Wind Farm In China Research Articles

Optimizing China's onshore wind farm layout crucial for carbon neutrality

China is taking renewable energy seriously for meeting ambitious carbon neutrality goal, and eagerly filling available space to harness the wind power. It is therefore crucial to rethink the unintended consequences. Herein, we employ the life cycle assessment (LCA) approach to assess the onshore wind farm from the perspective of aligning carbon neutrality goal in China. We find that the total carbon emissions of onshore wind farm in China in 2018 was ca. 11 Mt. CO2e, or 24.9 gCO2e per kWh on average, which indeed mitigated ca. 290 Mt. CO2e for replacing conventional coal power. Moreover, province-level carbon emissions and emission intensity outputs have been quantified. It shows that China's coastal provinces are preferred regions for the development of wind power industry after evaluating of China's onshore wind farms layout under multiple indicators. However, if existed wind farm layout mode which presents a significant conflict between resource potentials and demand market can be optimized, a scenarios-based analysis shows that 2.4 to 6.2 billion metric tons CO2e can be probably avoided every year between 2023 and 2060. This amount probably equals over half of the carbon emissions from fuel combustion in China.

Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting

Wind speed is the dominant meteorological factor affecting wind turbine power generation. Existing wind speed fluctuation division algorithms only focus on the wind speed changing process, but strong uncertainty exists in the wind speed–power conversion, which has been demonstrated as a multi-power transfer relationship under wind speed fluctuation. Therefore, a wind speed power transfer fluctuation partitioning (PTFP) algorithm is proposed, which fully considers the wind speed variation and power transfer characteristics to refine the modeling of wind power forecasting. The power transfer characteristics are investigated according to the wind speed–power conversion error of a wind farm. The measured wind speeds from three sources and fuzziness under different fluctuation trends are discussed based on the scatter distribution and probabilistic wind speed power curve. The model errors under different changing trends are quantitatively analyzed. Subsequently, the operation data of two wind farms in China are divided using the PTFP algorithm. The modeling error is reduced compared to that without considering the power transfer characteristics and the rationality of the proposed algorithm is demonstrated. The PTFP algorithm is also applied to wind power forecasting to verify its applicability. The results demonstrate that the proposed model can improve prediction accuracy on all time scales.

Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar.

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer.

Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.

A Review of Road Design for Wind Farms in China

Complex terrain conditions of wind farms, the large weight and the long size of wind turbine equipment, the high economic requirements of transportation on roads, and a shortage of relevant standards and codes lead to difficulty in road design and route selection of wind farms. In addition, frequent excavations and landfills in wind farm construction have seriously destroyed vegetation cover and soil structure on the primary surface, resulting in a large amount of water and soil loss and ecological damage to the wind farm. In view of the above problems, this paper reviews the relevant research status of the wind farm road route selection method, circular curve, longitudinal section design index, and water and soil loss problems, and summarizes them. Several calculation models for circular curve widening, the recommendation of a minimum radius under different restrictions, calculation models for longitudinal slope and vertical curve, and recommended values are listed. The causes of water and soil losses in wind farms are discussed and the characteristics of the diversity of erosion and the uneven distribution of time and space are put forward. Prediction models of wind farm water and soil loss and comprehensive prevention measures are given. The existing problems in the road design field of wind farms are put forward and the future development direction in this field is forecasted.

A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model

The wind farm parameterization in the mesoscale numerical weather prediction (NWP) model is a valuable approach to exploring the performance and atmospheric impact of the large-scale wind farm. However, the sub-grid interactions between turbines are not resolved in the current parameterization method. In this study, we propose an improved wind farm parameterization by coupling a novel sub-grid wind turbine interference model. It is incorporated in the state-of-the-art Weather Research and Forecasting (WRF) model. To verify the model, the coupled model is applied in a real offshore wind farm in China for comparing the model performances with the original parameterization. The findings demonstrate that the proposed approach improves the wind simulation and has minimal sensitivity to horizontal grid resolution, hence strengthening the model’s robustness. Additionally, it is discovered that the sub-grid interaction reduces the turbine-produced turbulence and shortens the wake length by more than 30% compared to the original parameterization. Importantly, the power output prediction of turbine cells is modified, approximately resulting in a 1 % reduction of the entire wind farm relative to the original model. The effects of sub-grid turbine interaction are effectively captured in the proposed model and it is recommended for further wind farm simulations.

