From DC to Holistic Centrality: AI-Driven Estimation for Enhanced Power Network Analysis

Document Type : Research Article

Authors

Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran

Abstract

This paper introduces a novel approach to power system analysis by integrating DC Holistic Centrality with advanced deep learning (DL) techniques to enhance the efficiency and accuracy of grid operation assessments. We propose DC Holistic Centrality, a computationally efficient measure derived from DC load flow data, which extends traditional operational centrality by incorporating generation and demand nodes as pendant buses. Leveraging this new metric, we develop a suite of AI-driven estimation methods: a linear regression baseline for active holistic betweenness prediction, a deep neural network (DNN) for accurate cross-bus prediction of active holistic betweenness, a convolutional neural network (CNN) for voltage magnitude estimation from DC holistic dependency matrices, and a scalability assessment using the IEEE 57-bus system to validate model robustness. The study utilizes a comprehensive dataset generated from varied operational scenarios, with feature selection guided by correlation analyses rather than additional extraction techniques. Results demonstrate significant improvements in capturing inter-bus dependencies and system dynamics, offering a promising framework for real-time grid monitoring and management.

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References

[1] M. Shahraeini, M. Besharatloo, P. Kotzanikolaou, Holistic Centrality: A New Approach to Evaluate Dynamics of Complex Power Networks, in:  2025 12th Iranian Conference on Renewable Energies and Distributed Generation (ICREDG), IEEE, 2025, pp. 1-6.
[2] G.A. Pagani, M. Aiello, The Power Grid as a complex network: A survey, Physica A: Statistical Mechanics and its Applications, 392(11) (2013) 2688-2700.
[3] R. Espejo, S. Lumbreras, A. Ramos, A Complex-Network Approach to the Generation of Synthetic Power Transmission Networks, IEEE Systems Journal, 13(3) (2019) 3050-3058.
[4] L.C. Freeman, A set of measures of centrality based on betweenness, Sociometry,  (1977) 35-41.
[5] A. Bavelas, A mathematical model for group structures, Human organization, 7(3) (1948) 16-30.
[6] M. Shahraeini, P. Kotzanikolaou, Analyzing electrical centrality metrics for optimal placement of microgrids and renewable sources, in:  2024 11th Iranian Conference on Renewable Energy and Distribution Generation (ICREDG), IEEE, 2024, pp. 1-8.
[7] Z. Wang, A. Scaglione, R.J. Thomas, Electrical centrality measures for electric power grid vulnerability analysis, in:  49th IEEE conference on decision and control (CDC), IEEE, 2010, pp. 5792-5797.
[8] A. Nasiruzzaman, H. Pota, Modified centrality measures of power grid to identify critical components: method, impact, and rank similarity, in:  2012 IEEE power and energy society general meeting, IEEE, 2012, pp. 1-8.
[9] A. Nasiruzzaman, H. Pota, Bus dependency matrix of electrical power systems, International Journal of Electrical Power & Energy Systems, 56 (2014) 33-41.
[10] M. Shahraeini, P. Kotzanikolaou, Towards a Centrality Control Center: Innovations in Complex Power Network Operation and Control, in:  2024 14th Smart Grid Conference (SGC), IEEE, 2024, pp. 1-7.
[11] M. Shahraeini, P. Kotzanikolaou, A dependency analysis model for resilient wide area measurement systems in smart grid, IEEE Journal on Selected Areas in Communications, 38(1) (2019) 156-168.
[12] M. Shahraeini, P. Kotzanikolaou, Towards an unified dependency analysis methodology for wide area measurement systems in smart grids, in:  2020 10th Smart Grid Conference (SGC), IEEE, 2020, pp. 01-06.
[13] M. Shahraeini, P. Kotzanikolaou, Resilience in wide area monitoring systems for smart grids, in:  Wide Area Power Systems Stability, Protection, and Security, Springer, 2020, pp. 555-569.
[14] M. Shahraeini, P. Kotzanikolaou, A methodology for unified assessment of physical and geographical dependencies of wide area measurement systems in smart grids, Energy Engineering and Management, 11(4) (2023) 30-39.
