Traditional Wind Power Research Articles

Design of an axial field direct-drive tidal stream generator

Abstract Tidal stream energy generation is a technology that harnesses the kinetic energy of tidal or ocean currents to produce electricity. Unlike traditional thermal and wind power generation, tidal stream energy generation relies on the natural movement of seawater for energy conversion. Due to the periodic and predictable nature of tides and currents, tidal stream energy generation offers high stability and reliability. This paper introduces the design of a micro-scale tidal generator with the power of 300W. An appropriate electromagnetic configuration was chosen, and the initial materials and electromagnetic structure of the generator were determined. Three-dimensional simulations were performed using finite element analysis. The design parameters of the prototype are reasonable, and all performance indicators meet the design requirements, confirming the feasibility of this generator for tidal stream energy generation.

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.

Research on the application of computer science in wind power generation and the future trends

In the context of the growing global demand for clean energy, wind power, as an important form of renewable energy, is gradually occupying a dominant position. At the same time, the continuous development of computer technology has also brought new breakthroughs in wind power generation. Traditional wind power generation suffers from low power generation efficiency and large fluctuations in power generation. However, through the integration of wind power generation with advanced computer technologies, the efficiency and cost of wind power generation have been greatly optimized. This paper seeks to provide an overview of how computer science is utilized in wind power generation, focusing on the integration of computational modeling, simulation, and machine learning. It highlights future trendsand emphasizes the significance of developing digital twins and integrating machine learning with physical models for efficient wind farm management. Combining findings from various existing studies, this paper delves into the profound implications for the future of energy, highlighting the transformative role of computer science in advancing sustainable power solutions and shaping the landscape of renewable energy sources.

Research on Collaborative Evaluation of Offshore Wind Power Value Chain in Guangxi-Based on BWM-FVIKOR Model

With the dynamic development of the offshore wind power industry and the global value chain, the traditional wind power industry is gradually turning to the offshore wind power value chain system that focuses on the value effect of the value chain, and it is of great significance to carry out value chain collaborative evaluation research. This paper puts forward the evaluation index system of Guangxi offshore wind power value chain from the comprehensive system dimension, key node dimension, subject contribution dimension and resource endowment dimension, builds the BWM-FVIKOR evaluation model, and proves the validity of the evaluation index and the feasibility of the evaluation model through example analysis, and this paper provides scientific and effective decision-making basis for the coordination of Guangxi offshore wind power value chain.

ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the graph attention mechanism, the features of the proximity node information of the individual wind turbines in the wind farm are extracted and the input feature matrix is constructed. Finally, the wind power probability density prediction results are obtained using the ForecastNet model based on three different hidden layer variants. The experimental results show that the ForecastNet model with a hidden layer as a dense network based on the attention mechanism (ADFN) predicts better. The average width of the prediction intervals at achieved confidence levels for all interval coverage is reduced by 34.19%, 35.41%, and 35.17%, respectively, when compared to the model with the hidden layer as a multilayer perceptron. For different categories of wind turbines, ADFN also achieves relatively narrow interval average widths of 368.37 kW, 315.87 kW, and 299.13 kW, respectively.

Research on Wind Power Prediction Based on A Gated Transformer

Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales.

Multi-objective genetic algorithm-based wind turbines control

With the development of science and technology, new energy technology has increasingly become a hot spot of academic research. Today, solar, nuclear, tidal and wind power generation is gradually replacing thermal power generation. Aiming at the configuration problem of traditional wind power generation, this research will optimize the control of wind turbine generator sets, so as to realize the configuration optimization of the entire distribution network. In this study, a MOG (MOG) algorithm control optimization method was introduced. First, each scenario is constructed, and components such as centralized wind power plants are configured and controlled through the internal power flow program; then, decision variables are defined, and three objective functions with trade-off relationships are constructed. Finally, the MOG algorithm is used for iterative solution, and the point with the lowest violation cost is selected as the optimal solution. Simulation experiments including three different configuration scenarios were conducted in MATLAB environment for comparing the influence of different connection modes on distribution network. According to experimental results, the effectiveness of the proposed method is verified and it is proved that the proposed method can be applied to complex distribution networks.

