Adaptive Extended Kalman Filter Based Dynamic Equivalent Method of PMSG Wind Farm Cluster

Wang, Tong; Huang, Shilou; Gao, Mingyang; Wang, Zengping

doi:10.1109/tia.2021.3055749

Cited by 38 publications

(12 citation statements)

References 19 publications

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“…The Kalman filter is an effective autoregressive filter and an optimal recursive mathematical processing method. It can predict and estimate the current system state of a linear dynamic system under a series of incomplete and Gaussian noise measurements [ 31 ]. Therefore, in this paper, Kalman filtering is also employed to filter the pressure values, obtaining more accurate airspeed values.…”

Section: Methods For Airspeed Identificationmentioning

confidence: 99%

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Chen,

Li,

et al. 2024

Sensors

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Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV’s flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a ‘multi-level time window + threshold’ approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.

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Section: Methods For Airspeed Identificationmentioning

confidence: 99%

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Chen,

Li,

et al. 2024

Sensors

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“…Wind turbines are involved in contributing a high level of impact in the generation process of electrical energy [18]. Moreover, it has been observed that the application of wind turbines is involved in turning wind power into electrical energy.…”

Section: Role Of Wind Turbines In the Wind Farmmentioning

confidence: 99%

Study on Grid-Connected Power Quality Improvement of Wind Farms Based on Repetitive Controller

Zhu,

Lv,

et al. 2024

SCPE

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The project highlights the wind energy and the process followed in the wind farms in order to generate the energy by using wind power and wind capacity as well. Moreover, it has been observed that wind energy is also considered one of the most effective electrical energy sources. The application of wind energy can be beneficial for different aspects of the business sectors of different countries across the globe and this is possible because of its cost-effectiveness. Furthermore, this study encompasses the strategies employed within wind farms to preserve and efficiently harness wind power for the generation of electrical energy. Notably, the project delineates the utilization of a diverse array of instruments, each instrumental in the conversion of wind power into valuable wind energy. Beyond the realm of technology, wind farms grapple with a spectrum of formidable challenges, stemming from the inherently capricious nature of wind. In addition, various issues concerning power quality and the stability of the power system have also been identified within these wind farms. To ameliorate these multifaceted challenges, the project introduces a range of meticulous control measures for implementation.

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“…Synthetic and model prediction methods are adopted. As a mighty tool for error correction, Kalman filtering has been employed in wind prediction (Wang et al, 2021;Vogelzang and Stoffelen, 2011). The machine learning technology has superiorities in regard to self-adaptive and data-driven features which enable it to be extensively applied for data post-processing of NWP.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive wind speed forecast correction strategy with an artificial intelligence algorithm

Zhao

Sun

Tang³

et al. 2022

Front. Environ. Sci.

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Wind speed forecasting is critical to renewable energy generation, agriculture, and disaster prevention. Due to the uncertainty and intermittence of wind, conventional forecasting methods with numerical weather prediction (NWP) models fall short of achieving satisfactorily high accuracy. Post-processing of the predicted results is necessary for enhancing the prediction accuracy. The industry generally employs time-series prediction (TSP) methods for error correction, yet it is time-consuming since repeated modeling is needed if the location changes. Aiming at addressing this problem, this paper discusses the application of a deep learning algorithm in the post-processing period of wind speed prediction. NWP results are utilized as the forecasting basis, and deep learning algorithms are used for minimizing errors. An experimental study is conducted with industrial data. The functionality and performance of TSP-based algorithms including rolling mean, exponential smoothing, and autoregressive integrated moving average algorithms are compared with deep learning-based algorithms, including long-short term memory and convolutional neural network. From the numerical results, both TSP and deep-learning error-correction methods can effectively increase the accuracy of day-level NWP model prediction results, while deep-learning methods are data-driven, and no modeling process is needed. This work also poses an insight into the future development of wind speed prediction in meteorology.

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Adaptive Extended Kalman Filter Based Dynamic Equivalent Method of PMSG Wind Farm Cluster

Cited by 38 publications

References 19 publications

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Study on Grid-Connected Power Quality Improvement of Wind Farms Based on Repetitive Controller

A comprehensive wind speed forecast correction strategy with an artificial intelligence algorithm

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