Work

Detection and Clustering of Photovoltaic Faults

Year: 2025

Type: conference-paper

Abstract: Photovoltaic (PV) systems are susceptible to faults that diminish energy yield and efficiency. This paper presents a novel framework for detecting and clustering PV faults using accessible data: maximum power point voltage and current from the inverter's DC side, plus weather time series. Fault detection uses a degradation-adaptive model to estimate expected outputs, flagging anomalies via dynamic irradiance-based thresholds and performance filters. Fault impacts are quantified with current and voltage performance indices (CPI and VPI), serving as clustering features. Among tested algorithms (K-means, FCM, DBSCAN, HDBSCAN), HDBSCAN excels in robustness to noise on simulated data, grouping faults by system effects.

Source: EU PVSEC

DOI: 10.4229/eupvsec2025/4cv.1.50

Authors: Kladas, Anastasios, Herteleer, Bert, Cappelle, Jan

Language: English

Topic: Photovoltaic System Optimization Techniques

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: green

OpenAlex ID: W7106340386

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