Intelligent Optimization Algorithm for Power Grid Fault Decision Based on Multi-Source Data Fusion
DOI:
https://doi.org/10.4108/ew.11592Keywords:
Power grid fault decision, Multi-source data fusion, Intelligent optimization algorithm, Logical reasoning, Improved chimpanzee optimization algorithmAbstract
Power grid fault recovery scheduling often faces challenges such as strong data heterogeneity, low fault identification efficiency, and difficulty in coordinating multiple conflicting objectives. To enhance the intelligence and practicality of fault handling, a power grid fault decision-making algorithm is proposed that integrates multi-source data modeling with intelligent optimization. On the basis of unified fusion of structured and unstructured data, the algorithm incorporates power flow calculation analysis and logical reasoning to achieve rapid fault area localization and preliminary judgment, thereby significantly improving diagnostic accuracy and timeliness. In the optimization stage, an improved chimpanzee foraging optimization algorithm is developed, integrating Hammersley sequence initialization, adaptive weighting, and somersault foraging strategies to address multi-objective coordination and accelerate convergence. Experimental results demonstrate the superiority of the proposed method on typical power grid fault datasets. In the preliminary decision-making phase, the fault identification accuracy reaches 98.7%, with an area under the curve of 0.967, indicating high classification stability. In the optimization scheduling phase, the power restoration ratio peaks at 98.7%, switching operation cost is reduced to 0.15, and fault response time is shortened to 1.1 s, showcasing strong global optimization and scheduling efficiency. The above results show that the algorithm has obvious advantages in fault diagnosis accuracy, dispatch globality and optimization stability. It not only provides a deployable and scalable intelligent optimization solution for power grid fault recovery in complex scenarios, but also provides solid technical support for improving the intelligent decision-making level and operational resilience of power systems. It has important engineering and practical significance.
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[1] Zhang L, Wang J, Liu Z. (2023) Power grid operation optimization and forecasting using a combined forecasting system. J Journal of Forecasting 42(1): 124-153. https://doi.org/10.1002/for.2888
[2] Khaleel M, Abulifa S A, Abulifa A A. (2023) Artificial intelligent techniques for identifying the cause of disturbances in the power grid. J Brilliance: Research of Artificial Intelligence 3(1): 19-31. https://doi.org/10.47709/brilliance.v3i1.2165
[3] Wang J, Gao D, Zhu S, et al. (2023) Fault diagnosis method of photovoltaic array based on support vector machine. J Energy sources, part a: recovery, utilization, and environmental effects 45(2): 5380-5395. https://doi.org/10.1080/15567036.2019.1671557
[4] Rana S. (2025) AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, digital twins, and self-healing grids. J American Journal of Advanced Technology and Engineering Solutions 1(1): 258-289. https://doi.org/10.63125/4p25x993
[5] Liu Z, Qian R, Jin X, et al. (2024) Multi-source heterogeneous data fusion technology for electric power based on big data mining. J Journal of Computational Methods in Sciences and Engineering 24(6): 3366-3380. https://doi.org/10.1177/147279782412938
[6] Wang Z, Wang S, Wang S, et al. (2025) An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data. J Journal of Intelligent Manufacturing 36(1): 441-457. https://doi.org/10.1007/s10845-023-02238-6
[7] Cheng S, Yu R, Shen A, et al. (2025) MSDF-TL: A transfer learning and physics-constrained multi-source data fusion framework for weld state prediction. J Journal of Manufacturing Processes 154(5): 504-523. https://doi.org/10.1016/j.jmapro.2025.09.070
