Adaptive Weighted Kernel Principal Component Analysis and Multi-attribute Decision Making-based Judgment Model for Power Grid Forced Transmission
DOI:
https://doi.org/10.4108/ew.13342Keywords:
HESS, Adaptive weighted kernel principal component analysis, Multi-attribute decision-making, TOPSIS method, Particle swarm optimization algorithmAbstract
INTRODUCTION: response to the theoretical bottlenecks of the hybrid energy storage system (HESS) when participating in grid regulation.
OBJECTIVES: Such as strong coupling of multi-dimensional state variables, difficulties in coordinating energy storage scheduling with grid stability, and prominent subjectivity in traditional power transfer decisions, a model for AI optimization collaborative control and grid state assessment with HESS as the core control object is proposed.
METHODS: Firstly, based on traditional strong transfer features such as fault characteristics, grid topology, equipment status, and environmental disturbances, the operation state variables of HESS (including state of charge, charging and discharging power, health level, etc.) are introduced to construct a multi-dimensional feature matrix integrating energy storage and the grid, and the objective weighting of each dimension is achieved based on the information entropy theory. Secondly, an adaptive weighted kernel principal component analysis (AW-KPCA) algorithm is proposed, which uses the particle swarm optimization (PSO) algorithm to dynamically adjust the kernel function parameters and feature weights, and incorporates the HESS charging and discharging strategy into the optimization space to achieve joint optimization of energy storage scheduling and high-dimensional feature dimension reduction. The core novelty is the synergistic integration of HESS dynamics into grid feature modeling, PSO-based joint optimization of kernel parameters, feature weights, and storage dispatch, and TOPSIS-driven risk quantification for forced transmission decisions.
RESULTS: Compared with the traditional kernel principal component analysis algorithm, the proposed method improved data processing efficiency by 41.3% and feature extraction accuracy by 15.7%. Finally, the TOPSIS multi-attribute decision-making theory is integrated to build a comprehensive judgment model on the adaptability of forced transportation, quantify the feasibility and risk level of forced transportation, and achieve optimal decision-making under multiple constraints.
CONCLUSION: The simulation test was completed on the IEEE 300-node system and the Guizhou Power Grid Actual Fault Data Set. The accuracy of the proposed model reached 95.7, and its robustness was significantly stronger. The research provides new theoretical and algorithmic support for HESS to participate in the restoration of power grid failures.
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[1] B. Yang, R. Zheng, Y. Han, et al., "Recent advances in fault diagnosis techniques for photovoltaic systems: A critical review," Prot. Control Mod. Power Syst., vol. 9, no. 3, pp. 36-59, May 2024, doi: 10.23919/PCMP.2023.000583.
[2] W. Zhuang, J. Fan, M. Xia, et al., "A multi-scale spatial-temporal graph neural network-based method of multienergy load forecasting in integrated energy system," IEEE Trans. Smart Grid, vol. 15, no. 3, pp. 2652-2666, May 2024, doi: 10.1109/TSG.2023.3315750.
[3] F. M. Shakiba, S. M. Azizi, M. Zhou, and A. Abusorrah, "Application of machine learning methods in fault detection and classification of power transmission lines: A survey," Artif. Intell. Rev., vol. 56, no. 7, pp. 5799-5836, Jul. 2023, doi: 10.1007/s10462-022-10296-0.
[4] M. N. Ali, M. Amer, and M. Elsisi, "Reliable IoT paradigm with ensemble machine learning for faults diagnosis of power transformers considering adversarial attacks," IEEE Trans. Instrum. Meas., vol. 72, no. 2, pp. 1-13, Feb. 2023, doi: 10.1109/TIM.2023.3300444.
[5] B. A. Tama, M. Vania, S. Lee, and S. Lim, "Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals," Artif. Intell. Rev., vol. 56, no. 5, pp. 4667-4709, May 2023, doi: 10.1007/s10462-022-10293-3.
[6] T. Wang, L. Zhang, and X. Wang, "Fault detection for motor drive control system of industrial robots using CNN-LSTM-based observers," CES Trans. Electr. Mach. Syst., vol. 7, no. 2, pp. 144-152, Jun. 2023, doi: 10.30941/CESTEMS.2023.00014.
