Intelligent Identification of Dominant Error-Causing Parameters for Dynamic Simulation Models of Power Systems Based on Time–Frequency Feature Learning of Response Errors
DOI:
https://doi.org/10.4108/ew.14041Keywords:
power system dynamic simulation, dominant error-causing parameter identification, response error, time–frequency feature learning, continuous wavelet transformAbstract
INTRODUCTION: Dynamic simulation is important for disturbance analysis and security assessment in power systems. Parameter deviations can cause discrepancies between simulated and measured responses, and multiple deviations may produce complex transient response errors.
OBJECTIVES: This paper aims to characterize the error-causing effects of candidate parameters from response error patterns and identify priority parameters for subsequent model parameter calibration. It is intended as a pre-calibration parameter screening method rather than direct parameter correction.
METHODS: An intelligent identification method for dominant error-causing parameters is proposed based on time–frequency feature learning of response errors. Continuous wavelet transform is employed to extract local time–frequency features from short-term post-fault response errors and construct multi-channel time–frequency feature tensors. Parameter error-causing contribution labels are constructed by jointly considering parameter deviation magnitude and local response sensitivity. A convolutional neural network-based multi-output regression model is then trained using the feature tensors and labels to predict and rank candidate parameters by contribution score.
RESULTS: Case studies based on a modified IEEE 39-bus system show that the proposed method can identify a dominant error-causing parameter set more consistent with current response error patterns under multiple-parameter deviation conditions. Compared with conventional sensitivity-based parameter selection, the proposed method achieves smaller post-calibration response errors. When used for subsequent calibration, the selected subset reduced voltage and active-power errors by 85.34% and 90.09%, respectively.
CONCLUSION: The proposed method provides a more targeted candidate parameter set for dynamic simulation model parameter calibration and supports more efficient subsequent model refinement.
Downloads
References
[1] Erden F, Acilan E, Ustundag O, Bozkurt E, Gol M. PMU-Based Dynamic Model Calibration of Type 4 Wind Turbine Generators. Electronics. 2023;12(9):2004.
[2] Foroutan A, Basumallik S, Srivastava A. Estimating and Calibrating DER Model Parameters Using Levenberg–Marquardt Algorithm in Renewable Rich Power Grid. Energies. 2023;16(8):3512.
[3] Zhao D, Ning Y, Zhang C, Ma J, Qian M, Liu Y. Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method. Energies. 2024;17(23):5831.
[4] Sharma N, Lin Y. Parameter Error Identification for Validation and Calibration of Dynamic Models of Inverter-Based Resources. IEEE Trans Power Syst. 2026;41(1):396–412.
[5] Wang L, Qi J. Parameter Subset Selection for Power System Model Calibration Using Both Sensitivity and Identifiability. IEEE Access. 2024;12:153783–153795.
[6] Asmine M, Brochu J, Fortmann J, Gagnon R, Kazachkov Y, Langlois CE, Larose C, Muljadi E, MacDowell J, Pourbeik P, Seman SA, Wiens K. Model Validation for Wind Turbine Generator Models. IEEE Trans Power Syst. 2011;26(3):1769–1782.
[7] Akhlaghi S, Zhou N, Chiang HD. Starting Point Selection Approach for Power System Model Validation Using Event Playback. IET Gener Transm Distrib. 2020;14(19):3972–3982.
[8] Xu T, Birchfield AB, Overbye TJ. Modeling, Tuning, and Validating System Dynamics in Synthetic Electric Grids. IEEE Trans Power Syst. 2018;33(6):6501–6509.
[9] Dakhlan DF, Muslim J, Kurniawan I, Banjar-Nahor KM, Soedjarno BA, Hariyanto N. Comparing Fast Fourier Transform and Prony Method for Analysing Frequency Oscillation in Real Power System Interconnection. Energies. 2025;18(9):2377.
[10] Khodadadi Arpanahi M, Kordi M, Torkzadeh R, Haes Alhelou H, Siano P. An Augmented Prony Method for Power System Oscillation Analysis Using Synchrophasor Data. Energies. 2019;12(7):1267.