Wind speed interval prediction based on multidimensional time series of Convolutional Neural Networks

Wind power, as an economical and clean energy source, has rapidly infiltrated into modern power grids. Wind speed prediction is a pivotal technology for wind power integration, which has attracted much attention from researchers and practitioners. Nevertheless, traditional methods only focus on point prediction, which is far from meeting the requirements of power system risk assessment and risk control. To fill this gap, we propose an interval prediction and analysis system based on data preprocessing, deep learning, convolutional neural networks, and interval prediction. It can effectively predict and analyze the uncertainty in short-term wind speeds. First, an original wind speed sequence is divided into high-frequency and low-frequency data using a data preprocessing strategy. After deleting the high-frequency noise data, the denoised multidimensional time series is entered as input into an optimized convolutional neural network. Subsequently, the lower and upper bounds of the interval prediction are obtained using the interval construction principle and variance of the training data set. To verify the effectiveness of the proposed hybrid system, a 10 min wind speed data set of the Shandong wind farm in China is used as an example for analysis. The experimental results show that the proposed hybrid prediction system can not only effectively predict the variation trend of wind speed series, but also accurately predict the local characteristics and volatility of wind speed with strong randomness.

Research of a combination system based on fuzzy sets and multi-objective marine predator algorithm for point and interval prediction of wind speed.

Short-term wind speed forecasting is fundamental to improving the stability of power grid operation and enhancing its transmission efficiency; thus, it has long been a research hotspot. Nonetheless, quantities of literature in this field only used the single prediction model and overemphasized deterministic prediction, which resulted in deficient forecasting performance. To address these issues, a novel point and interval combination prediction system was developed in this paper. Specifically, wind speed time series were reconstructed by dividing windows and fuzzification to input highly effective data; next, four single prediction models and a multi-objective weight-determining mechanism were integrated to obtain the point prediction results; and their distributions were assessed to implement interval prediction under distinct confidence levels. In the meantime, this study demonstrated that the proposed system reached the Pareto optimal by the theoretical proof, and empirical research was conducted based on 10-min real wind speed data from the wind farm in China. Judging from the experimental results, the combination prediction system was always capable of providing the most satisfactory forecasting performance by contrast with the comparative models. Consequently, it has broad application prospects in guiding the operation of wind farms and optimizing the power grid dispatching.

A deep generative model based on CNN-CVAE for wind turbine condition monitoring

Auto-encoder (AE)-based condition monitoring (CM) methods for fault detection of wind turbines have received considerable attention due to their powerful feature extraction ability. However, traditional AE-based monitoring methods can only learn point-to-point features by minimizing reconstruction errors, which leads to a low sensitivity to anomaly data and weak robustness to noise data. To this end, we introduce a novel deep generative method based on the convolutional neural network (CNN)-conditional variational auto-encoder (CVAE). The key idea of CNN-CVAE is to unify the representation learning capacity of the CVAE and CNN. Specifically, CVAE can learn a probability distribution model by being trained on an anomaly-free supervisory control and data acquisition systems (SCADA) dataset; CNN and deconvolution operations are adopted for better time-series feature extraction and reconstruction performance. A statistical process control chart is applied to determine the alarm threshold. The effectiveness of the CNN-CVAE-based method is validated by datasets collected by SCADA installed in a commercial wind farm in China for impending blade breakage and gearbox failure. Abundant experiments with state-of-the-art deep learning-based CM methods are conducted, which indicate that our proposed method outperforms other methods in robustness, fault detection data sensitivity, fault warning time, and model parameters.

Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.

A new compound structure combining DAWNN with modified water cycle algorithm-based synchronous optimization for wind speed forecasting

With the aims to improve the forecasting performance, a novel hybrid model based on variational mode decomposition (VMD), phase space reconstruction (PSR), improved water cycle algorithm (WCA) and double activation function wavelet neural network (DAWNN) is established for wind speed forecasting. In the proposed wind speed forecasting model, VMD is firstly employed to decompose the original wind speed time series into different modes and PSR is utilized to construct appropriate input matrix of each mode for DAWNN. To take advantage of different activation functions, DAWNN with optimal combination of Mexican hat function and Morlet wavelet function is constructed to make short-term wind speed forecasting. Then, the proposed improved WCA based on the chaos initialization of population, exploration–exploitation synergy optimization strategy and adaptively adjusting the search intensity, is developed to optimize the reconstruction parameters of PSR, hidden node number and the weighted coefficients in DAWNN synchronously. To confirm and evaluate the forecasting performance of the proposed hybrid model, two sets of historical wind speed samples from a wind farm in China are employed to make multi-step short-term wind speed forecasting. The experimental results illustrate that the proposed compound structure is effective when applying in wind speed forecasting.

