- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Fault Classification in Electrofusion Polyethylene Joints by Combined Machine Learning, Thermal Pulsing and IR Thermography Methods - A Comparative Study
摘要： The capability of conveniently classifying the fault types in the electrofusion joints can certainly increase the security of polyethylene gas pipelines. Therefore in the current study, we use machine learning to push the horizons of our recent thermal pulsing and IR thermography method, to identify ovality versus unalignment faults. To do so, we extend our experimental IR-thermography data bank and then apply k-means, Random Forests and GLMNet algorithms in a two stage approach. The overall classification accuracy for k-means and Random Forests were 70.37% and 84.21% respectively; GLMNet could successfully outperform the others with a classification accuracy of 93.75%.
关键词： Machine Learning,Electrofusion Polyethylene Joint,IR Thermography,Fault Classification,Thermal Pulsing NDT
更新于2025-09-23 15:21:21
-
[IEEE 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - New Delhi, India (2019.11.16-2019.11.17)] 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - Fault Identification Algorithm for Grid Connected Photovoltaic Systems using Machine Learning Techniques
摘要： The motivation and background behind the fault detection for grid connected solar power plant is presented in this paper. The major issues encountered when integrating a PV system to the grid include multi-peak phenomenon due to partial shading, regulation of circulating currents, the impact of grid impedances on PV system stability, Fault Ride-Through (FRT) Capability, and anti-islanding detection. Hence, fault detection and condition monitoring system are necessary for smooth operation. In this paper, a fault classification technique for single-phase grid connected PV systems is developed. Wavelet Transform and Neural network approaches are used for developing the fault classification algorithm. The results depicted that the developed fault detection algorithm shows a significant improvement in the classification accuracy with 98.4%.
关键词： Wavelet Transform,fault classification,fault diagnosis,Neural Network,Photovoltaic System
更新于2025-09-23 15:19:57
-
Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
摘要： Fault detection and diagnosis (FDD) in the photovoltaic (PV) array has become a challenge due to the magnitudes of the faults, the presence of maximum power point trackers, non-linear PV characteristics, and the dependence on isolation efficiency. Thus, the aim of this paper is to develop an improved FDD technique of PV systems faults. The common FDD technique generally has two main steps: feature extraction and selection, and fault classification. Multivariate feature extraction and selection is very important for multivariate statistical systems monitoring. It can reduce the dimension of modeling data and improve the monitoring accuracy. Therefore, in the proposed FDD approach, the principal component analysis (PCA) technique is used for extracting and selecting the most relevant multivariate features and the supervised machine learning (SML) classifiers are applied for faults diagnosis. The FDD performance is established via different metrics using data extracted from different operating conditions of the grid-connected photovoltaic (GCPV) system. The obtained results confirm the feasibility and effectiveness of the proposed approaches for fault detection and diagnosis.
关键词： fault classification,fault diagnosis,photovoltaic (PV) systems,feature extraction,Supervised machine learning (SML),principal component analysis (PCA)
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Development and validation of measurement techniques according to ISO/IEC 17025:2017
摘要： Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. This paper presents a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The diagnostic process involves signal processing and statistical techniques for feature extraction, data reduction for implementation in memory-constrained electronic control units, and variety of fault classification methodologies to isolate faults in the regenerative braking system. The results demonstrate that highly accurate fault diagnosis is possible with the classification methodologies. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.
关键词： inference,regenerative braking system,Automotive systems,fault classification,distance measure,multiple fault diagnosis
更新于2025-09-19 17:13:59
-
A Novel Fault Classification Approach for Photovoltaic Systems
摘要： Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.
关键词： feature extraction,radial basis function networks (RBFN),fault classification,photovoltaic system,wavelet analysis,kernels
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - Gramado, Brazil (2019.9.15-2019.9.18)] 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - A Comparison of Machine Learning-Based Methods for Fault Classification in Photovoltaic Systems
摘要： Photovoltaic (PV) energy use has been increasing lately and, being highly dependent on environmental variables, its efficiency becomes a major factor for concern. Additionally, these systems can be affected by several kinds of faults, which can lead to a severe energy loss. In this sense, this work compares different machine-learning-based methods, such as K-Nearest Neighbors (k-NN), Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), for detecting the following faults that can occur in Photovoltaic (PV) systems: Module short circuit, MPPT fault, Open Circuit, Partial Shading, and Degradation. The accuracy and computational time taken for training each classifier were compared. ANN achieved the best accuracy, with 99.65%, while being the slowest to train. The SVM achieved a similar result, with significant less training time. There is lack of discussion on the analysis and comparison of PV fault classification methods in the literature, specially with the focus on further practical applications and computational complexity. This way, those points are the main contributions of this work, along with making all simulations and codes publicly available.
关键词： Fault Detection,Fault Diagnostic,PV Systems,Fault Classification,Machine Learning
更新于2025-09-16 10:30:52
-
[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - A Fault Classification Method of Photovoltaic Array Based on Probabilistic Neural Network
摘要： The energy crisis has promoted the development of solar photovoltaic power generation systems, but during the operation of solar panels, there will be hidden troubles such as ground fault, line-to-line fault, open-circuit fault, short-circuits fault and the hot spots. This will cause serious obstacles to the power generation of photovoltaic systems. Therefore, the immediate diagnosis and elimination of the fault of the photovoltaic system is the guarantee for the stable operation of the photovoltaic system. To address these issues, this paper makes contribution in the following Three aspects: (1) Building a 4 3× PV array model based on the key points and model parameters extracted from PV array by using Matlab, an efficient feature vector of five dimensions is proposed as the input of the fault diagnosis model; (2) The probabilistic neural network (PNN) is proposed as the fault classification tools, and achieving a good classification effect by using the simulated data after normalization to classify; (3) Performing the field test and inputting the experimental data into PNN for classification, with an accuracy of 97%. Both the simulation and experimental results show that the PNN can achieve high accuracy classification, provide a more favorable premise basis for the intelligent classification of faults in photovoltaic arrays.
关键词： PV array,Fault Diagnosis,PNN,Fault classification
更新于2025-09-11 14:15:04