A Low-Pass Virtual Filter for Output Power Smoothing of Wind Energy Conversion Systems

As theincrease of wind power penetration, the fluctuations of wind power results in serious power system frequency turbulences. This article proposes a low-pass virtual filter (VF) for wind energy conversion systems (WECSs) to smoothen output power. First, a linearized model is established for a WECS. Then, a low-pass VF is derived for the active power control loop in WECS to modulate the kinetic energy stored in rotating mass for output smoothing. The principle is to modify the traditional wind power controller to emulate a physical energy storage system controlled as a low-pass filter to smooth the wind power output. The proposed low-pass VF does not need the physical deployment of energy storage system but still can filter the high-frequency fluctuations in the output wind power. Furthermore, in order to ensure operation stability of the wind turbine (WT), a stability-constrained coefficient is determined according to the proposed stability criteria to ensure the stability of WTs. Simulation and experiment results verify the effectiveness of the proposed VF in WECS for output wind power fluctuation reduction and the stability-constrained coefficient for stability ensuring.

Forecasting ultra‐short‐term wind power by multiview gated recurrent unit neural network

AbstractWind power generation prediction plays an important role in the safety and economic operation of the power system. There are many parameters recorded in wind farm data, such as wind power, wind speed, wind direction, and so on. Traditional wind power prediction modeling methods lack the mining of these parameter data and fail to make good use of some potential physical information. To address this challenge, this paper proposes a multiview neural network learning framework to predict wind power. One is the data attribute view of wind power, which uses the historical data feature of the wind power itself to learn the future wind power feature. The other is the physical attribute view of wind power, which uses the physical attribute features associated with the wind power definition to learn the future features. Then all the learned features are jointly fused to predict the future wind power values. In addition, an uncertain factor is proposed and computed, which is inspired by the wind power formula and usually associated with internal and external environment perturbation. All time series features are input into the gated recurrent unit neural network to form a hybrid neural network framework for wind power prediction. Experimental results under the measured condition and the standard condition of wind farms demonstrate the effectiveness of the proposed method.

A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network.

Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and uncontrollability, so it is challenging to predict wind speed accurately. Considering the shortcomings of traditional wind power point predictions, a new hybrid model comprised three main modules used for data preprocessing, deterministic point prediction, and interval prediction is proposed to predict the wind speed interval. The first module, the data preprocessing module, uses variational mode decomposition (VMD), sample entropy (SE), and singular spectrum analysis (SSA) to extract the different frequency components of the initial wind speed series. The second module, the deterministic point prediction module, uses extreme learning machines (ELM), and a gated recursive unit (GRU) model to perform point prediction on the wind speed series. The third module, the interval prediction module, uses the nonparametric kernel density estimation method to construct the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval is obtained by integrating the prediction results of multiple interval prediction results to improve the robustness and generalization of the wind speed interval prediction. Finally, the effectiveness of the prediction performance of the proposed hybrid model is verified based on the data of two actual wind farms. The experimental results show that the proposed hybrid model can obtain the appropriate wind speed interval with high confidence and quality with different confidence levels of 95%, 90%, and 85%.

A stacking-based short-term wind power forecasting method by CBLSTM and ensemble learning

Aiming at the problem that the traditional wind power forecasting is difficult to deal with a large amount of strong volatility data and limited processing capacity of time series, a wind power forecasting method based on multi-model combination under stacking framework was proposed. First, the wind turbine data are cleaned by density-based spatial clustering of applications with the noise clustering method. Considering the differences of data observation and training principles, the proposed stacking method embedded multiple machine learning algorithms to utilize their diversified strength. The stacking base-learner includes the CBLSTM model, which has the advantages of deep architecture feature extraction, and takes into account data timing and nonlinear relationship as well as XGBoost and other tree ensemble learning models that were suitable for complex data modeling. The feasibility of the algorithm was verified by using the actual wind power data of two wind farms in Northeast and Western China. Experimental results show that the stacking ensemble learning method proposed has better forecasting performance and stability than other single forecasting models, which is of great significance to guide wind power dispatching operation and improve wind power consumption capacity.

