions by optimizing the maintenance and replacement decisions on assets.At the strategic level, there is usually a fair understanding that smarter maintenance and asset management strategies can generate significant added value. However, moving from the “time-based” to “condition-based” and “reliability centered” maintenance requires a systematic approach on asset decisions using asset condition data and asset criticality.Thanks to the recent advance in various data collection techniques, analytics and data science, the tools and methodology to build advanced analytics and support effectively the decisions are now available. With those analytics, long term asset replacement perspective and shorter-term interventions are now based on concrete figures. Links to the OPEX & CAPEX financials are also done so that the user can set the right priorities for the network.The paper presents the return on experience from the implementation of an APM decision support system on Transmission Assets, compliant with ISO 55000 recommendations.Finally, the paper shows that such APM methodology and solutions can generate significant Reliability improvements with an excellent Return on Investment.\",\"PeriodicalId\":241317,\"journal\":{\"name\":\"2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APPEEC45492.2019.8994622\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APPEEC45492.2019.8994622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Experience in Asset Performance Management Analytics for decision support on Transmission & Distribution Assets
As a response to the continuous pressure for reducing costs while maintaining or even improving the reliability of the network, the Asset Performance Management process (APM) brings concrete solutions by optimizing the maintenance and replacement decisions on assets.At the strategic level, there is usually a fair understanding that smarter maintenance and asset management strategies can generate significant added value. However, moving from the “time-based” to “condition-based” and “reliability centered” maintenance requires a systematic approach on asset decisions using asset condition data and asset criticality.Thanks to the recent advance in various data collection techniques, analytics and data science, the tools and methodology to build advanced analytics and support effectively the decisions are now available. With those analytics, long term asset replacement perspective and shorter-term interventions are now based on concrete figures. Links to the OPEX & CAPEX financials are also done so that the user can set the right priorities for the network.The paper presents the return on experience from the implementation of an APM decision support system on Transmission Assets, compliant with ISO 55000 recommendations.Finally, the paper shows that such APM methodology and solutions can generate significant Reliability improvements with an excellent Return on Investment.