[11] Khalilinia H, Zhang L, Venkatasubramanian V. Fast Frequency-Domain Decomposition for Ambient Oscillation Monitoring. IEEE Trans Power Deliv. 2015;30(3):1631–1633.
[12] Priyadarshini MS, Bajaj M, Prokop L, Berhanu M. Perception of Power Quality Disturbances Using Fourier, Short-Time Fourier, Continuous and Discrete Wavelet Transforms. Sci Rep. 2024;14:3443.
[13] Wang P, Zhang Z, Huang Q, Zhang W, Lee WJ. PMU-Based Problematic Parameter Identification Approach for Calibrating Generating Unit Models. IEEE Trans Ind Appl. 2021;57(5):4520–4527.
[14] Li J, Liu H, Bi T. Classification of Power Quality Disturbance Based on S-Transform and Convolutional Neural Network. Front Energy Res. 2021;9:708131.
[15] Avdakovic S, Nuhanovic A, Kusljugic M, Music M. Wavelet Transform Applications in Power System Dynamics. Electr Power Syst Res. 2012;83(1):237–245.
[16] Fan L, Miao Z, Shah S, Koralewicz P, Gevorgian V, Fu J. Data-Driven Dynamic Modeling in Power Systems: A Fresh Look on Inverter-Based Resource Modeling. IEEE Power Energy Mag. 2022;20(3):64–76.
[17] Matar M, Xu B, Elmoudi R, Olatujoye O, Wshah S. A Deep Learning-Based Framework for Parameters Calibration of Power Plant Models Using Event Playback Approach. IEEE Access. 2022;10:72132–72144.
[18] Xiong L, Liu X, Liu Y, Zhuo F. Modeling and Stability Issues of Voltage-Source Converter-Dominated Power Systems: A Review. CSEE J Power Energy Syst. 2022;8(6):1530–1549.
[19] Li F, Ma J. Comprehensive Dynamic Interaction Studies in Inverter-Penetrated Power Systems. Energies. 2024;17(9):2235.
[20] Hatziargyriou N, Milanovic JV, Rahmann C, Ajjarapu V, Canizares C, Erlich I, Hill D, Hiskens I, Kamwa I, Pal B, Pourbeik P, Sanchez-Gasca J, Stankovic A, Van Cutsem T, Vittal V, Vournas C. Definition and Classification of Power System Stability—Revisited & Extended. IEEE Trans Power Syst. 2021;36(4):3271–3281.
[21] Gill-Estevez P, Marchi P, Galarza C, Elizondo MA. Non-Stationary Power System Forced Oscillation Analysis Using Synchrosqueezing Transform. IEEE Trans Power Syst. 2021;36(2):1583–1593.
[22] Yu M, Wei J, Tian S, Sun J, Wu Y, Zhang S, Hu J. A Low-Frequency Oscillation Identification Method for Power System Based on Adaptive Generalized S-Transform with Bat Algorithm. J Electr Comput Eng. 2024;2024:2088540.
[23] Oliveira MO, Reversat JH, Reynoso LA. Wavelet Transform Analysis to Applications in Electric Power Systems. In: Baleanu D, editor. Wavelet Transform and Complexity. London: IntechOpen; 2019.
[24] Khazeiynasab SR, Zhao J, Batarseh I, Tan B. Power Plant Model Parameter Calibration Using Conditional Variational Autoencoder. IEEE Trans Power Syst. 2022;37(2):1642–1652.
[25] Qin C, Jin Y, Tian M, Ju P, Zhou S. Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification. Energies. 2023;16(16):5915.
[26] Hajnoroozi AA, Aminifar F, Ayoubzadeh H. Generating Unit Model Validation and Calibration Through Synchrophasor Measurements. IEEE Trans Smart Grid. 2015;6(1):441–449.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jianxin Zhang, Qiyue Wang, Hongxuan Zhang, Tuo Jiang, Qin Gao, Chunxiao Liu, Yanzhe Cheng
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