Where is the most feasible, economical, and green wind energy? Evidence from high-resolution potential mapping in China

China has ambitious renewable energy development plans after undergoing a rapid development phase for wind power. China's prospects for wind development affect the global decarbonization path. The question of where to deploy turbines of this magnitude to achieve technical feasibility, high economic competitiveness, and high emission reduction has become an urgent issue for China. Due to regional differences in wind resources and economic development, the deployment of wind power in China requires clarity on the likely siting and investment costs. It requires an accurate assessment of the exploitable possibilities of wind energy resources in China, which provides practical solutions for national and regional wind energy development. In this study, a multi-potential assessment was developed to estimate the feasibility and emission reduction contribution of wind power using a combined GIS-based approach and learning curves. It is found that there are large areas of land and ocean available for wind farms in China, especially the desert in the northwest and the coastal area of Fujian province. The technical potential of onshore and offshore wind power is 8650.33 TWh/yr and 11298.9 TWh/yr, respectively. Moreover, 76.06% and 20.69% of onshore and offshore wind, respectively, could be supplied economically. The optimal areas for building onshore wind by 2030 are located in Inner Mongolia, Gansu, and Xinjiang, while the optimal construction area for offshore wind power is mainly the coastal area of Fujian. Wind energy has a large potential to meet China's electricity demand and is set to be more cost-competitive as time passes. The cost of onshore and offshore wind will decrease by 25–53% and 21–46%, respectively, by 2030. Wind development could contribute 7823.73–10723.50 Mt CO2 reduction during 2021–2030, which could help decarbonize China's energy system.

Green development strategy of offshore wind farm in China guided by life cycle assessment

Offshore wind power contributes to the decarbonization of the power system; however, its green development faces many challenges, including environmental impact concerns, scale limitations, and grid parity to go carbon neutral. This study applies life cycle assessment (LCA) to evaluate the potential environmental impact of China's first high-power prototype windfarm. The environmental impact of the wind farm is more sensitive to steel, copper, and electricity usage. The recycling of waste turbines can be a solution to reduce the environmental impacts of not only the wind farm but also other entities such as metal producers. Overall, this wind farm case can reduce energy consumption and greenhouse gas emissions by 9.23 MJ and 767.9 g CO2-eq for 1 kWh electricity produced, respectively. Guided by LCA results, green development strategies that include toward the deep sea, promote industrial upgrading and synergy, and advance demonstration projects are discussed in the offshore wind power industry.

Wind power probabilistic forecasting based on combined decomposition and deep learning quantile regression

With the expansion of scale of the grid-connected wind power, wind power forecasting plays an increasing important role in ensuring the security and steady operation and instructing the dispatch of power systems. In consideration of the randomness and intermittency of wind power, the probabilistic forecasting is required in quantifying the uncertainty of wind power. This study proposes a probabilistic wind power prediction method that combines variational modal decomposition (VMD), singular spectrum analysis (SSA), quantile regression (QR), convolutional neural network (CNN) and bidirectional gated neural network (BGRU). Firstly, a combination decomposition method VMDS combining VMD and SSA is proposed to decompose wind power sequence to reduce the complexity of the sequence. Next, a feature extractor based on CNN and BGRU (CBG) is used to extract complex dynamic features of NWP data and high-frequency components. Then, the QR is performed by the BGRU based on the high-order features to obtain the predicted values for different quantiles. Finally, the kernel density estimation (KDE) is employed to estimate the probability density curve of wind power. The proposed model can achieve reliable probabilistic prediction while achieving accurate deterministic prediction. According to comparisons with related prediction models, the effectiveness of the proposed method is verified with the example test using datasets from the wind farm in China.

