A Sparse and Low Rank Penalized Signal Decomposition Model with Constraints: Anomaly Detection in PV Systems

Conference · 05 June 2022 · 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC)

DOI:https://doi.org/10.1109/pvsc48317.2022.9938821· OSTI ID:1960135

Yang, Wei ^[1]; Fregosi, Daniel ^[2]; Bolen, Michael ^[2]; Paynabar, Kamran ^[3]

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1. Industrial and Systems Engineering, Georgia Institute of Technology,Atlanta,GA,USA,30332; EPRI
2. Electric Power Research Institute (EPRI),Palo Alto,CA,USA,94304
3. Industrial and Systems Engineering, Georgia Institute of Technology,Atlanta,GA,USA,30332

Recently, robust PCA has seen its wide application in various industries for its ability to perform the task of anomaly detection. The essence of robust PCA approach is to break down the signal into a low rank component and sparse component. In many applications, a simple breakdown of the signal without accounting for the signs of low rank components and sparse components would violate the physical constraints of the decomposed signal. In addition, often times, the signals in the real world collected for a long duration has smooth changes within a day and between days. As an example, the power signals collected in a photovoltaic (PV) system are cyclostationary, exhibiting these characteristics. Neglecting the smoothness of signals would result in miss detection of anomalous signals which are smooth within a day but non-smooth between days and vice versa. In this paper, we developed a signal decomposition approach for the purpose of anomaly detection based on the idea of low rank and sparse decomposition taking into consideration the signs of the decomposed low rank and sparse components and the within-day and between-day smooth changes in the original signals. The proposed unsupervised approach for fault detection eliminates the need for faulty samples required by other machine learning methods. It does not require the full I-V characteristics to work. Furthermore, there is no need for complex modelling of PV systems as in the case of power loss analysis. Using Monte Carlo simulations, we demonstrate the ability of our proposed approach for detecting anomalies of different duration and severity in PV systems.

• Research Organization: Electric Power Research Institute
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: EE0008976
• OSTI ID: 1960135
• Report Number(s): DOE-EPRI-8976-5
• Resource Type: Conference paper
• Conference Information: Journal Name: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC)
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
Anomaly detection
PV systems
signal de-composition
low rank and sparse decomposition
constrained optimization