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s.

Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid dispatching and grid planning. To address the problem of the low accuracy of traditional wind power interval prediction, a new interval prediction method of wind power is proposed based on PSR-BLS-QR with adaptive rolling error correction. First, one-dimensional wind power data are mapped to high-dimensional space by phase space reconstruction (PSR) to achieve data reconstruction and the input and output of the broad learning system (BLS) model are constructed. Second, the training set and the test set are divided according to the input and output data. The BLS model is trained by the training set and the initial power interval of training data is constructed by quantile regression (QR). Then, the error distribution of nonparametric kernel density estimation is constructed at different power interval segments of the interval upper and lower boundaries, respectively, and the corresponding error-corrected power is found. Next, the optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries. Finally, a test set is used for testing the performance of the proposed method. Three wind power datasets from different regions are used to prove that the proposed method can improve the average prediction accuracy by about 6–14% with the narrower interval width compared with the traditional interval prediction methods.

An easy and efficient power generator with ultrahigh voltage for lighting, charging and self-powered systems

Can you imagine that when you slide on the polymer plate with your hands, the generated electromagnetic field can drive 28-watt fluorescent tube lamps and can even charge the mobile phone? The progress of science and technology subverts traditional cognition. Here, an easy and efficient power generator (EEPG) with ultrahigh voltage is invented, which can light up 28-watt fluorescent tube lamps. The average output voltage and current of planar EEPG can reach up to nearly 5000 V and 116 μA, respectively. Not only can it be used for lighting, but also for charging, and even for mobile phones. The rotary EEPG can be used to harvest wind energy and tidal energy, which overcomes the shortcomings of traditional electromagnetic wind power generation working only when the wind speed is greater than 5 m/s. In addition, the EEPG power generator can be utilized as a footswitch and trajectory tracking tool. This is likely to become an easy and effective method of power generation in the world. It can be expected that mobile phones can be charged by just rubbing their backs. Collecting the green wind energy more easily and efficiently will be truly realized.

Online Ensemble Approach for Probabilistic Wind Power Forecasting

Probabilistic wind power forecasting is an important input in the decision-making process in future electric power grids with large penetrations of renewable generation. Traditional probabilistic wind power forecasting models are trained offline and are then used to make predictions online. However, this strategy cannot make full use of the most recent information during the prediction process. In addition, ensemble learning is recognized as an effective approach for further improving forecasting performance by combining multiple forecasting models. This paper studies an online ensemble approach for probabilistic wind power forecasting by taking full advantage of the most recent information and leveraging the strengths of multiple forecasting models. The online ensemble approach is first formulated as an online convex optimization problem. On this basis, a quantile passive-aggressive regression model is proposed to solve the online convex optimization problem. Case studies and comparisons with other online learning methods are conducted on an open wind power data set from Belgium. Results show that the proposed method outperforms competing methods in terms of pinball loss and Winkler score with high reliability.

Assessing Institutional Readiness of East-Asian Countries to Pass over to Technologies of Hydrogen Power Engineering

