2504000053
  • Open Access
  • Article
Recursive Strong Tracking Filtering for Power Harmonic Detection With Outliers-Resistant Event-Triggered Mecha-nism
  • Xingzhen Bai 1, *,   
  • Guhui Li 1,   
  • Mingyu Ding 2,   
  • Liqun Yu 1,   
  • Yufeng Sun 1

Received: 24 Sep 2023 | Accepted: 25 Dec 2023 | Published: 24 Dec 2024

Abstract

This paper is concerned with the problem of power harmonic detection subject to communication resource constraints and measurement outliers. A dynamic tracking model is established to capture the dynamics of harmonic signals considering that the underlying system is subject to multiplicative noises, additive noises and outliers. Furthermore, an outlier-resistant event-triggered mechanism is designed to prevent the transmission of unnecessary measurements and outliers. In order to guarantee the satisfactory filtering performance, this paper aims to design a recursive strong tracking filtering algorithm under the event-triggered mechanism, where an upper bound on the filtering error covariance matrix is obtained by solving a set of Riccati difference equations, and minimized to recursively compute the filter gain matrix. Finally, the effectiveness of the proposed algorithm is verified through carrying out two sets of simulations.

References 

  • 1.
    Chen, W.; Liu, L.; Liu, G.P. Privacy-preserving distributed economic dispatch of microgrids: A dynamic quantization-based consensus scheme with homomorphic encryption. IEEE Trans. Smart Grid, 2023, 14: 701−713. doi: 10.1109/TSG.2022.3189665
  • 2.
    Fang, Z.; Lin, Y.Z.; Song, S.; et al. State estimation for situational awareness of active distribution system with photovoltaic power plants. IEEE Trans. Smart Grid, 2021, 12: 239−250. doi: 10.1109/TSG.2020.3009571
  • 3.
    Qu, B.G.; Wang, Z.D.; Shen, B.; et al. Outlier-resistant recursive state estimation for renewable-electricity-generation-based microgrids. IEEE Trans. Ind. Inf., 2023, 19: 7133−7144. doi: 10.1109/TII.2022.3205354
  • 4.
    Eroğlu, H.; Cuce, E.; Cuce, P.M.; et al. Harmonic problems in renewable and sustainable energy systems: A comprehensive review. Sustain. Energy Technol. Assessm., 2021, 48: 101566. doi: 10.1016/j.seta.2021.101566
  • 5.
    Kettner, A.M.; Reyes-Chamorro, L.; Becker, J.K.M.; et al. Harmonic power- flow study of polyphase grids with converter-interfaced distributed energy resources–Part I: Modeling framework and algorithm. IEEE Trans. Smart Grid, 2022, 13: 458−469. doi: 10.1109/TSG.2021.3120108
  • 6.
    Sun, Y.Y.; Xie, X.M.; Zhang, L.H.; et al. A voltage adaptive dynamic harmonic model of nonlinear home appliances. IEEE Trans. Ind. Electron., 2020, 67: 3607−3617. doi: 10.1109/TIE.2019.2921261
  • 7.
    Deng, Y.; Zhao, G.J.; Zhu, K.H.; et al. NCAFI: Nuttall convolution window all-phase FFT interpolation-based harmonic detection algorithm for infrared imaging detection. Infrared Phys. Technol., 2022, 125: 104310. doi: 10.1016/j.infrared.2022.104310
  • 8.
    Su, T.X.; Yang, M.F.; Jin, T.; et al. Power harmonic and interharmonic detection method in renewable power based on Nuttall double-window all-phase FFT algorithm. IET Renew. Power Generat., 2018, 12: 953−961. doi: 10.1049/iet-rpg.2017.0115
  • 9.
    Kapisch, E.B.; Filho, L.M.A.; Silva, L.R.M.; et al. Novelty detection in power quality signals with surrogates: A time-frequency technique. In Proceedings of 2020 International Conference on Systems, Signals and Image Processing, Niteroi, Brazil, 01-03 July 2020; IEEE: New York, 2020; pp. 373–378. doi: 10.1109/IWSSIP48289.2020.9145149
