Signal-to-Noise Ratio Improvement of MAMR on CoX/Pt Media

The precision of conventional PV fault diagnostic methods faces challenges due to the nonlinear output power characteristics of photovoltaic (PV) arrays and the implementation of the maximum power point tracking (MPPT) algorithm. In severe cases, it may lead to power losses and even arouse safety issues. In this study, the variation characteristics of the sequence waveforms at the moment of failure are investigated and used to develop a novel PV fault diagnostic framework. Firstly, the sequence waveforms of string voltages and currents before and after the fault occurred are collected; the normalized sequence data of voltages, currents, and powers are used as analytic data. Then, the fault feature extraction is realized via a stacked autoencoder (SAE) model. After that, an improved multi-grained cascade forest (IgcForest) is proposed to diagnose faults, e.g., line-to-line (L-L) fault, open-circuit (OC) fault, partial-shading of PV arrays, etc. The advantages of the proposed method are that the SAE method to extract features with higher recognition automatically, and the IgcForest to enhance and exploit fault features. Particularly, the proposed improvements can reduce the feature vector dimension and enhance the information connectivity between forests at all levels for further improving the accuracy of diagnoses. In addition, the validity of the proposed method is verified by numerical simulations and measured data, and the corresponding accuracy of fault diagnoses for single failure reach 99.33% and 98.61%, respectively, which are superior to traditional methods, such as softmax, support vector machines, random forest, gcForest, and daForest. Furthermore, it also has a high accuracy of 98.83% for data sets with the occurrence of multiple faults.

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“…The adjustment of the SNR value can control the intensity of the loading noise. The calculation formula [32] can be expressed as follows:…”

Section: ) Effect Of Noise Interference On Methodsmentioning

confidence: 99%

Novel PV Fault Diagnoses via SAE and Improved Multi-Grained Cascade Forest With String Voltage and Currents Measures

2020

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“…The signal-to-noise ratio (SNR) reflects the ratio of the normal signal to the noise signal [35]. The larger the SNR, the smaller the noise mixed in the normal signal.…”

Section: Analysis Of Anti-interference Abilitymentioning

confidence: 99%

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Cui

Gao

2022

Energies

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With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Considering that all the fault information of the PV module is contained in the current-voltage (I-V) curve, this pioneering study takes the I-V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been verified in this study, which is of great significance for realizing the edge diagnosis of PV fault.

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Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

et al. 2020

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Signal-to-Noise Ratio Improvement of MAMR on CoX/Pt Media

Cited by 6 publications

References 17 publications

Novel PV Fault Diagnoses via SAE and Improved Multi-Grained Cascade Forest With String Voltage and Currents Measures

Novel PV Fault Diagnoses via SAE and Improved Multi-Grained Cascade Forest With String Voltage and Currents Measures

A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

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