Parameter Estimation of Wind Turbines With PMSM Using Cubature Kalman Filters

Given the growing installed capacity, wind energy will exert a profound impact on the flexibility of modern energy systems. Wind power forecasting is a practical solution for dealing with the attributed variations and uncertainties, balancing supply and demand, and improving the reliability of the system. To achieve more accurate and generalizable forecast models, comprehensive data sets, supplied by multiple wind farms owing to their spatio-temporal dependencies, are required. In addition, data aggregation/collaboration across many wind farms scattered around a country is difficult, if not impossible, due to complex administrative processes, industry competition, and data privacy and security concerns. This article offers federated learning-based wind energy forecasting as a novel decentralized collaborative modeling method capable of training a single model on data from many wind farms without jeopardizing the privacy or security of data. To this end, rather than sending private data across sites, local model parameters are securely transmitted. A comparison between the proposed private distributed model and non-private centralized and fully private localized models indicates the high performance of the proposed federated learning-based wind power forecasting with 87.96% accuracy. Enjoying the smoothing effect, the higher generalizability of the proposed model with 83.63% accuracy is also substantiated in comparison to localized and centralized approaches while the privacy of the underlying data is preserved.

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“…MAE, which evaluates the mean absolute difference between predictions and observations, is expressed in (8) as…”

Section: ) Mean Absolute Error (Mae)mentioning

confidence: 99%

“…Statistical and intelligent models use past observations to extract time-varying relationships in timeseries [7]. Various statistical models for wind speed forecasting have been introduced, including Kalman filter [8], Box. Jenkins models (AR, ARIMA models, etc.)…”

Section: Introductionmentioning

confidence: 99%

Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting

et al. 2023

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“…Since there are errors with the wind speed measurements, DSE for DFIG with unknown wind speeds is addressed in [78]. The joint estimation of dynamic states and parameters of permanent-magnet synchronous motor-based wind generator is investigated in [79]. It should be noted that the relation of estimated states via DSE with system dynamics and stability phenomena is still the subject of ongoing research.…”

Section: Dse For Power Electronics-interfaced Renewable Generation Visibilitymentioning

confidence: 99%

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Zhao

Netto

Huang

et al. 2021

IEEE Trans. Power Syst.

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150

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Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, timesynchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving This work was partially supported by US Department of Energy under Advanced Grid Modernization Program.

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“…Recently, the signal injection is an effective way to estimate machine parameters [18,5,[20][21][22][23][24][25]. This way may not be sufficient to make the estimation model unique-solution if it does not consider changes in motor model, such as dynamic change on the inductance.…”

Section: Introductionmentioning

confidence: 99%

Online multi‐parameter estimation of permanent magnet synchronous machine with step‐pulse injection

Wang

Lin

et al. 2021

IET Electric Power Applications

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Online estimation of the machine parameters is essential to fault diagnosis and high‐performance control of permanent magnet synchronous machine (PMSM). The signal injection is an effective way to estimate machine parameters. However, the inductance variation caused by signal injection will result in difficulties for the estimated parameters to converge. At the same time, online parameters estimation brings heavy burden to the operation of control system. To solve these two problems, optimized inductance model is built with the step‐pulse injection to improve the accuracy of estimation. Compared with the existing method, the step‐by‐step parameter estimation method is adopted and orders of feedback matrix are reduced, which improves the computation efficiency. The forgetting factor recursive least square (FFRLS) algorithm is adopted to estimate stator resistance, rotor flux linkage and d‐, q‐axes inductances without acquiring any nominal parameter. The minimum amplitude and appropriate frequency of step‐pulse are determined by the parameter error and convergence analysis. The effectiveness of the proposed method is verified via a 1.5 kW PMSM drive platform.

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Parameter Estimation of Wind Turbines With PMSM Using Cubature Kalman Filters

Cited by 19 publications

References 26 publications

Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting

Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Online multi‐parameter estimation of permanent magnet synchronous machine with step‐pulse injection

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