| Project/Area Number |
17K06331
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Power engineering/Power conversion/Electric machinery
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| Research Institution | Ube National College of Technology |
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Principal Investigator |
Nanno Ikuo 宇部工業高等専門学校, 機械工学科, 教授 (40647723)
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| Co-Investigator(Kenkyū-buntansha) |
濱田 俊之 宇部工業高等専門学校, 電気工学科, 准教授 (20733695)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | 太陽電池 / ホットスポット / 自動検知 / 常時監視 / 太陽光発電 / 安全性 / バイパスダイオード / 故障 / 診断 / 太陽光発電システム |
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| Outline of Final Research Achievements |
With the spread of photovoltaic power generation systems, new safety problems (serious failures) such as fire cases have occurred. A method has already been established to detect a hot spot, which is one of the causes of a fire, and a bypass diode (BPD) failure, which is the cause of the fire, by periodic inspection. However, it is complicated and there is a problem that it may be too late due to low frequency inspection once every few years. Therefore, we proposed an automatic diagnosis method based on a research funded by KAKENHI. In addition, it was clarified that dealing with all environmental conditions is a practical issue.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果の学術的意義は，異常再現モデルと診断ルールにつながる点である。今回実際の故障に即した異常系再現モデルの構築の準備が行えた。今後、実用課題が解決し、提案する異常系再現モデルが完成すれば、故障の原因究明・対策が実現でき，故障状態を見分ける新たな知見と，この知見からこれまでになかった実用的な診断ルールが創出される。このアルゴリズムが住宅用PVSの住人の生命と財産を救い、大きな社会的意義になる。
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