Multi-step wind speed forecasting model using a compound forecasting architecture and an improved QPSO-based synchronous optimization

The intermittency and randomness of wind speed time series influence the forecasting accuracy. To figure out this problem and enhance the forecasting performance, a novel compound structure is developed for short-term wind speed forecasting. The developed compound method firstly eliminates the inherent noise from the original empirical wind speed time series using wavelet packet decomposition (WPD) and subsequently constructs appropriate input matrix by phase space reconstruction (PSR) for multi-kernel least square support vector machine (MKLSSVM). To take advantage of different kernel function, MKLSSVM with optimal combination of radial basis kernel function, polynomial kernel function and linear kernel function is constructed to make wind speed forecasting. Then, the proposed improved quantum particle swarm optimization algorithm (QPSO) based on the chaos initialization, Gaussian distribution local attraction points, precocity judgment, and disturbance operator, namely ADQPSO, is employed to optimize the decomposition level of WPD, reconstruction parameters of PSR, kernel parameters and weighted coefficients in MKLSSVM synchronously. To evaluate the forecasting performance of the proposed hybrid model, four sets of historical wind speed data samples from Weihai wind farm in China are utilized to make multi-step short-term wind speed forecasting tests. The experimental results illustrate that the proposed hybrid model outperforms the compared single and new recently developed forecasting models, thus, the proposed WPD-PSR-ADQPSO-MKLSSVM is an effective method for short-term wind speed forecasting.

Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method

To ensure the operational reliability of power systems, it is important for wind speed signal forecasting systems of wind turbines to be efficient, accurate and stable. This paper proposes a two-phase deep learning structure with network augmentation and pruning. By introducing the cross-correlation and quasi-convex optimization, a fractional quadratic programming problem and related convex optimization models are constructed to generate the augmented data for this proposed internal network; by pruning weakly correlated convolution channels, the redundant features of its external network are reduced. Furthermore, the closed-form solution of the convex optimization model is derived, which reduces the computational complexity considerably from O(n*log(2N)) to O(n). The proposed approach has been extensively validated using the real data of the wind farm in China. The results of the numerical experiments demonstrate that the proposed method achieves the superior performance in the training flexibility, model accuracy, stability, and interpretability.

Research on Normal Behavior Models for Status Monitoring and Fault Early Warning of Pitch Motors

Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor.

Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy

The construction of wind-energy storage hybrid power plants is critical to improving the efficiency of wind energy utilization and reducing the burden of wind power uncertainty on the electric power system. However, the overall benefits of wind-energy storage system (WESS) must be improved further. In this study, a dynamic control strategy based on the state of charge (SOC) for WESS is proposed to maintain a healthy SOC for energy storage system (ESS). Then, four scenarios with different operation strategies are set based on the historical operation data of a wind farm in China. Particle swarm optimization is used to solve the problem, with the goal of optimizing the overall system benefit. The results show that the dynamic control strategy is the most cost-effective while maintaining reliability. When compared to other scenarios, the profit of the dynamic control strategy is extended by 7.63 %, 327.69 % and 9.75 % respectively, and the energy storage life is extended by 10.02 %, 62.89 % and 21.61 % respectively, demonstrating that different working state of energy storage will significantly affect the storage life and, ultimately, affect the economic benefit of the system. Finally, the influences of feed-in tariff, frequency regulation mileage price and energy storage investment cost on the optimal energy storage capacity and the overall benefit of wind-energy storage hybrid power plant are discussed.

A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance.

Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy

Distributed joint complementary wind generation is a powerful way to reduce the variability and improve the penetration of wind power. But distributed wind farm siting is a complex problem of nonlinear and global combinatorial optimization. Various correlation coefficients were used to measure the complementarity of renewable energy, which were not intuitive and accurate in former studies, and traditional siting methods are based on the abundance of wind energy, siting results cannot fully utilize the complementary potential of distributed wind energy. In this study, intelligent optimization algorithm is adopted to select the location of distributed wind farm in China mainland from the perspective of utilizing the complementarity of wind energy. The result showed that the existing wind power bases have obvious complementary superiority, combined power generation can improve availability from about 57.61% to 66.93%, reduce the long/short term variability confidence by about 40%. Siting result indicated that central and eastern Inner Mongolia, Xinjiang and Hexi Corridor are still key regions for the future wind farms construction. This study provides more direct evidence of the complementarity of existing wind power bases, and the proposed siting methodology can be applied in different regions worldwide.