[8] Abedi F, Ghanimi H M A, Algarni A D, et al. (2023) Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification. J Comput. Syst. Sci. Eng. 47(3): 2791-2814. https://doi.org/10.32604/csse.2023.038762
[9] Sarihaddu C K, Raaza A, Akre S. (2025) Cognitive workload detection via binary chimp optimization algorithm and machine learning. J Arabian Journal for Science and Engineering 50(21): 17605-17616. https://doi.org/10.1007/s13369-025-10034-y
[10] He H, Yang H, Zhang W, et al. (2023) Msds: A novel framework for multi-source data selection based cross-network node classification. J IEEE Transactions on Knowledge and Data Engineering 35(12): 12799-12813. https://doi.org/10.1109/TKDE.2023.3277957
[11] He D, Lao Z, Jin Z, et al. (2023) Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network. J Nonlinear Dynamics 111(16): 14901-14924. https://doi.org/10.1007/s11071-023-08638-w
[12] Guo Y, Liu R W, Qu J, et al. (2023) Asynchronous trajectory matching-based multimodal maritime data fusion for vessel traffic surveillance in inland waterways. J IEEE Transactions on Intelligent Transportation Systems 24(11): 12779-12792. https://doi.org/10.1109/TITS.2023.3285415
[13] Yang B, Ding Y, Zhu Q, et al. (2024) Implicit modelling and dynamic update of tunnel unfavourable geology based on multi-source data fusion using support vector machine. J Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 18(1): 257-274. https://doi.org/10.1080/17499518.2023.2239778
[14] Atrigna M, Buonanno A, Carli R, et al. (2023) A machine learning approach to fault prediction of power distribution grids under heatwaves. J IEEE Transactions on Industry Applications 59(4): 4835-4845. https://doi.org/10.1109/TIA.2023.3262230
[15] Branco N W, Cavalca M S M, Ovejero R G. (2024) Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems. J Electrical Engineering, 106(3): 3657-3670. https://doi.org/10.1007/s00202-023-02146-1
[16] Dwivedi A, Tajer A. (2023) GRNN-based real-time fault chain prediction. J IEEE Transactions on Power Systems 39(1): 934-946. https://doi.org/10.1109/TPWRS.2023.3258740
[17] Yuming H, Jiaohong L, Zhenguo M, et al. (2023) On combined PSO-SVM models in fault prediction of relay protection equipment. J Circuits, Systems, and Signal Processing 42(2): 875-891. https://doi.org/10.1007/s00034-022-02056-w
[18] Khan A A, Liu D S, Shaikh A A, et al. (2021) EPS-Ledger: Blockchain hyperledger sawtooth-enabled distributed power systems chain of operation and control node privacy and security. J Electronics 10(19): 2395. https://doi.org/10.3390/electronics10192395
[19] Rashid M, Li H, Javed A R, et al. (2023) Artificial intelligence and blockchain technology for secure smart grid and power distribution automation: A state-of-the-art review. J Sustainable Energy Technologies and Assessments 57: 103282. https://doi.org/10.1016/j.seta.2023.103282
[20] Ghadi Y Y, Mazhar T, Aurangzeb K, et al. (2024) Security risk models against attacks in smart grid using big data and artificial intelligence. J PeerJ Computer Science 10: e1840. https://doi.org/10.7717/peerj-cs.1840
[21] Guo P, Xiao K, Wang X, et al. (2024) Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things. J Global energy interconnection 7(1): 94-105. https://doi.org/10.1016/j.gloei.2024.01.009
[22] Ganjkhani M, Gholami A, Giraldo J, et al. (2023) Multi-source data aggregation and real-time anomaly classification and localization in power distribution systems. J IEEE Transactions on Smart Grid 15(2): 2191-2202. https://doi.org/10.1109/TSG.2023.3316548
[23] Jia Y, Sheng W, Cai Z, et al. (2025) The application of big data and artificial intelligence technology in power system state monitoring and fault diagnosis: A review. J Advances in Resources Research 5(3): 1318-1334. https://doi.org/10.50908/arr.5.3_1318
[24] Colak A M. (2024) Parameters optimized with the pattern search algorithm for fuzzy control based active power factor correction. J Electric Power Components and Systems 52(7): 1115-1128. https://doi.org/10.1080/15325008.2023.2297999