  • 10.
    Wu, J.Z.; Mei, F.; Chen, C.; et al. Harmonic detection method in power system based on empirical wavelet transform. Power System Protection and Control, 2020, 48: 136−143. doi: 10.19783/j.cnki.pspc.190470
  • 11.
    Bagheri, A.; Mardaneh, M.; Rajaei, A.; et al. Detection of grid voltage fundamental and harmonic components using Kalman filter and generalized averaging method. IEEE Trans. Power Electron., 2016, 31: 1064−1073. doi: 10.1109/TPEL.2015.2418271
  • 12.
    Nie, X.H. Detection of grid voltage fundamental and harmonic components using Kalman filter based on dynamic tracking model. IEEE Trans. Ind. Electron., 2020, 67: 1191−1200. doi: 10.1109/TIE.2019.2898626
  • 13.
    Qu, B.G.; Li, N.; Liu, Y.R.; et al. Estimation for power quality disturbances with multiplicative noises and correlated noises: A recursive estimation approach. Int. J. Syst. Sci., 2020, 51: 1200−1217. doi: 10.1080/00207721.2020.1755476
  • 14.
    Nie, H.Y.; Nie, X.H. Research on dynamic modeling of KF algorithm for detecting distorted AC signal. Energies, 2021, 14: 8175. doi: 10.3390/en14238175
  • 15.
    Xi, Y.H.; Li, Z.W.; Zeng, X.J.; et al. Detection of voltage sag using an adaptive extended Kalman filter based on maximum likelihood. J. Electr. Eng. Technol., 2017, 12: 1016−1026. doi: 10.5370/JEET.2017.12.3.1016
  • 16.
    Chen, X.J.; Li, K.C.; Xiao, J. Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine. Int. Trans. Electr. Energy Syst., 2018, 28: e2560. doi: 10.1002/etep.2560
  • 17.
    He, S.F.; Li, K.C.; Zhang, M. A new transient power quality disturbances detection using strong trace filter. IEEE Trans. Instrum. Meas., 2014, 63: 2863−2871. doi: 10.1109/TIM.2014.2326762
  • 18.
    Pramanik, M.; Routray, A.; Mitra, P. A two-stage adaptive symmetric-strong-tracking square-root cubature Kalman filter for harmonics and interharmonics estimation. Electr. Power Syst. Res., 2022, 210: 108133. doi: 10.1016/j.jpgr.2022.108133
  • 19.
    Seger, P.V.H.; Grando, F.L.; Lazzaretti, A.E.; et al. Power system monitoring through low-voltage distribution network using freePMU. IEEE Trans. Ind. Applicat. 2022 , 58, 3153–3163. doi: 10.1109/TIA.2022.3151045
  • 20.
    Bai, X.Z.; Zheng, X.L.; Ge, L.J.; et al. Event-triggered forecasting-aided state estimation for active distribution system with distributed generations. Front. Energy Res., 2021, 9: 707183. doi: 10.3389/fenrg.2021.707183
  • 21.
    Cheng, C.; Bai, X.Z. Robust forecasting-aided state estimation in power distribution systems with event-triggered transmission and reduced mixed measurements. IEEE Trans. Power Syst., 2021, 36: 4343−4354. doi: 10.1109/TPWRS.2021.3062386
  • 22.
    Hu, J.; Wang, Z.D.; Liu, S.; et al. A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements. Automatica, 2016, 64: 155−162. doi: 10.1016/j.automatica.2015.11.008
  • 23.
    Li, Q.; Wang, Z.D.; Shen, B.; et al. A resilient approach to recursive distributed filtering for multirate systems over sensor networks with time-correlated fading channels. IEEE Trans. Signal Inf. Process. Over Networks, 2021, 7: 636−647. doi: 10.1109/TSIPN.2021.3117366
  • 24.
    Qian, W.; Gao, Y.S.; Yang, Y. Global consensus of multiagent systems with internal delays and communication delays. IEEE Trans. Syst. Man Cybern. Syst., 2019, 49: 1961−1970. doi: 10.1109/TSMC.2018.2883108
  • 25.
    Qian, W.; Xing, W.W.; Fei, S. M. H∞ state estimation for neural networks with general activation function and mixed time-varying delays. IEEE Trans. Neural Netw. Learn. Syst., 2021, 32: 3909−3918. doi: 10.1109/TNNLS.2020.3016120
