{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:45:14Z","timestamp":1774723514421,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).<\/jats:p>","DOI":"10.3390\/systems11050237","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T01:06:28Z","timestamp":1683594388000},"page":"237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies"],"prefix":"10.3390","volume":"11","author":[{"given":"Qais Ibrahim","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul 34310, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8028-7918","authenticated-orcid":false,"given":"Hani","family":"Attar","sequence":"additional","affiliation":[{"name":"Department of Energy Engineer, Zarqa University, Zarqa 13133, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayman","family":"Amer","sequence":"additional","affiliation":[{"name":"Department of Energy Engineer, Zarqa University, Zarqa 13133, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4388-1480","authenticated-orcid":false,"given":"Mohanad A.","family":"Deif","sequence":"additional","affiliation":[{"name":"Department of Bioelectronics, Modern University of Technology and Information (MTI) University, Cairo 11728, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed A. A.","family":"Solyman","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Ni\u015fanta\u015f\u0131 University, Istanbul 34398, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vlaminck, M., Heidbuchel, R., Philips, W., and Luong, H. (2022). Region-based CNN for anomaly detection in PV power plants using aerial imagery. Sensors, 22.","DOI":"10.3390\/s22031244"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4836","DOI":"10.1016\/j.rser.2011.07.067","article-title":"On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools","volume":"15","author":"Mendes","year":"2011","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ceglia, F., Macaluso, A., Marrasso, E., Roselli, C., and Vanoli, L. (2020). Energy, environmental, and economic analyses of geothermal polygeneration system using dynamic simulations. Energies, 13.","DOI":"10.3390\/en13184603"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1108\/17538331111117133","article-title":"City branding: A state-of-the-art review of the research domain","volume":"4","author":"Lucarelli","year":"2011","journal-title":"J. Place Manag. Dev."},{"key":"ref_5","first-page":"143","article-title":"Placing place branding: An analysis of an emerging research field in human geography","volume":"114","author":"Andersson","year":"2014","journal-title":"Geogr. Tidsskr. J. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012018","DOI":"10.1088\/1755-1315\/349\/1\/012018","article-title":"Analysis of subsidy strategy for sustainable development of environmental protection policy","volume":"349","author":"Ko","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.eng.2021.12.018","article-title":"Policy and management of carbon peaking and carbon neutrality: A literature review","volume":"14","author":"Wei","year":"2022","journal-title":"Engineering"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, Z., and Zhang, T. (2023). GBH-YOLOv5: Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5 for PV Panel Defect Detection. Electronics, 12.","DOI":"10.3390\/electronics12030561"},{"key":"ref_9","unstructured":"Lin, L.-S., Chen, Z.-Y., Wang, Y., and Jiang, L.-W. (2022). Machine Learning and Artificial Intelligence, IOS Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112466","DOI":"10.1016\/j.measurement.2023.112466","article-title":"Solar Panel Inspection Techniques and Prospects","volume":"209","author":"Meribout","year":"2023","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7741535","DOI":"10.1155\/2022\/7741535","article-title":"Utilizing artificial intelligence and lotus effect in an emerging intelligent drone for persevering solar panel efficiency","volume":"2022","author":"Almalki","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11760-021-01955-w","article-title":"Cascade neural network-based joint sampling and reconstruction for image compressed sensing","volume":"16","author":"Zeng","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111532","DOI":"10.1016\/j.rser.2021.111532","article-title":"Potential measurement techniques for photovoltaic module failure diagnosis: A review","volume":"151","author":"Rahman","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hooda, N., Azad, A.P., Kumar, P., Saurav, K., Arya, V., and Petra, M.I. (2016, January 6\u20139). PV power predictors for condition monitoring. Proceedings of the 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia.","DOI":"10.1109\/SmartGridComm.2016.7778763"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1007\/s13198-021-01544-7","article-title":"Machine learning framework for photovoltaic module defect detection with infrared images","volume":"13","author":"Kurukuru","year":"2022","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111561","DOI":"10.1016\/j.solmat.2021.111561","article-title":"Electrical Pulsed Infrared Thermography and supervised learning for PV cells defects detection","volume":"237","author":"Bu","year":"2022","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24027","DOI":"10.1007\/s11042-021-10634-4","article-title":"A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix","volume":"80","author":"Aouat","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","first-page":"1113904","article-title":"Machine learning prediction of defect types for electroluminescence images of photovoltaic panels","volume":"11139","author":"Mantel","year":"2019","journal-title":"Appl. Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jumaboev, S., Jurakuziev, D., and Lee, M. (2022). Photovoltaics plant fault detection using deep learning techniques. Remote Sens., 14.","DOI":"10.3390\/rs14153728"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lu, F., Niu, R., Zhang, Z., Guo, L., and Chen, J. (2022). A generative adversarial network-based fault detection approach for photovoltaic panel. Appl. Sci., 12.","DOI":"10.3390\/app12041789"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.psep.2020.09.068","article-title":"Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate","volume":"148","author":"Elsheikh","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101671","DOI":"10.1016\/j.csite.2021.101671","article-title":"Productivity forecasting of solar distiller integrated with evacuated tubes and external condenser using artificial intelligence model and moth-flame optimizer","volume":"28","author":"Elsheikh","year":"2021","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8439719","DOI":"10.1155\/2020\/8439719","article-title":"Short-time wind speed forecast using artificial learning-based algorithms","volume":"2020","author":"Ibrahim","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6364102","DOI":"10.1155\/2022\/6364102","article-title":"Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach","volume":"2022","author":"Deif","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110992","DOI":"10.1016\/j.rser.2021.110992","article-title":"A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids","volume":"144","author":"Aslam","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ibrahim, M., Alsheikh, A., Awaysheh, F.M., and Alshehri, M.D. (2022). Machine learning schemes for anomaly detection in solar power plants. Energies, 15.","DOI":"10.3390\/en15031082"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Branco, P., Gon\u00e7alves, F., and Costa, A.C. (2020). Tailored algorithms for anomaly detection in photovoltaic systems. Energies, 13.","DOI":"10.3390\/en13010225"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1142\/S0219622021500528","article-title":"ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates","volume":"20","author":"Deif","year":"2021","journal-title":"Int. J. Inf. Technol. Decis. Mak."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2018.05.017","article-title":"Anomaly detection and predictive maintenance for photovoltaic systems","volume":"310","author":"Leonardi","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_30","first-page":"967","article-title":"Fault detection of solar PV system using SVM and thermal image processing","volume":"10","author":"Natarajan","year":"2020","journal-title":"Int. J. Renew. Energy Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.solener.2018.12.045","article-title":"An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class support vector machine","volume":"179","author":"Harrou","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Feng, M., Bashir, N., Shenoy, P., Irwin, D., and Kosanovic, D. (2020, January 15\u201317). Sundown: Model-driven per-panel solar anomaly detection for residential arrays. Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies, Guayaquil, Ecuador.","DOI":"10.1145\/3378393.3402257"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012119","DOI":"10.1088\/1742-6596\/364\/1\/012119","article-title":"Intelligent system for a remote diagnosis of a photovoltaic solar power plant","volume":"364","author":"Roque","year":"2012","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TSTE.2018.2867009","article-title":"Hierarchical anomaly detection and multimodal classification in large-scale photovoltaic systems","volume":"10","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1080\/08839514.2019.1691839","article-title":"Anomaly detection in power generation plants using machine learning and neural networks","volume":"34","author":"Mulongo","year":"2020","journal-title":"Appl. Artif. Intell."},{"key":"ref_36","unstructured":"Benninger, M., Hofmann, M., and Liebschner, M. (2020, January 17). Online Monitoring System for Photovoltaic Systems Using Anomaly Detection with Machine Learning. Proceedings of the NEIS 2019 Conference on Sustainable Energy Supply and Energy Storage Systems, Hamburg, Germany."},{"key":"ref_37","unstructured":"Benninger, M., Hofmann, M., and Liebschner, M. (2020, January 1). Anomaly detection by comparing photovoltaic systems with machine learning methods. Proceedings of the NEIS 2020 Conference on Sustainable Energy Supply and Energy Storage Systems Hamburg, Germany."