Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems
@article{Wali2022ExplainableSM, title={Explainable Signature-based Machine Learning Approach for Identification of Faults in Grid-Connected Photovoltaic Systems}, author={Syed Abdul Wali and Irfan Ahmed Khan}, journal={2022 IEEE Texas Power and Energy Conference (TPEC)}, year={2022}, pages={1-6} }
Transformation of conventional power networks into smart grids with the heavy penetration level of renewable energy resources, particularly grid-connected Photovoltaic (PV) systems, has increased the need for efficient fault identification systems. Malfunctioning any single component in grid-connected PV systems may lead to grid instability and other serious consequences, showing that a reliable fault identification system is the utmost requirement for ensuring operational integrity. Therefore…
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SHOWING 1-10 OF 17 REFERENCES
Fault Location in Ungrounded Photovoltaic System Using Wavelets and ANN
- EngineeringIEEE Transactions on Power Delivery
- 2018
Identifying ground faults is a significant problem in ungrounded photovoltaic (PV) systems because such earth faults do not provide sufficient fault currents for their detection and location during…
A Novel Fault Classification Approach for Photovoltaic Systems
- EngineeringEnergies
- 2020
A fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems and results indicate that the developed method can effectively detect faults with low misclassification.
Fault detection and diagnosis methods for photovoltaic systems: A review
- EngineeringRenewable and Sustainable Energy Reviews
- 2018
Fault classification for Photovoltaic Modules Using Thermography and Machine Learning Techniques
- Engineering2019 International Conference on Computer and Information Sciences (ICCIS)
- 2019
Thermography and machine learning based PV module fault classification is developed and the developed algorithm depicted 93.4% training efficiency and 91.7% testing efficiency which is better when compared with the conventional classification techniques.
A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays
- Computer Science, EngineeringIEEE Access
- 2020
A novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults is presented.
Explainable AI and Random Forest Based Reliable Intrusion Detection system
- Computer Science
- 2021
A comparative analysis and series of experiments endorse the credibility of the proposed IDS framework, depicting that the integration of XAI with conventional IDS can ensure credibility, integrity, and availability of cyber-networks.
Research on Solar Photovoltaic (PV) energy conversion system: An overview
- Engineering
- 2013
An overview of the research going on in this area of solar PV energy conversion is presented, focused on aspects mentioned above, which may be useful for further investigation to improve the system.
Selecting critical features for data classification based on machine learning methods
- Computer ScienceJournal of Big Data
- 2020
This paper adopts Random Forest to select the important feature in classification and compares the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination to get the best percentage accuracy and kappa.
DeepImpact: a deep learning model for whole body vibration control using impact force monitoring
- EngineeringNeural Computing and Applications
- 2020
A novel state-of-the-art deep learning model, ‘DeepImpact,’ is designed and developed for impact force real-time monitoring during a HISLO operation, which would minimize the impact force on truck surface, which in turn would reduce the level of vibration on the operator, thus leading to a safer and healthier working environment at mining sites.