Advanced search
Publication Cover
Electric Power Components and Systems Latest Articles
Submit an article Journal homepage
24
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

Rajanish Kumar Kaushal1 Department of Electrical Engineering, Chandigarh University, Mohali, IndiaCorrespondence[email protected]
View further author information
,
K. Raveendra2 Department of ECE, Sri Venkateswara College of Engineering, Tirupati, IndiaView further author information
,
N. Nagabhooshanam3 Department of Mechanical Engineering, Aditya Engineering College, Surampalem, India;4 Department of Mechanical Engineering, Institute of Engineering and technology, GLA University, Mathura, India;5 Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IndiaView further author information
,
Mohammed Azam6 Electronic and Communication Engineering, ISL College of Engineering, Chandrayangutta, Hyderabad, Telangana, IndiaView further author information
,
G. Brindha7 St. Joseph’s College of Engineering, Chennai, IndiaView further author information
,
D. Anand8 Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, IndiaView further author information
,
L. Natrayan9 Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, IndiaView further author information
&
Kasukurthi Rambabu10 Department of Electrical and Electronics Engineering, Aditya Engineering College, Surampalem, IndiaView further author information
 show all
Received 09 Jul 2023, Accepted 25 Mar 2024, Published online: 02 May 2024

References

  • T. Mansouri and S. Vadera, “A deep explainable model for fault prediction using IoT sensors,” IEEE Access, vol. 10, pp. 66933–66942, 2022. DOI: 10.1109/ACCESS.2022.3184693.
  • E. Borandag, “Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques,” APPl. Sciences, vol. 13, no. 3, pp. 1639, 2023. DOI: 10.3390/app13031639.
  • N. A. Razali, et al., “Political security threat prediction framework using hybrid lexicon-based approach and machine learning technique,” IEEE Access, vol. 11, pp. 17151–17164, 2023. DOI: 10.1109/ACCESS.2023.3246162.
  • B. Aslam, A. Maqsoom, A. H. Cheema, F. Ullah, A. Alharbi and M. Imran, “Water quality management using hybrid machine learning and data mining algorithms: an indexing approach,” IEEE Access, vol. 10, pp. 119692–119705, 2022. DOI: 10.1109/ACCESS.2022.3221430.
  • P. Kainthura and N. Sharma, “Hybrid machine learning approach for landslide prediction, Uttarakhand, India,” Sci Rep, vol. 12, no. 1, pp. 20101, 2022. DOI: 10.1038/s41598-022-22814-9.
  • H. A. Raja, et al., “Signal spectrum-based machine learning approach for fault prediction and maintenance of electrical machines,” Energies, vol. 15, no. 24, pp. 9507, 2022. DOI: 10.3390/en15249507.
  • V. V. R. M, A. Chaparala, P. K. Jain, H. Karamti and W. Karamti, “Monitor the strength status of buildings using hybrid machine learning technique,” IEEE Access, vol. 11, pp. 26441–26458, 2023. DOI: 10.1109/ACCESS.2023.3247499.
  • S. Walther and A. Fuerst, “Reduced data volumes through hybrid machine learning compared to conventional machine learning demonstrated on bearing fault classification,” Appl. Sciences, vol. 12, no. 5, pp. 2287, 2022. DOI: 10.3390/app12052287.
  • J. Parmar, S. Patel and V. Katkar, “Graphene-based metasurface solar absorber design with absorption prediction using machine learning,” Sci Rep, vol. 12, no. 1, pp. 2609, 2022. DOI: 10.1038/s41598-022-06687-6.
  • P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar and R. Ayyasamy, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electric Power Components Syst., vol. 52, no. 2, pp. 219–232, 2023. DOI: 10.1080/15325008.2023.2217175.
  • H. Shekhar, et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electric Power Components Syst., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
  • P. Chaturvedi, S. Mishra and S. Agrawal, “Credit card fraud detection using hybrid machine learning algorithms,” IJRASET, vol. 10, no. 4, pp. 463–472, 2022. DOI: 10.22214/ijraset.2022.47028.
  • R. Khalid, A. Ullah, A. Khan, A. Khan, and M. H Inayat, “Comparison of standalone and hybrid machine learning models for prediction of critical heat flux in vertical tubes,” Energies, vol. 16, no. 7, pp. 3182, 2023. DOI: 10.3390/en16073182.
  • M. Alrifaey, et al., “Hybrid deep learning model for fault detection and classification of grid-connected photovoltaic system,” IEEE Access, vol. 10, pp. 13852–13869, 2022. DOI: 10.1109/ACCESS.2022.3140287.
  • M. A. Mahmoud, N. R. Md Nasir, M. Gurunathan, P. Raj and S. A. Mostafa, “The current state of the art in research on predictive maintenance in smart grid distribution network: fault’s types, causes, and prediction methods—A systematic review,” Energies, vol. 14, no. 16, pp. 5078, 2021. DOI: 10.3390/en14165078.
  • M. S. Ibrahim, W. Dong and Q. Yang, “Machine learning driven smart electric power systems: current trends and new perspectives,” APPl. Energy, vol. 272, pp. 115237, 2020. DOI: 10.1016/j.apenergy.2020.115237.
