Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

Seshapalli Sairam, Subathra Seshadhri, Giancarlo Marafioti, Seshadhri Srinivasan, Geir Mathisen and Korkut Bekiroglu

Renewable Energy, 2022, vol. 185, issue C, 1425-1440

Abstract: This paper presents an eXplainable Fault Detection Systems (XFDS) for incipient faults in PV panels. The XFDS is realizable on simple edge devices and has four main components: (i) irradiance-based three diode model (IB3DM), (ii) data-based fault classifier, (iii) eXplainable Artificial Intelligence (XAI) application, and (iv) edge node implementing XFDS. The IB3DM is a high fidelity model having nine parameters, modeling the PV panel behavior even at low irradiance conditions, thereby helping incipient fault detection. The fault-classifier uses an Extreme gradient boosting (XGBoost) classifier to classify faults using PV panels’ data streams. Nevertheless, both IB3DM and XGBoost lack the explainability required for the field personnel to understand the fault causes. To achieve this, the IB3DM and XGBoost are used as a base model to build an XAI application using the Local interpretable model-agnostic explanations (LIME) framework. By fusing the XGBoost with XAI, the low amplitude fault signals and their intermittent nature are handled. The edge node implements the XFDS using compact sensors and user interfaces that explain the faults to the field technicians. The proposed XFDS is illustrated using deployment and simulation studies on different PV technologies to demonstrate model accuracy, incipient fault detection, fault explanations, and human augmentation to the system.

Keywords: Explainable Artificial Intelligence (XAI); Edge computing; Fault detection systems; Photovoltaic (PV) panels (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:185:y:2022:i:c:p:1425-1440

DOI: 10.1016/j.renene.2021.10.063

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