Abstract
This article presents state-of-the-art sensing techniques used for monitoring photovoltaic (PV) plants. They are grouped into cameras, which are typically two-dimensional (2-D) cameras and non-cameras-based techniques. The sensors can be either permanently deployed, handheld by an experienced operator, or carried by unmanned aerial vehicles (UAVs). The topic is important for years to come since the actual PV plants are continuously exposed to different types of failures which can significantly affect their power efficiency, in addition to eventually causing some fatalities. This has encouraged some researchers to publish reviewer papers on different monitoring techniques of PV plants. The main contribution of this article compared to those previous works is to present state-of-the-art monitoring techniques for every type of fault. It also describes the fundamental concepts of the sensors used which is necessary to grasp their limitations and to understand the design solutions which were made with regard to satisfying some safety and accuracy constraints. For instance, the article reviews five different kinds of voltage/current sensors having different accuracy levels and suggests their adequate placement within PV plants. In addition to the recent research findings disclosed in patents, the article also presents currently commercially available smart sensors dedicated to PV monitoring, as well as the most recent findings in industry and real-field experience. This includes for instance NTT's recent work to monitor a 2-MW PV plant using drones or undetectable faults which may be caused by placing indispensable protective relays within the PV plants. Most of the current review papers were less comprehensive and they mainly focused on academic works.
Original language | British English |
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Article number | 9502016 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 72 |
DOIs | |
State | Published - 2023 |
Keywords
- Artificial intelligence (AI)
- drones
- PV plant monitoring
- sensor systems