Abstract
Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.
Highlights
Photovoltaic (PV)-based electrical power generation has been a growing research area in the academia and industry fields [1], [2], where the grid-connected PV systems have witnessed the highest growth rate
The recurrent neural network (RNN)-based fault detection and diagnosis (FDD) has the following advantages: (1) The inputs of the RNN are time-series data and the depth depends on the length of the input sequence, which is suitable for dynamic PV systems monitoring and prediction; (2) RNN are Turing complete, the chain connection mode is conducive to the extraction and representation of the dynamic nonlinear characteristics of PV systems; (3) The RNN is stable when the length of the learning and testing sequence are different (PV system control is often of variable length and the sampling is irregular)
In the present review paper, the deep learning (DL)-based FDD have been classified into five categories: FDD based on convolutional neural network (CNN), FDD based on recurrent neural network (RNN), FDD based on stacked auto encoder network (SAEN), FDD based on deep belief network (DBN) and FDD based on deep transfer learning (DTL), where their main advantages and drawbacks were indicated
Summary
Photovoltaic (PV)-based electrical power generation has been a growing research area in the academia and industry fields [1], [2], where the grid-connected PV systems have witnessed the highest growth rate. Combining multivariate statistical analysis (such as: PCA [15]–[17], kernel PCA [18]–[20]), signal processing (such as: Fourier transform, multiscale representation [13], [21], interval-valued data representation [22], [23]), and other tools with DL models could improve the performance of the FDD and decision-making accuracy It could reduce the impact of noise, outliers [24], and uncertainties and estimate the severity of the fault location. The DL-based FDD is mainly divided into three kinds of techniques [25]–[28]: (i) Data preprocessing (DP) → Features Extraction and Selection (FES) → Faults classification (FC) based on DL (FC-DL): This type of method utilizes traditional statistical analysis, signal analysis and other methods for data preprocessing and FES, and applies the DL tool for FC This allows to reduce the model complexity and improve the diagnosis rate. The following section presents the most occurring failures in PV systems
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