{"title":"Quality for Sustainability: SPC Analysis of Photovoltaic Modules Flash Test","authors":"R. Al-Aomar, Mohamed Al-Shehhi","doi":"10.1109/CSDE50874.2020.9411574","DOIUrl":null,"url":null,"abstract":"This paper addresses the link between quality and sustainability in engineering systems and illustrates the role of quality tools in sustaining a high-level performance. It focuses on the application of Statistical Process Control (SPC) as a quality method at a PV (Photovoltaic) power plant that is set to generate the electricity from solar panels. In terms of performance, the PV plant has to meet certain contractual requirements including the level of delivered energy during the commissioning. Such level is highly impacted by the quality of the PV modules in terms of the solar energy generated from each module (based on the result of a flash test). The aim is to test the performance of two different PV technologies (i.e., thin film and crystalline silicon panels) that are supplied to the project from two different manufacturers during installation. To this end, SPC samples are drawn from the flash test results of the 39000 module supplied to the PV project. Results are analyzed and SPC control charts are developed to identify the special causes of process variability, explore variation patterns, and take remedial actions to ensure process stability. Results showed that even if the manufacturer supplied PV modules that are within the customer specification, it is not repress that the manufacturing process is statistically in control. SPC control charts have revealed multiple special causes in the process due to high variability in the power generated from the tested modules. These causes were carefully investigated and turned into opportunities for process improvement and stability. The study has highlighted the statistical challenges of the PV manufacturing process, as adopting SPC analyses will help manufacturer/suppliers to reduce the waste and improve the overall sustainability of the PV power plant.","PeriodicalId":445708,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSDE50874.2020.9411574","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
This paper addresses the link between quality and sustainability in engineering systems and illustrates the role of quality tools in sustaining a high-level performance. It focuses on the application of Statistical Process Control (SPC) as a quality method at a PV (Photovoltaic) power plant that is set to generate the electricity from solar panels. In terms of performance, the PV plant has to meet certain contractual requirements including the level of delivered energy during the commissioning. Such level is highly impacted by the quality of the PV modules in terms of the solar energy generated from each module (based on the result of a flash test). The aim is to test the performance of two different PV technologies (i.e., thin film and crystalline silicon panels) that are supplied to the project from two different manufacturers during installation. To this end, SPC samples are drawn from the flash test results of the 39000 module supplied to the PV project. Results are analyzed and SPC control charts are developed to identify the special causes of process variability, explore variation patterns, and take remedial actions to ensure process stability. Results showed that even if the manufacturer supplied PV modules that are within the customer specification, it is not repress that the manufacturing process is statistically in control. SPC control charts have revealed multiple special causes in the process due to high variability in the power generated from the tested modules. These causes were carefully investigated and turned into opportunities for process improvement and stability. The study has highlighted the statistical challenges of the PV manufacturing process, as adopting SPC analyses will help manufacturer/suppliers to reduce the waste and improve the overall sustainability of the PV power plant.