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.solener.2009.08.004","article-title":"A simple model of PV system performance and its use in fault detection","volume":"84","author":"Firth","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Balzategui, J., Eciolaza, L., and Maestro-Watson, D. (2021). Anomaly detection and automatic labeling for solar cell quality inspection based on generative adversarial network. Sensors, 21.","DOI":"10.3390\/s21134361"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1080\/00224065.2021.1948372","article-title":"Online automatic anomaly detection for photovoltaic systems using thermography imaging and low rank matrix decomposition","volume":"54","author":"Wang","year":"2022","journal-title":"J. Qual. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hempelmann, S., Feng, L., Basoglu, C., Behrens, G., Diehl, M., Friedrich, W., Brandt, S., and Pfeil, T. (August, January 15). Evaluation of unsupervised anomaly detection approaches on photovoltaic monitoring data. Proceedings of the 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, ON, Canada.","DOI":"10.1109\/PVSC45281.2020.9300481"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Iyengar, S., Lee, S., Sheldon, D., and Shenoy, P. (2018, January 20\u201322). Solarclique: Detecting anomalies in residential solar arrays. Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, Menlo Park and San Jose, CA, USA.","DOI":"10.1145\/3209811.3209860"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tsai, C.-W., Yang, C.-W., Hsu, F.-L., Tang, H.-M., Fan, N.-C., and Lin, C.-Y. (2020, January 7\u201315). Anomaly Detection Mechanism for Solar Generation using Semi-supervision Learning Model. Proceedings of the 2020 Indo--Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), Rajpura, India.","DOI":"10.1109\/Indo-TaiwanICAN48429.2020.9181310"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pereira, J., and Silveira, M. (2018, January 17\u201320). Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00207"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kosek, A.M., and Gehrke, O. (2016, January 12\u201314). Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids. Proceedings of the 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada.","DOI":"10.1109\/EPEC.2016.7771704"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rossi, B., Chren, S., Buhnova, B., and Pitner, T. (2016, January 9\u201312). Anomaly detection in smart grid data: An experience report. Proceedings of the 2016 IEEE International Conference on Systems Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844583"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Toshniwal, A., Mahesh, K., and Jayashree, R. (2020, January 7\u20139). Overview of anomaly detection techniques in machine learning. Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social Mobile, Analytics and Cloud) (I-SMAC),  Palladam, India.","DOI":"10.1109\/I-SMAC49090.2020.9243329"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Deif, M.A., Solyman, A.A.A., Alsharif, M.H., Jung, S., and Hwang, E. (2021). A hybrid multi-objective optimizer-based SVM model for enhancing numerical weather prediction: A study for the Seoul metropolitan area. Sustainability, 14.","DOI":"10.3390\/su14010296"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6548800","DOI":"10.1155\/2022\/6548800","article-title":"Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization","volume":"2022","author":"Hammam","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Deif, M.A., Hammam, R.E., Solyman, A., Alsharif, M.H., and Uthansakul, P. (2021). Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach. Sensors, 21.","DOI":"10.3390\/s21196379"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1097\/JCE.0000000000000405","article-title":"Skin lesions classification based on deep learning approach","volume":"45","author":"Deif","year":"2020","journal-title":"J. Clin. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8933","DOI":"10.3934\/mbe.2021440","article-title":"A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences","volume":"18","author":"Deif","year":"2021","journal-title":"Math. Biosci. Eng. AIMS-Press"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7614264","DOI":"10.1155\/2022\/7614264","article-title":"A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification","volume":"2022","author":"Deif","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ins.2021.11.051","article-title":"Policy iteration reinforcement learning-based control using a grey wolf optimizer algorithm","volume":"585","author":"Zamfirache","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_55","unstructured":"Kannal, A. (2022, January 30). Solar Power Generation Data. Kaggle.com. Available online: https\/\/www.kaggle.com\/anikannal\/solar-powergeneration-data."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/5\/237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:31:43Z","timestamp":1760124703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/5\/237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,8]]},"references-count":55,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["systems11050237"],"URL":"https:\/\/doi.org\/10.3390\/systems11050237","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,8]]}}}