[15] M. Shahraeini, P. Kotzanikolaou, M. Nasrolahi, Communication resilience for smart grids based on dependence graphs and eigenspectral analysis, IEEE Systems Journal, 16(4) (2022) 6558-6568.
[16] J. Gui, H. Lei, T.R. McJunkin, B. Chen, B.K. Johnson, Operational resilience metrics for power systems with penetration of renewable resources, IET Generation, Transmission & Distribution, 17(10) (2023) 2344-2355.
[17] M. Shahraeini, A. Alvandi, S. Khormali, Behavior analysis of random power graphs for optimal PMU placement in smart grids, in:  2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, 2020, pp. 107-112.
[18] M. Shahraeini, S. Khormali, A. Alvandi, Optimal pmu placement considering reliability of measurement system in smart grids, in:  2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, 2022, pp. 205-210.
[19] M. Shahraeini, R. Soltanifar, Performance comparison between simple and Adam—Eve-like genetic algorithms in optimal PMU placement problem, in:  2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), IEEE, 2022, pp. 1-6.
[20] M. Shahraeini, Modified Erdős–Rényi Random Graph Model for Generating Synthetic Power Grids, IEEE Systems Journal, 18(1) (2023) 96-107.
[21] S. Pahwa, D. Weerasinghe, C. Scoglio, R. Miller, A complex networks approach for sizing and siting of distributed generators in the distribution system, in:  2013 North American Power Symposium (NAPS), IEEE, 2013, pp. 1-5.
[22] M. Saleh, Y. Esa, N. Onuorah, A.A. Mohamed, Optimal microgrids placement in electric distribution systems using complex network framework, in:  2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), IEEE, 2017, pp. 1036-1040.
[23] M. Saleh, Y. Esa, A. Mohamed, Applications of complex network analysis in electric power systems, Energies, 11(6) (2018) 1381.
[24] S. Korjani, A. Facchini, M. Mureddu, G. Caldarelli, A. Damiano, Optimal positioning of storage systems in microgrids based on complex networks centrality measures, Scientific Reports, 8(1) (2018) 16658.
[25] S. Harasis, H. Abdelgabir, Y. Sozer, M. Kisacikoglu, A. Elrayyah, A center of mass determination for optimum placement of renewable energy sources in microgrids, IEEE Transactions on Industry Applications, 57(5) (2021) 5274-5284.
[26] M. Shahraeini, G. Kohsari, M.H. Javidi, Comparison of meta-heuristic algorithms for solving dominating set problems in wams design, in:  2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), IEEE, 2022, pp. 1-7.
[27] M. Shahraeini, R. Soltanifar, A complex network-based approach for designing of wide area measurement systems in smart grids using Adam-Eve-like genetic algorithm, International Journal of Engineering, 37(2) (2024) 298-311.
[28] A. Keyhani, A. Abur, Knowledge-based power flow models, Electric power systems research, 9(2) (1985) 183-191.
[29] N. Kumar, R. Wangneo, P. Kalra, S. Srivastava, Application of artificial neural networks to load flow solutions, in:  TENCON'91. Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems, IEEE, 1991, pp. 199-203.
[30] Z. Kaseb, S. Orfanoudakis, P.P. Vergara, P. Palensky, Adaptive informed deep neural networks for power flow analysis, arXiv preprint arXiv:2412.02659,  (2024).
[31] R.D. Zimmerman, C.E. Murillo-Sánchez, R.J. Thomas, MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education, IEEE Transactions on Power Systems, 26(1) (2010) 12-19.
[32] D. Tiwari, M.J. Zideh, V. Talreja, V. Verma, S.K. Solanki, J. Solanki, Power flow analysis using deep neural networks in three-phase unbalanced smart distribution grids, IEEE Access, 12 (2024) 29959-29970.
[33] X. Hu, J. Yang, Y. Gao, M. Zhu, Q. Zhang, H. Chen, J. Zhao, Adaptive power flow analysis for power system operation based on graph deep learning, International Journal of Electrical Power & Energy Systems, 161 (2024) 110166.
[34] B. Taheri, S.A. Hosseini, H. Hashemi-Dezaki, Enhanced fault detection and classification in ac microgrids through a combination of data processing techniques and deep neural networks, Sustainability, 17(4) (2025) 1514.
AUT Journal of Electrical Engineering
Volume 58, Issue 1
2026
Pages 121-148

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