AIEng/
Articles/
2506000743
  • Open Access
  • Article
On Wide-Band Oscillation Localization in Power Transmission Grids: Explainability and Improvement
  • Yuyou Li,   
  • Jie Gu *,   
  • Jiaqing Wu,   
  • Zhijian Jin,   
  • Honglin Wen

Received: 14 Apr 2025 | Revised: 23 May 2025 | Accepted: 30 May 2025 | Published: 13 Jun 2025

Abstract

The broadband oscillation caused by the large-scale integration of new energy generation units into the power grid poses hidden dangers to the stable operation of the power grid. Fast and accurate positioning of the oscillation source is the basis for cutting off the oscillation source. In order to improve the interpretability and accuracy of the broadband oscillation positioning model, this paper proposes an interpretability framework for the transmission network broadband oscillation positioning model, mainly including the improved broadband oscillation model and its interpretation framework. This model integrates graph convolutional neural network and long short-term memory network, takes transmission network measurement sampling data as input, and establishes a broadband oscillation localization model in a data-driven manner; An explanatory framework was constructed for the proposed wideband oscillation localization model, which combines global and local interpretations based on the additive interpretation of Shapley values to improve the interpretability of the wideband oscillation localization model. Based on the explanatory results, an attention feature mechanism is introduced into the localization model to enhance the wideband oscillation localization model. This article uses MATLAB/Simulink (version 2024b) to build a power grid model, produces a sample dataset, and verifies the feasibility and effectiveness of the proposed explanatory framework through numerical simulation. 

References 

  • 1.
    Cheng, H.; Li, J.; Wu, Y.; et al. Challenges and Prospects of AC/DC Transmission Network Planning Considering High Penetration of Renewable Energy. Autom. Electr. Power Syst. 2017, 41, 19–27.
  • 2.
    Wang, M.; Sun, H. Online Localization Analysis Technology for Forced Power Oscillation Sources. Proc. CSEE 2014, 34, 6209–6215.
  • 3.
    Banna, H.U.; Solanki, S.K.; Solanki, J. Data-driven Disturbance Source Identification for Power System Oscillations Using Credibility Search Ensemble Learning. IET Smart Grid 2019, 2, 293–300.
  • 4.
    Gu, J.; Xie, D.; Gu, C.; et al. Location of Low-Frequency Oscillation Sources Using Improved D-S Evidence Theory. Int. J. Electr. Power Energy Syst. 2021, 125, 106444.
  • 5.
    Doran, D.; Schulz, S.; Besold, T.R. What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. arXiv 2017, arXiv:1710.00794.
  • 6.
    Samek, W.; Wiegand, T.; Müller, K.R. Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv 2017, arXiv:1708.08296.
  • 7.
    Zhang, Q.-S.; Zhu, S.-C. Visual Interpretability for Deep Learning: A Survey. Front. Inf. Technol. Electron. Eng. 2018, 19, 27–39.
  • 8.
    Han, T.; Chen, J.; Li, Y.; et al. Research on Interpretable Proxy Models for Power System Stability Assessment Using Machine Learning. Proc. CSEE 2020, 40, 4122–4131.
  • 9.
    Ji, S.; Li, J.; Du, T.; et al. A Review of Interpretability Methods, Applications, and Security Research for Machine Learning Models. J. Comput. Res. Dev. 2019, 56, 2071–2096.
  • 10.
    Saltelli, A.; Tarantola, S.; Campolongo, F.; et al. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. J. R. Stat. Soc. Ser. A 2004, 168, 464.
  • 11.
    Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why Should I Trust You?” Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016.
  • 12.
    Lundberg, S.M.; Lee, S.I. A Unified Approach to Interpreting Model Predictions. Adv. Neural Inf. Process. Syst. 2017, 30.
  • 13.
    Lundberg, S.M.; Erion, G.G.; Lee, S.I. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv 2018, arXiv:1802.03888.
  • 14.
    Robnik-Šikonja, M.; Kononenko, I. Explaining Classifications for Individual Instances. IEEE Trans. Knowl. Data Eng. 2008, 20, 589–600.
  • 15.
    Fong, R.; Vedaldi, A. Interpretable Explanations of Black Boxes by Meaningful Perturbation. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017.
  • 16.
    Li, J.; Monroe, W.; Jurafsky, D. Understanding Neural Networks through Representation Erasure. arXiv 2016, arXiv:1612.08220.
  • 17.
    Chen, W. Pre-Loan Overdue Identification and Model Expression for Internet Finance Based on XGBoost. Master’s Thesis, Harbin Institute of Technology, Harbin, China, 2019.
  • 18.
    Yu, J. Research on Prediction Model for Gestational Diabetes Based on Ensemble Learning Algorithms. Master’s Thesis, Harbin Institute of Technology, Harbin, China, 2019.
  • 19.
    Feng, S.; Cui, H.; Chen, J.; et al. Wide-Frequency Oscillation Disturbance Source Localization Method Based on Autoencoder Signal Compression and LSTM. Autom. Electr. Power Syst. 2022, 1–12.
Share this article:
Download PDF
DOI
10.53941/aieng.2025.100002
Issue
Volume 1, Issue 1
How to Cite
Li, Y.; Gu, J.; Wu, J.; Jin, Z.; Wen, H. On Wide-Band Oscillation Localization in Power Transmission Grids: Explainability and Improvement. AI Engineering 2025, 1 (1), 2. https://doi.org/10.53941/aieng.2025.100002.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2025 by the authors.