Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor

Pan, Lin; Xiong, Yan; Zhu, Ze; Wang, Leichong

doi:10.1016/j.renene.2021.11.104

Cited by 14 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a data-driven soft sensor systems are required. Some works utilize soft sensors for wind power forecasting [20] [21]. However, these single-model-based soft sensors lack stability in real-world deployment.…”

Section: Soft Sensorsmentioning

confidence: 99%

Large-scale ensemble learning for robust wind power forecasting with supercomputing

Long¹,

Wei²,

Zhong³

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

View full text Add to dashboard Cite

Wind power is renewable energy and plays a critical role in the carbon emission peak and carbon neutrality plan. As wind speed and direction is unstable, the generated electricity power fluctuates, and therefore cannot directly accepted by the power grid. A common solution is to forecast the wind power generation and adjust the power generation and dispatching strategy to improve the stability and acceptance when provided to the power grid based on the forecasting results. The accurate forecasting of wind power is the key to this scheme. Common forecasting methods include physical approach and statistical approach. For physical approach, the Numerical Weather Prediction (NWP) model forecasts the wind speed and direction, so that the power is directly calculated. For statistical approach, the wind power is forecasted according to historical time series and predicted weather. The forecasting model including statistical autoregressive models, support vector machines, and neural networks, etc. However, the single model prediction lacks robustness, so we propose to ensemble multiple models to achieve robust short-term wind power forecasting. Based on supercomputing, we propose a large-scale parallel forecasting of various methods and models and a statistical method to assign reasonable weights to various models, achieving the large-scale ensemble of various models. In order to adapt to real-time data distribution changes, we update the models and corresponding weights online. The experiments are conducted on a dataset of wind power generation and meteorology from a province in China. The experimental results show that compared with the single model, the proposed large-scale ensemble model achieves better forecast accuracy and robustness.

show abstract

Section: Soft Sensorsmentioning

confidence: 99%

Large-scale ensemble learning for robust wind power forecasting with supercomputing

Long¹,

Wei²,

Zhong³

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the performance of KELM is influenced by the selection of kernel function and its parameters, and a careful selection process should be based on the characteristics of the data and the specific problem. The flowchart shown in FIGURE 6 represents the KELM optimization based on IGWO algorithm [35].…”

Section: Kernel Extreme Learning Machine (Kelm)mentioning

confidence: 99%

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

Ravi

Kannaiah

2023

IEEE Access

View full text Add to dashboard Cite

Power quality disturbances (PQDs) in modern electrical power systems, caused by the integration of nonlinear power electronic devices and erratic distributed generation (DG), lead to interruptions and significant energy losses for end users. However, conventional methods for PQDs classification face challenges in dealing with noise interference and feature selection. To address these challenges, this research paper proposes a novel approach that combines the stockwell transform (ST) with an improved grey wolf optimization-based kernel extreme learning machine to enhance classification accuracy. The stockwell transform is utilized to extract meaningful features from the power quality (PQ) signals, which are subsequently input into the kernel extreme learning machine (KELM). Furthermore, the parameters of the KELM model are optimized using the improved grey wolf optimization (IGWO) approach to improve the accuracy of classification. To evaluate the performance of the proposed method, real-time implementation is considered by incorporating PQDs data with signal-to-noise ratios (SNR) of 20 dB, 30 dB, and 40 dB into the original synthetic signals. Multiple noise conditions are simulated to assess the proposed model ability to identify and classify disturbance signals. The results demonstrate a detection accuracy of 99.76% under noiseless conditions, indicating the model's high accuracy. Moreover, the proposed method exhibits robustness to noise, achieving accuracies of 98.86%, 98.32%, and 97.3% at SNR levels of 40 dB, 30 dB, and 20 dB, respectively. In the end, this work has performed a comparative study with other previously published work. Compared with other classification methods, the algorithm proposed in this paper has higher accuracy, and it is an efficient and feasible classification method.INDEX TERMS Power quality disturbances, stockwell transform, improved grey wolf optimization, kernel extreme learning machine, decision tree, confusion matrix.

show abstract

“…The pitch command values are often the same for all three blades in collective pitch control, and the pitch control loop uses the rotor speed as feedback [84][85][86]. Furthermore, the rotor mechanical output is used directly as feedback [87][88][89]. In addition, discrete pitch control techniques are also the subject of on-going research explicitly aimed at reducing the load while maintaining the output power at its rated value.…”

Section: Review Of Pitch Control For Super-large Wtsmentioning

confidence: 99%

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

et al. 2022

View full text Add to dashboard Cite

As the impacts of environmental change become more severe, reliable and sustainable power generation and efficient aerodynamic power collection of onshore and offshore wind turbine systems present some of the associated key issues to address. Therefore, this review article aims to present current advances and challenges in the aerodynamic power extraction of wind turbines, associated supporting technologies in pitch, yaw, and torque control systems, and their advantages and implications in the renewable energy industry under environmental challenges. To do this, first, mathematical modeling of the environmental characteristics of the wind turbine system is presented. Next, the latest technological advances consider the environmental challenges presented in the literature, and merits and drawbacks are discussed. In addition, pioneering research works and state-of-the-art methodologies are categorized and evaluated according to pitch, yaw, and torque control objectives. Finally, simulation results are presented to demonstrate the impact of environmental issues, improvement claims, findings, and trade-offs of techniques found in the literature on super-large wind turbine systems. Thus, this study is expected to lay the groundwork for future intensive efforts to better understand the performance of large-scale wind turbine systems in addressing environmental issues.

show abstract

Research on variable pitch control strategy of direct-driven offshore wind turbine using KELM wind speed soft sensor

Cited by 14 publications

References 29 publications

Large-scale ensemble learning for robust wind power forecasting with supercomputing

Large-scale ensemble learning for robust wind power forecasting with supercomputing

Detection and Classification of Power Quality Disturbances Using Stock Well Transform and Improved Grey Wolf Optimization-Based Kernel Extreme Learning Machine

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

Contact Info

Product

Resources

About