{"title":"Time-Frequency Image Representation Aided Deep Feature Extraction-Based Grid Connected Solar PV Fault Classification Framework","authors":"Ananya Chakraborty, Ratan Mandal, Soumya Chatterjee","doi":"10.3103/S0003701X23601667","DOIUrl":null,"url":null,"abstract":"<p>Accurate detection of faults in grid connected solar PV systems is important to ensure the reliability of power systems with distributed generation. Considering the aforesaid fact, here, a smoothed pseudo-Wigner-Ville distribution (SPWVD) and stacked sparse autoencoder (SSA) based automated feature extraction technique is proposed for accurate detection of faults in grid connected solar PV systems. To this end, three phase current data of normal as well as different fault scenarios obtained from point of common coupling (PCC) were converted into direct (<i>d</i>) and quadrature (<i>q</i>) axis using extended Park’s vector approach. Then, the obtained <i>d</i>-axis (<i>I</i><sub><i>d</i></sub>) and <i>q</i>-axis (<i>I</i><sub><i>q</i></sub>) currents were converted to 2D time-frequency images using SPWVD. The converted time-frequency spectrum of the normal as well as faulty current data were used as inputs to the proposed SSA model for deep feature extraction. After extraction of deep features using SSA, analysis of variance (ANOVA) test and false discovery rate (FDR) correction was employed to select the most discriminative features. The feature selection was followed by classification using machine learning classifiers. It has been observed that the proposed technique achieved mean fault recognition accuracy of 98.79 and 97.56% for <i>d</i>-axis and <i>q</i>-axis currents respectively, respectively. The present approach can be used for accurate diagnosis of faults in grid connected solar PV systems.</p>","PeriodicalId":475,"journal":{"name":"Applied Solar Energy","volume":null,"pages":null},"PeriodicalIF":1.2040,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Solar Energy","FirstCategoryId":"1","ListUrlMain":"https://link.springer.com/article/10.3103/S0003701X23601667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate detection of faults in grid connected solar PV systems is important to ensure the reliability of power systems with distributed generation. Considering the aforesaid fact, here, a smoothed pseudo-Wigner-Ville distribution (SPWVD) and stacked sparse autoencoder (SSA) based automated feature extraction technique is proposed for accurate detection of faults in grid connected solar PV systems. To this end, three phase current data of normal as well as different fault scenarios obtained from point of common coupling (PCC) were converted into direct (d) and quadrature (q) axis using extended Park’s vector approach. Then, the obtained d-axis (Id) and q-axis (Iq) currents were converted to 2D time-frequency images using SPWVD. The converted time-frequency spectrum of the normal as well as faulty current data were used as inputs to the proposed SSA model for deep feature extraction. After extraction of deep features using SSA, analysis of variance (ANOVA) test and false discovery rate (FDR) correction was employed to select the most discriminative features. The feature selection was followed by classification using machine learning classifiers. It has been observed that the proposed technique achieved mean fault recognition accuracy of 98.79 and 97.56% for d-axis and q-axis currents respectively, respectively. The present approach can be used for accurate diagnosis of faults in grid connected solar PV systems.
期刊介绍:
Applied Solar Energy is an international peer reviewed journal covers various topics of research and development studies on solar energy conversion and use: photovoltaics, thermophotovoltaics, water heaters, passive solar heating systems, drying of agricultural production, water desalination, solar radiation condensers, operation of Big Solar Oven, combined use of solar energy and traditional energy sources, new semiconductors for solar cells and thermophotovoltaic system photocells, engines for autonomous solar stations.