[25] Soares M B, Guimarães É C, Rodrigues D B, et al. (2025) Control strategy for voltage disturbance compensation and power factor correction with on-site short-circuit level calculation using grid-following inverter. J IET Power Electronics 18(1): e70123. https://doi.org/10.1049/pel2.70123
[26] Su T, Zhao J, Gomez-Exposito A, et al. (2025) Grid-enhancing technologies for clean energy systems. J Nature Reviews Clean Technology 1(1): 16-31. https://doi.org/10.1038/s44359-024-00001-5
[27] Ghazal T M, Hasan M K, Mokhtar U A, et al. (2025) Machine learning-based real-time outage fault detection for distribution networks in smart grid. J Energy Reports 14: 3739-3752. https://doi.org/10.1016/j.egyr.2025.10.045
[28] Li Z. (2025) Fault traceability algorithm for communication networks based on fault tree and decision tree. J International Journal of Network Security 27(1): 152-162. https://doi.org/10.6633/IJNS.202501_27(1).17
[29] Wang C, Yan M, Pang K, et al. (2023) Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication. J IEEE Transactions on Smart Grid 14(5): 3413-3426. https://doi.org/10.1109/TSG.2023.3244785
[30] Li Y, Su S, Hu F, et al. (2025) A novel fault location method for distribution networks with distributed generators based on improved seagull optimization algorithm. J Energy Reports 13(2): 3237-3245. https://doi.org/10.1016/j.egyr.2025.02.058
[31] Alhanaf A S, Farsadi M, Balik H H. (2024) Fault detection and classification in ring power system with DG penetration using hybrid CNN-LSTM. J IEEE Access 12: 59953-59975. https://doi.org/10.1109/ACCESS.2024.3394166
[32] Arunachalam S, Kumar A K V, Reddy D N, et al. (2025) Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks. J Int J Reconfigurable & Embedded Syst 14(1): 221-230. https://doi.org/10.11591/ijres.v14.i1.pp221-230
[33] Chen L F, Zhang S P, Qin Y Y, et al. (2025) Software defect prediction based on support vector machine optimized by reverse differential chimp optimization algorithm. J International Journal of Data Science and Analytics 20(5): 4423-4448. https://doi.org/10.1007/s41060-025-00726-x
[34] Saadeh M, Koutsougeras C. (2025) Analysis of decision-making algorithm to operate an industrial grid conveying system. J Journal of Intelligent Manufacturing and Special Equipment 6(2): 198-209. https://doi.org/10.1108/JIMSE-05-2025-0009
[35] Kumar V R, Jeyanthy P A. (2025) Fault Classification and Detection in Transmission Lines by Chimp optimization algorithm (ChOA) Associated support Vector Machine. J JITSI: Jurnal Ilmiah Teknologi Sistem Informasi 6(1): 86-101. https://doi.org/10.62527/jitsi.6.1.284
[36] Anka F. (2025) A multi-strategy chimp optimization algorithm for solving global and constraint engineering problems. J Knowledge and Information Systems 67(8): 6753-6802. https://doi.org/10.1007/s10115-025-02422-5
[37] Saifullah S, Yudhana A, Suryotomo A P. (2025) Automatic brain tumor segmentation: advancing U-Net with ResNet50 encoder for precise medical image analysis. J IEEE Access 13(3): 43473-43489. https://doi.org/10.1109/ACCESS.2025.3547430
[38] Manakkadu S, Dutta S. (2024) Ant colony optimization based support vector machine for improved classification of unbalanced datasets. J Procedia Computer Science 237: 586-593. https://doi.org/10.1016/j.procs.2024.05.143
[39] Deevi D P, Kodadi S, Allur N S, et al. (2025) Improvement and application of particle swarm optimization algorithm. J Intelligent Decision Technologies 19(4): 2347-2367. https://doi.org/10.1177/18724981251331781
[40] Martini Silva R, de Oliveira Vitor A L, Favoretto Castoldi M, et al. (2026) Optimized time–frequency analysis for induction motor fault detection using hybrid differential evolution and deep learning techniques. J International Journal of Adaptive Control and Signal Processing 40(1): 2-26. https://doi.org/10.1002/acs.4075
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Copyright (c) 2026 Kun Zhang, Qingbiao Lin, Zhantao Fan, Shengmin Qiu, Xiaogang Wu, Zhizhong Li, Yan Guo
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