  • L. Li, Z. Liu, Y. Lu, F. Wang and S. Jeon, “Hard-rock TBM thrust prediction using an improved two-hidden-layer extreme learning machine,” IEEE Access, vol. 10, pp. 112695–112712, 2022. DOI: 10.1109/ACCESS.2022.3216294.
  • V. Rupapara, F. Rustam, W. Aljedaani, H. F. Shahzad, E. Lee and I. Ashraf, “Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model,” Sci Rep, vol. 12, no. 1, pp. 1000, 2022. DOI: 10.1038/s41598-022-04835-6.
  • R. Dhakal, A. Sedai, S. U. Pol, S. Parameswaran, A. Nejat and H. Moussa, “A novel hybrid method for short-term wind speed prediction based on wind probability distribution function and machine learning models,” Appl. Sciences, vol. 12, no. 18, pp. 9038, 2022. DOI: 10.3390/app12189038.
  • A. Ibrahim, F. J. Anayi, M. S. Packianather and O. A. Alomari, “New hybrid invasive weed optimization and machine learning approach for fault detection,” Energies, vol. 15, no. 4, pp. 1488, 2022. DOI: 10.3390/en15041488.
  • O. Abdusalam, A. Ibrahim, F. J. Anayi and M. S. Packianather, “New hybrid machine learning method for detecting faults in three-phase power transformers,” Energies, vol. 15, no. 11, pp. 3905, 2022. DOI: 10.3390/en15113905.
  • A. Hussein Humod Al Jlibawi, M. L. Othman, A. Ishak, B. S. Moh Noor and A. H. Sajitt, “Optimization of distribution control system in oil refinery by applying hybrid machine learning techniques,” IEEE Access, vol. 10, pp. 3890–3903, 2022. DOI: 10.1109/ACCESS.2021.3134931.
  • S. Malik, M. Tahir, M. Sardaraz and A. Alourani, “A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques,” Appl. Sciences, vol. 12, no. 4, pp. 2160, 2022. DOI: 10.3390/app12042160.
  • G. Liu, C. Wang, H. Qin, J. Fu and Q. Shen, “A novel hybrid machine learning model for wind speed probabilistic forecasting,” Energies, vol. 15, no. 19, pp. 6942, 2022. DOI: 10.3390/en15196942.
  • E. Mohammadian, M. Kheirollahi, B. Liu, M. Ostadhassan and M. Sabet, “A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran,” Sci Rep, vol. 12, no. 1, pp. 4505, 2022. DOI: 10.1038/s41598-022-08575-5.
  • D. H. K, G. N, C. K. V P, P. Naik and G. M, “Disease prediction using machine learning,” IJRASET, vol. 10, no. 7, pp. 4064–4072, 2022. DOI: 10.22214/ijraset.2022.41230.
  • V. M. Lourenço, J. O. Ogutu, R. A. Rodrigues and H. Piepho, “Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data,” 2022.
  • R. Rezaee and J. M. Ekundayo, “Permeability prediction using machine learning methods for the CO2 injectivity of the precipice sandstone in Surat Basin, Australia,” Energies, vol. 15, no. 6, pp. 2053, 2022. DOI: 10.3390/en15062053.
  • M. Walvekar, S. Gupta, M. Bhoire, S. Khan and A. A. Borse, “Analysis of hybrid machine learning algorithm for heart disease prediction,” IJRASET, vol. 11, no. 4, pp. 1432–1436, 2023. DOI: 10.22214/ijraset.2023.50373.
  • K. Takke, R. Bhaijee, A. Singh and M. A. Patil, “Medical disease prediction using machine learning algorithms,” IJRASET, vol. 10, no. 5, pp. 221–227, 2022. DOI: 10.22214/ijraset.2022.42135.
  • K. S. Jagadeesh and R. R, “Heart disease prediction using machine learning,” IJRASET, vol. 10, no. 4, pp. 940–944, 2022. DOI: 10.22214/ijraset.2022.40579.
  • S. S. Lakkakula, S. N. Maddukuri, S. Neelam and D. M. Ramasubramanian, “Covid future prediction using machine learning,” IJRASET, vol. 10, no. 8, pp. 179–183, 2022. DOI: 10.22214/ijraset.2022.46133.
  • D. Dave, S. Prajapati and U. Solanki, “Stock price prediction using machine learning,” IJRASET, vol. 10, no. 9, pp. 263–275, 2022. DOI: 10.22214/ijraset.2022.46610.
  • K. P. Kumar, A. Pravalika, P. Sheela and Y. Vishwam, “Disease prediction using machine learning algorithms KNN and CNN,” IJRASET, vol. 10, no. 5, pp. 446–450, 2022. DOI: 10.22214/ijraset.2022.42214.
  • R. Patil, P. Alandikar, V. Chaudhari, P. Patil and P. S. Deshpande, “Soil health prediction using supervised machine learning technique,” IJRASET, vol. 10, no. 12, pp. 122–128, 2022. DOI: 10.22214/ijraset.2022.40081.
  • D. W. Santos, “Breast cancer survival prediction using machine learning and gene expression profiles.” 2022. DOI: 10.1101/2022.01.22.22269470v1.
  • T. K. M and R. M. D, “Drug target interaction [DTI] and prediction using machine learning,” IJRASET, vol. 10, no. 10, pp. 116–122, 2022. DOI: 10.22214/ijraset.2022.46901.
  • L. Hussain, et al., “Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features,” APPl. Sciences, vol. 12, no. 13, pp. 6517, 2022. DOI: 10.3390/app12136517.
  • K. Dahikar, S. Dandge and D. V. Deshmukh, “Implementation of hybrid prediction model for Covid19 using machine learning,” IJRASET, vol. 10, no. 5, pp. 4390–4393, 2022. DOI: 10.22214/ijraset.2022.43386.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Supercharge Your Next Research Paper: Research and write your next paper with Jenni AI.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.