  • 26.
    Zhao, Z.Y.; Wang, Z.D.; Zou, L.; et al. Set-membership filtering for time-varying complex networks with uniform quantisations over randomly delayed redundant channels. International Journal of Systems Science, 2020, 51: 3364−3377. doi: 10.1080/00207721.2020.1814898
  • 27.
    Bai, X.Z.; Wang, Z.D.; Zou, L.; et al. Target tracking for wireless localization systems using set-membership filtering: A component-based event-triggered mechanism. Automatica, 2021, 132: 109795. doi: 10.1016/j.automatica.2021.109795
  • 28.
    Rahimi, F.; Rezaei, H. An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects. Neurocomputing, 2021, 426: 104−113. doi: 10.1016/j.neucom.2020.09.074
  • 29.
    Yu, Y.J.; Dong, H.L.; Wang, Z.D.; et al. Delay-distribution-dependent non-fragile state estimation for discrete-time neural networks under event-triggered mechanism. Neural Comput. Applicat., 2019, 31: 7245−7256. doi: 10.1007/s00521-018-3516-z
  • 30.
    Ge, X.H.; Han, Q.L.; Wang, Z.D. A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybern., 2019, 49: 171−183. doi: 10.1109/TCYB.2017.2769722
  • 31.
    Wang, S.Y.; Wang, Z.D.; Dong, H.L.; et al. A dynamic event-triggered approach to recursive nonfragile filtering for complex networks with sensor saturations and switching topologies. IEEE Trans. Cybern., 2022, 52: 11041−11054. doi: 10.1109/TCYB.2021.3049461
  • 32.
    Yi, X.L.; Liu, K.; Dimarogonas, D.V.; et al. Dynamic event-triggered and self-triggered control for multi-agent systems. IEEE Trans. Automat. Control, 2019, 64: 3300−3307. doi: 10.1109/TAC.2018.2874703
  • 33.
    Fang, H.Z.; Haile, M.A.; Wang, Y.B. Robustifying the Kalman filter against measurement outliers: An innovation saturation mechanism. In Proceedings of 2018 IEEE Conference on Decision and Control, Miami, FL, USA, 17-19 December 2018; IEEE: New York, 2018; pp. 6390–6395. doi: 10.1109/CDC.2018.8619140
  • 34.
    Zhao, X.; Liu, C.S.; Liu, J.L.; et al. Probabilistic-constrained reliable H∞ tracking control for a class of stochastic nonlinear systems: An outlier-resistant event-triggered scheme. J. Franklin Inst., 2021, 358: 4741−4760. doi: 10.1016/j.jfranklin.2021.04.012
  • 35.
    Zou, L.; Wang, Z.D.; Geng, H.; et al. Set-membership filtering subject to impulsive measurement outliers: A recursive algorithm. IEEE/CAA J. Autom. Sin., 2021, 8: 377−388. doi: 10.1109/JAS.2021.1003826
  • 36.
    Liu, Q.Y.; Wang, Z.D.; He, X.; et al. Event-based recursive distributed filtering over wireless sensor networks. IEEE Trans. Automat. Control, 2015, 60: 2470−2475. doi: 10.1109/TAC.2015.2390554
  • 37.
    Wen, C.B.; Wang, Z.D.; Geng, T.; et al. Event-based distributed recursive filtering for state-saturated systems with redundant channels. Inf. Fus., 2018, 39: 96−107. doi: 10.1016/j.inffus.2017.04.004
  • 38.
    Li, Q.; Wang, Z.D.; Li, N.; et al. A dynamic event-triggered approach to recursive filtering for complex networks with switching topologies subject to random sensor failures. IEEE Trans. Neural Netw. Learn. Syst., 2020, 31: 4381−4388. doi: 10.1109/TNNLS.2019.2951948
Share this article:
How to Cite
Bai, X.; Li, G.; Ding, M.; Yu, L.; Sun, Y. Recursive Strong Tracking Filtering for Power Harmonic Detection With Outliers-Resistant Event-Triggered Mecha-nism. International Journal of Network Dynamics and Intelligence 2024, 3 (4), 100023. https://doi.org/10.53941/ijndi.2024.100023.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2024 by the authors.