Abstract
This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.
Keywords
Fault detection, Electrical grids, Machine Learning, Long Short-Term Memory (LSTM) networks, Wireless communication,Downloads
References
- W. Ding, Q. Chen, Y. Dong, N. Shao, Fault Diagnosis Method of Intelligent Substation Protection System Based on Gradient Boosting Decision Tree. Applied Sciences, 12(18), (2022) 8989. https://doi.org/10.3390/app12188989
- K. Sudharson, S. Rajalalakshmi, K.R. Mohan Raj, Dhakshunhaa moorthiy, A Trust-Based Framework for IoT Device Management Using Blockchain Technology. International Journal of Electrical and Electronics Engineering, 10(10), (2023) 32–39. https://doi.org/10.14445/23488379/IJEEE-V10I10P104
- K. Sudharson, C. Rohini, A.M. Sermakani, P. Menaga, M. Maharasi, (2023) Quantum-Resistant Wireless Intrusion Detection System using Machine Learning Techniques. In 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), IEEE, Pune, India. https://doi.org/10.1109/ICCUBEA58933.2023.10392127
- C.S. Anita, R. Sasikumar, Learning automata and lexical composition method for optimal and load balanced RPL routing in IoT. International Journal of Ad Hoc and Ubiquitous Computing, 40(4), (2022) 288-300. https://doi.org/10.1504/IJAHUC.2022.124560
- M.H.L. Louk, B.A. Tama, Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system. Expert Systems with Applications, 213, (2023) 119030. https://doi.org/10.1016/j.eswa.2022.119030
- J. Yang, Y. Sheng, J. Wang, A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System. in IEEE Access, 8, (2020) 175467-175482. https://doi.org/10.1109/ACCESS.2020.3026044
- I. Surenther, K. Sridhar, M. Kingston Roberts, Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach. Alexandria Engineering Journal, 83, (2023) 53–65. https://doi.org/10.1016/j.aej.2023.10.016
- A.E. Labrador Rivas, T. Abrão, Faults in smart grid systems: Monitoring, detection and classification. Electric Power Systems Research, 189, (2020) 106602. https://doi.org/10.1016/j.epsr.2020.106602
- H. Alshede, L. Nassef, N. Alowidi, E. Fadel, Ensemble voting-based anomaly detection for a smart grid communication infrastructure. Intelligent Automation & Soft Computing, 36(3), (2023) 3257–3278. https://doi.org/10.32604/iasc.2023.035874
- B.A. Alabsi, M. Anbar, S.D.A. Rihan, CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks. Sensors. 23(14), (2023) 6507. https://doi.org/10.3390/s23146507
- B. Konatham, T. Simra, F. Amsaad, M.I. Ibrahem, N.Z. Jhanjhi, (2024) A Secure Hybrid Deep Learning Technique for Anomaly Detection in IIoT Edge Computing. Authorea Preprints. https://doi.org/10.36227/techrxiv.170630909.96680286/v1
- J. Zhang, S.X. Ding, D. Zhang, L. Li, Distributed fault detection for large‐scale interconnected systems. IET Control Theory & Applications, (2023). https://doi.org/10.1049/cth2.12573
- S. Akhtar, M. Adeel, M. Iqbal, A. Namoun, A. Tufail, K.H. Kim, Deep learning methods utilization in electric power systems. Energy Reports, 10, (2023) 2138–2151. https://doi.org/10.1016/j.egyr.2023.09.028
- Y. Zhang, C. Liu, M. Liu, T. Liu, H. Lin, C.B. Huang, L. Ning, Attention is all you need: utilizing attention in AI-enabled drug discovery. Briefings in Bioinformatics, 25(1), (2024) bbad467. https://doi.org/10.1093/bib/bbad467
- A. Hernández, J.M. Amigó, Attention Mechanisms and Their Applications to Complex Systems. Entropy (Basel). 23(3), (2021) 283. https://doi.org/10.3390/e23030283
- M.J. Abdulaal, M.I. Ibrahem, M.M. Mahmoud, J. Khalid, A.J. Aljohani, A.H. Milyani, A.M. Abusorrah, Real-time detection of false readings in smart grid AMI using deep and ensemble learning. IEEE Access, 10, (2022) 47541-47556. https://doi.org/10.1109/ACCESS.2022.3171262
- K. Dhibi, M. Mansouri, K. Bouzrara, H. Nounou, M. Nounou, an Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems. In IEEE Access, 9, (2021) 155622-155633. https://doi.org/10.1109/ACCESS.2021.3128749
