| Trial Project |
PV Investigator – AI tool for Automatic Fault Detection and Diagnosis (AFDD) of photovoltaic (PV) systems
(REF: P-0310) |
| Matched I&T Wish |
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| Matched I&T Solution |
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| Solution Feature |
- Python-based self-adaptive AFDD model for PV site energy output prediction that is web-embedded and is designed to accept the large amount of existing EMSD and other 3rd parties solar plant’s historical data for AI training
- The resulting AFDD model will greatly ease the needs of typically years of on-site data feeding and allow immediate satisfactory energy output prediction accuracy for a given PV site with only limited target data
- Detect likely-failing PV site equipment by using the AFDD’s automatic compound-fault detection and diagnosis capabilities
- Able to combine available PV panel DC outputs and inverter fault code data to further enrich the details of AFDD’s fault prediction, e.g. able to tell which PV string is ageing earlier than others
- Ability to continuously increase the AFDD’s self-adaptiveness and generalization level, by allowing AI training with self and 3rd party PV site data. This greatly eases specific AI-model fine-tuning and huge development costs when new sites are added
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| Trial Information |
| Trial Site |
: |
Rooftop of school |
| Trial Scale |
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3 Pilot Sites |
| Trial Duration |
: |
May 2024 to Jun 2025 |
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| Additional Solution Information |
PV Investigator - AFDD - Trial Application and Expected Outcome - v2.pptx
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| Final Report |
EMSD-M&V-P0310-W0509-S1605_Final.pdf
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| Info on I&T Solution Provider |
| Solution Provider | : | GDEPRI Power Control Systems & Equipment (HK) Limited | | Address | : | Room 1909, 19/F., Tai Yau Building, 181 Johnston Road, Wanchai, Hong Kong. | | Contact Person | : | Stanley K W Leung |
| Position | : | General Manager | | Tel | : | 94628258 | | Email | : |
stanley.kw.leung@gdepri.com | | Webpage | : | www.gdepri.com |
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