IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v249y2022ics0360544222006156.html
   My bibliography  Save this article

Harmonic state estimation based on discrete exponential expansion, singular value decomposition and a variable measurement model

Author

Listed:
  • Cisneros-Magaña, Rafael
  • Medina-Rios, Aurelio
  • Fuerte-Esquivel, Claudio R.
  • Segundo-Ramírez, Juan

Abstract

In this contribution, a method for harmonic state estimation (HSE) of electrical systems using the discrete exponential expansion (DEE), singular value decomposition (SVD) and a variable measurement model is presented. Variable harmonics, interharmonics, and subharmonics can be estimated using this DEE-SVD time-domain methodology, depending on the number of state variables in the system model and the measurements related through a variable measurement equation, which is proposed as a function of a variable number of measurements and their derivatives to define over, normal and under-determined conditions, respectively. The observability analysis can be verified using the SVD and the resulting null-space vectors, if the electrical system is not completely observable; this analysis can define observable system areas. The HSE regularly receives a limited number of measurements from the system, performs the state estimation and evaluates the global state of the actual harmonic condition. The discrete exponential expansion Newton method determines the periodic steady-state under a particular harmonic condition, obtains a convenient initial steady-state condition to apply the SVD and performs the HSE with a small state estimation error. Noisy measurements adversely affect the state estimation and may increase the state estimation error. The time-domain DEE-HSE can be used to enhance the operation and control of electrical systems under harmonic distortion conditions, e.g., to control active and passive harmonic filters. The state estimation results have been validated through direct comparison against the response obtained with the PSCAD/EMTDC® simulator.

Suggested Citation

  • Cisneros-Magaña, Rafael & Medina-Rios, Aurelio & Fuerte-Esquivel, Claudio R. & Segundo-Ramírez, Juan, 2022. "Harmonic state estimation based on discrete exponential expansion, singular value decomposition and a variable measurement model," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006156
    DOI: 10.1016/j.energy.2022.123712
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222006156
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123712?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Khorshidi, Reza & Shabaninia, Faridon & Niknam, Taher, 2016. "A new smart approach for state estimation of distribution grids considering renewable energy sources," Energy, Elsevier, vol. 94(C), pages 29-37.
    2. Basetti, Vedik & Chandel, Ashwani K. & Chandel, Rajeevan, 2016. "Power system dynamic state estimation using prediction based evolutionary technique," Energy, Elsevier, vol. 107(C), pages 29-47.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amina Mseddi & Omar Naifar & Mohamed Rhaima & Lassaad Mchiri & Abdellatif Ben Makhlouf, 2023. "Robust Control for Torque Minimization in Wind Hybrid Generators: An H ∞ Approach," Mathematics, MDPI, vol. 11(16), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    3. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    4. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    5. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    6. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    7. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    9. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    10. Snyder, Ralph D. & Ord, J. Keith & Koehler, Anne B. & McLaren, Keith R. & Beaumont, Adrian N., 2017. "Forecasting compositional time series: A state space approach," International Journal of Forecasting, Elsevier, vol. 33(2), pages 502-512.
    11. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    12. Rivera, Nilza & Guzmán, Juan Ignacio & Jara, José Joaquín & Lagos, Gustavo, 2021. "Evaluation of econometric models of secondary refined copper supply," Resources Policy, Elsevier, vol. 73(C).
    13. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    14. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    16. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    17. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    18. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    19. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    20. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    21. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    22. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.