- S. Liu, Y. Sun, L. Zhang, P. Su, Fault diagnosis of shipboard medium-voltage DC power system based on machine learning. International Journal of Electrical Power & Energy Systems, 124, (2021) 106399. https://doi.org/10.1016/j.ijepes.2020.106399
- M. Ibrar, M.A. Hassan, K. Shaukat, T.M. Alam, K.S. Khurshid, I.A. Hameed, H. Aljuaid, S. Luo, A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources. Wireless Communications and Mobile Computing, (2022) 1–15. https://doi.org/10.1155/2022/2697303
- K. Sudharson, Alekhya, Badi, A Comparative Analysis of Quantum-Based Approaches for Scalable and Efficient Data Mining in Cloud Environments. Quantum Information and Computation, 23(9&10), (2023), 783-813. https://doi.org/10.26421/QIC23.9-10-3
- S. Li, Y. Han, X. Yao, S. Yingchen, J. Wang, Q. Zhao, (2019). Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering, (2019) 1–12. https://doi.org/10.1155/2019/4136874
- K. Sudharson, N.S. Usha, G. Babu, P.S. Apirajitha, S.H. Nallamala, G.M. Kumar, (2023) Hybrid Quantum Computing and Decision Tree-Based Data Mining for Improved Data Security. 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), IEEE, Pune, India. https://doi.org/10.1109/ICCUBEA58933.2023.10391989
- O.D. Okey, S.S. Maidin, P. Adasme, R. Lopes Rosa, M. Saadi, D. Carrillo Melgarejo, D. Zegarra Rodríguez, BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. Sensors. 22(19), (2022) 7409. https://doi.org/10.3390/s22197409
- L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria, M.A. Fadhel, M. Al-Amidie, L. Farhan, Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, (2021) 1-74. https://doi.org/10.1186/s40537-021-00444-8
- K. Sudharson, S.P. Panimalar, C. Ambhika, K. Vijaya, S.P. Kumar, R.K. Kovarasan, (2023) Enhanced Security Technique for Adhoc Transmission Using Hyper Elliptic Curve. 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), IEEE, Pune, India. https://doi.org/10.1109/ICCUBEA58933.2023.10392064
- Z. Qu, H. Liu, Z. Wang, J. Xu, P. Zhang, H. Zeng, A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption. Energy and Buildings, 248, (2021) 111193. https://doi.org/10.1016/j.enbuild.2021.111193
- S. Arun, K. Sudharson, DEFECT: discover and eradicate fool around node in emergency network using combinatorial techniques. Journal of Ambient Intelligence and Humanized Computing, 14(5), (2023) 5995–6006. https://doi.org/10.1007/s12652-020-02606-7
- C.S. Anita, R. Sasikumar, Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks. Wireless Personal Communications, 126, (2022) 2949–2968. https://doi.org/10.1007/s11277-022-09846-0
- M. Vedaraj, C.S. Anita, A. Muralidhar, V. Lavanya, K. Balasaranya, P. Jagadeesan, Early Prediction of Lung Cancer Using Gaussian Naive Bayes Classification Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), (2023) 838-848.
- B. Sai Mani Teja, C.S. Anita, D. Rajalakshmi, M.A. Berlin, A CNN based facial expression recognizer. Materials Today: Proceedings, 37(2), (2021) 2578-2581. https://doi.org/10.1016/j.matpr.2020.08.501 .
- C.S. Anita, R.M. Suresh, On Demand Stable Routing with Channel Allocation and Backoff Countdown Optimization in Wireless Mesh Networks. Wireless Personal Communications, 89, (2016) 1123–1145. https://doi.org/10.1007/s11277-016-3308-7
- C.S. Anita, R.M. Suresh, Improving QoS Routing in Hybrid Wireless Mesh Networks, Using Cross-Layer Interaction and MAC Scheduling. Cybernetics and Information Technologies 15(3), (2015) 52–67. https://doi.org/10.1515/cait-2015-0041
- X. Wang, Y. Wang, Z. Javaheri, L. Almutairi, N. Moghadamnejad, O.S. Younes, Federated deep learning for anomaly detection in the internet of things. Computers and Electrical Engineering, 108, (2023) 108651. https://doi.org/10.1016/j.compeleceng.2023.108651