[7] A. S. Alhanaf, M. Farsadi, and H. H. Balik, "Fault detection and classification in ring power system with DG penetration using hybrid CNN-LSTM," IEEE Access, vol. 12, no. 2, pp. 59953-59975, Feb. 2024, doi: 10.1109/ACCESS.2024.3394166.
[8] J. Hang, X. Shu, S. Ding, et al., "Robust open-circuit fault diagnosis for PMSM drives using wavelet convolutional neural network with small samples of normalized current vector trajectory graph," IEEE Trans. Ind. Electron., vol. 70, no. 8, pp. 7653-7663, Aug. 2023, doi: 10.1109/TIE.2022.3231304.
[9] M. Hajji, Z. Yahyaoui, M. Mansouri, et al., "Fault detection and diagnosis in grid-connected PV systems under irradiance variations," Energy Rep., vol. 9, no. 2, pp. 4005-4017, Mar. 2023, doi: 10.1016/j.egyr.2023.03.033.
[10] M. Kouraichi, M. Mansouri, M. Trabelsi, et al., "Deep learning for fault diagnosis in power transmission lines: Current trends, limitations, and future directions," IEEE Access, vol. 13, pp. 192105-192142, 2025, doi: 10.1109/ACCESS.2025.3628908.
[11] J. Mathebula and N. Mbuli, "Application of TOPSIS in power systems: A review," in 2024 Int. Conf. Electr., Comput. Energy Technol. (ICECET), IEEE, 2024, pp. 1-6, doi: 10.1109/ICECET61485.2024.10698355.
[12] S. Wang, Y. Fu, W. Zhang, C. Fernandez, and F. Blaabjerg, "Review on improved neural network algorithms for battery state of energy estimation in smart grids," Renew. Sustain. Energy Rev., vol. 231, p. 116797, May 2026, doi: 10.1016/j.rser.2026.116797.
[13] S. Wang, H. Gao, P. Takyi-Aninakwa, et al., "Improved multiple feature-electrochemical thermal coupling modeling of lithium-ion batteries at low-temperature with real-time coefficient correction," Prot. Control Mod. Power Syst., vol. 9, no. 3, pp. 157-173, May 2024, doi: 10.23919/PCMP.2023.000257.
[14] S. Wang, C. Ma, H. Gao, et al., "Improved hyperparameter Bayesian optimization-bidirectional long short-term memory optimization for high-precision battery state of charge estimation," Energy, vol. 328, p. 136598, Aug. 2025, doi: 10.1016/j.energy.2025.136598.
[15] S. Aziz, A. Qaiser, U. Kulsoom, et al., "Fault detection and fault diagnosis in power system using AI: A review," Sir Syed Univ. Res. J. Eng. Technol., vol. 14, 10.19781/j.issn.1673-9140.2026.01.017. no. 1, pp. 27-32, Jan. 2024, doi: 10.33317/ssurj.598.
[16] W. Liu, Z. Zhou, K. Cai, et al., "Application of knowledge graph in smart grid fault diagnosis," Appl. Math. Nonlinear Sci., vol. 8, no. 2, pp. 349-360, Jul. 2023, doi: 10.2478/amns.2021.2.00258.
[17] J. Liang, K. Zhang, A. Al-Durra, and D. Zhou, "A multi-information fusion algorithm to fault diagnosis of power converter in wind power generation systems," IEEE Trans. Ind. Inform., vol. 20, no. 2, pp. 1167-1179, Feb. 2024, doi: 10.1109/TII.2023.3271358.
[18] C. Jiang, H. Xu, W. Jin, et al., "Research on application of power Internet of Things technology in leakage fault diagnosis of rural power grid," Energy Rep., vol. 9, no. 2, pp. 847-854, Apr. 2023, doi: 10.1016/j.egyr.2023.04.164.
[19] A. Varbella, K. Amara, B. Gjorgiev, and G. Sansavini, "PowerGraph: A power grid benchmark dataset for graph neural networks," in Adv. Neural Inf. Process. Syst., vol. 37, 2024, pp. 110784-110804, doi: 10.52202/079017-3517.
[20] S. Su, X. Li, Z. Li, et al., "A distributed photovoltaic power anomaly perception method based on KPCA-OPTICS clustering," J. Electr. Power Sci. Technol., vol. 41, no. 1, pp. 174-184, Jan. 2026, doi:
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