An Infrared Images-based Hotspot Fault Detection and Mitigation Measures in Solar-pv Using Ldpqr and LC2RN-AGN

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🔬研究者:Researchers in photovoltaic systems and AI-based fault detection will find this novel algorithm useful.

🏢実務担当者:Solar farm operators can adopt the hotspot detection method to reduce downtime and improve energy yield.

<title>Abstract</title> <p>Universally, a solar photovoltaic (PV) system stands out as a leading option among the diverse range of renewable energy sources (RES). However, none of the traditional methods focused on identifying the misalignment in the Mounting Structure (MS) in the Solar Photo-Voltaic (Solar-PV). Thus, an MS alignment with hotspot detection and Mitigation Measures (MM) in solar-PV systems using Linear Dixon-Price Quadratic Regulator (LDPQR), and Log Contrastive Regularization Recurrent Neural Adaptive Gumbel Network (LC2RN-AGN) is proposed in this paper. Primarily, to optimize the PV system layout, the proposed Two-Dimensional-Exponential Gaussian Red Panda Optimization (2D-EGRPO) is used. Likewise, by using the double-bypass diodes, the Junction Box (JB) is configured. Next, the infrared images are acquired and further inputted into the pre-trained hotspot fault detection model. Initially, the noise elimination and super-resolution are done. Next, the features and temperature features are extracted and further fed into the proposed LC2RN-AGN, where the HF is obtained. Then, based on the proposed Cubic Bray-Curtis-based Penalty Quartic K-Means (CBC-PQKM), the hotspot location is identified. Then, to mitigate the hotspot effect, the heat sink is activated. Then, the mounting misalignment verification is done, followed by LDPQR-based compensation. Thus, the proposed method attained higher reliability with a peak time of 1.77s.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-9720222/v1first seen 2026-05-22 04:21:29 · last seen 2026-06-04 04:27:15