An unsupervised kernel norm monitoring for fault detection in a time series photovoltaic system

Grid-connected photovoltaic systems (GCPVS) are generally robust but remain susceptible to faults that can compromise energy conversion efficiency or raise safety concerns.

Promptly and automatically detecting such anomalies is therefore essential for maintaining system reliability and performance.

However, in practice, labeled fault data are rarely available in real-world deployments, which limits the applicability of supervised approaches.

Conventional unsupervised baseline models, including a one-class support vector machine (OCSVM), isolation forest (iForest), and local outlier factor (LOF), are trained on normal operation data and assign anomaly scores reflecting how closely new observations resemble that baseline.

Although these methods already accommodate non-linear behavior to varying degrees, kernel-based formulations offer further flexibility in shaping the decision boundary; however, tuning the kernel hyperparameters ordinarily requires some prior knowledge of the fault regime.

We overcome this limitation by proposing kernel-based norm monitoring (KNM), a non-linear, unsupervised, window-based fault-detection method designed for continuous processes.

Although the paper focuses on the GCPVS as a case study, KNM is a general-purpose monitoring framework applicable to a wide range of industrial processes.

Using the Grid-connected PV System Faults (GPVS-Faults) dataset operating in intermediate power point tracking (IPPT) mode, KNM is evaluated in two fault scenarios, sensor faults and partial shading, against three benchmark techniques: OCSVM, iForest, and LOF.

KNM achieves up to 99.1% and 98.3% accuracy on the two fault scenarios, respectively, using the Cauchy kernel, compared to 93.5% for the best-performing benchmark.

The method is interpretable, and variable contribution plots are proposed to support fault identification.