Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network

Zhu, Anfeng; Zhao, Qiancheng; Wang, Xian; Zhou, Ling

doi:10.3390/en15093055

Cited by 26 publications

(12 citation statements)

References 36 publications

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“…Wind energy is a clean energy source and has attracted considerable attention for satisfying the ever-increasing demand for energy [1]. Wind turbines (WTs) are used for conversion of wind energy into electricity and provides stable electricity to the grid effectively.…”

Section: Introductionmentioning

confidence: 99%

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Zhu¹,

Zhao²,

Gui³

et al. 2023

Energy Engineering

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To ensure wind turbine (WT) safe operations and improve the utilization rate of wind energy, effective evaluation of the operation state of the pitch system is critical. Therefore, a new method was proposed to evaluate the operation state of the pitch system of WT based on fuzzy comprehensive evaluation. Firstly, based on SCADA data, the working state of the pitch system under rated power state and power state of WT were analyzed. Secondly, through the analysis of characteristic parameters and physical mechanism of the pitch system, the consistency principle of characteristic parameters, the stability principle of power under rated state, and the stability principle of blade angle underpowered state were obtained. Next, based on the aforementioned principles, the evaluation indexes were established, and the fuzzy comprehensive evaluation method was used to establish the operation state evaluation model of the pitch system under rated power state and under power state of the WT. Finally, an example was provided to verify the effectiveness of the method. The evaluation model established in this study can be used as a technical reference for the online monitoring of WT pitch systems to ensure the safe and stable operation of WTs.

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Section: Introductionmentioning

confidence: 99%

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Zhu¹,

Zhao²,

Gui³

et al. 2023

Energy Engineering

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“…Chen et al [2] proposed an algorithm for a multi-objective artificial bee colony and used it to optimize the key parameters of a wavelet neural network; after constituting a prediction model for probabilistic interval prediction of power, it has good prediction results. Zhu [3] introduced the whale optimization algorithm (WOA) to improve the weight thresholds for the Elman model. The study constructed a forecasting model and then used it to forecast the high volatility and strength nonlinearity of wind power time series.…”

Section: Introductionmentioning

confidence: 99%

Elman short-term wind power prediction based on the optimized seagull algorithm

Sui,

Liu,

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Accurate prediction of wind farm power output can relieve the pressure of grid frequency regulation and peak regulation and improve grid stability. With the goal of improving power prediction accuracy and reducing overall prediction error, this paper proposes an Elman short-term wind power prediction model on the basis of an optimized seagull algorithm. Firstly, the Elman network is used as the base prediction model, and the seagull algorithm is applied to seek the best values for its weights. Secondly, the chaotic circle mapping with better initial characteristics is improved to equalize its sequence distribution for optimizing the population initialization. Then, to address the lack of local search capability, an optimized iterative approach using the sine cosine operator is used to achieve a balance between local exploitation ability and global search capability. Finally, after simulation and analysis of the actual data set, it is verified that the model has a better prediction effect.

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“…However, the effects of the particle swarm's own weights and learning rate on the global optimal solution were not considered. In addition, in recent years, some improved advanced algorithms have been proposed for load forecasting [16][17][18][19][20], and we will be showing a comparison between available and proposed technology and highlighting the novelty and advantages of these in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction

2022

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The accuracy of wind power prediction is crucial for the economic operation of a wind power dispatching management system. Wind power generation is closely related to the meteorological conditions around wind plants; a small variation in wind speed could lead to a large fluctuation in the extracted power and is difficult to predict accurately, causing difficulties in grid connection and generating large economic losses. In this study, a wind power prediction model based on a long short-term memory network with a two-stage attention mechanism is established. An attention mechanism is used to measure the input data characteristics and trend characteristics of the wind power and reduce the initial data preparation process. The model effectively alleviates the intermittence and fluctuation of meteorological conditions and improves prediction accuracy significantly. In addition, the modified particle swarm optimization algorithm is introduced to optimize the hyperparameters of the LSTM network, which speeds up the convergence of the model dramatically and avoids falling into local optima, reducing the influence of man-made random selection of LSTM network hyperparameters on the prediction results. The simulation results on the real wind power data show that the modified model has increased prediction accuracy compared with the previous machine learning methods. The monitoring and data collecting system for wind farms reveals that the accuracy of the model is around 95.82%.

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Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network

Cited by 26 publications

References 36 publications

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Operation State of the Wind Turbine Pitch System Based on Fuzzy Comprehensive Evaluation

Elman short-term wind power prediction based on the optimized seagull algorithm

Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction

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