Such states of East Asia as China, Japan, the Republic of Korea depend seriously on import of primary power resources. No wonder, they do their best to extend possibilities of renewable power engineering as the industry, which could cut volumes of crude oil, natural gas and coal import and provide higher power independence. The accident at the Fokusima-1 nuclear power station resulted in re-estimation of the place and role of nuclear power stations in the power generation structure not only in Japan. It formed another ground for accelerated introduction of renewable power engineering technologies. Apart from traditional solar and wind power stations we can see brand new solutions, among them the use of hydrogen for power needs. These technologies came into being in the 1960s – 1970s but only in the 2000s and later the acceptable level of security was reached as well as, at least partial commercial efficiency of using hydrogen as a power source in industry, municipal sector and transport. The article provides information about the readiness of formal institutions in the mentioned-above countries to pass over to hydrogen power plants, it also studies the present day investment mechanisms of supporting business in this field. To do this we should analyze the legal base of activity regulation in the sphere of production and consumption of hydrogen for power needs in China, Japan and the Republic of Korea and to compare results attained in this direction. This analysis could estimate a potential niche for Russia as an exporter of power hydrogen. Russia possesses all necessary resources for getting green hydrogen with the help of the hydrolysis method at hydroelectric stations. It is an acute trend as the ecologic situation in East-Asian states is continuously deteriorating.

Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models

Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.

Risk assessment of offshore wave-wind-solar-compressed air energy storage power plant through fuzzy comprehensive evaluation model

As a promising offshore multi-energy complementary system, wave-wind-solar-compressed air energy storage (WW-S-CAES) can not only solve the shortcomings of traditional offshore wind power, but also play a vital role in the complementary of different renewable energy sources to promote energy sustainable development in coastal area. However, as a new type of energy conversion system, the construction and operation risks of WW-S-CAES project faced by project managers are variable, complex and long term, which requires synthetic assessment and target responses. To evaluate WW-S-CAES project risk along low carbon development in this paper, 14 critical criteria in management, economy and environment are firstly identified. Then, a target risk assessment framework is established through hesitant fuzzy linguistic term sets improved triangular fuzzy number and fuzzy comprehensive evaluation method. Result indicates that the risk level of offshore WW-S-CAES is closer to middle high, and the management risk and economy risk occupy the determinant position. Based on this, targeted risk response strategies are proposed for decision makers to realize reasonable allocation of resources and appropriate risk aversion. Furthermore, since the risk analysis of offshore WW-S-CAES power plant has not been paid enough attention, this paper expands the knowledge and contributes to the literature.

GMM-HMM-Based Medium- and Long-Term Multi-Wind Farm Correlated Power Output Time Series Generation Method

Medium- and long-term wind power output time series are required in stochastic programming model for power system planning. Hidden Markov model (HMM) is a common method to generate wind power output time series, which can simultaneously consider the temporal and spatial correlation of multiple wind farms. However, the existing HMM methods use discrete matrix or Gaussian distribution to describe the output distribution of multiple wind farms, which usually leads to a relatively large error in statistical indices between the generated time series and the historical time series. Therefore, this paper proposes a method for generating medium- and long-term correlated output time series of multiple wind farms based on the Gaussians mixture model-Hidden Markov model (GMM-HMM). The discrete state variable in the hidden Markov model is used to describe the meteorological state. The Markov chain between discrete state variables is used to describe the temporal correlation of wind power output. The wind power output vector of multiple wind farms is used as the observation variable, and the mixed Gaussian probability distribution mapping relationship between the state variable and the multidimensional wind power output vector is established. Based on the Monte Carlo sampling method, the multi-wind farm output series satisfying the spatiotemporal correlation of historical output series are generated monthly. In the calculation example, the monthly wind power output series generated by five wind farms in Jilin Province are analyzed. The results show that the main statistical characteristics of the multi-wind power output time series generated by the proposed method are generally superior to those obtained with the traditional wind power output modeling method, which proves the superiority of the proposed method.

A novel two‐stage interval prediction method based on minimal gated memory network for clustered wind power forecasting

AbstractAs the global demand for clean renewable energy has grown, the contribution of wind power in grid systems has significantly improved. Wind power predictions play an important role in the stable and safe operation of grid systems. Considering the shortcomings of traditional wind power point predictions, a novel two‐stage short‐term wind power interval prediction method is proposed in this study. In the proposed method, the minimal gated memory (MGM) network and improved interval width adaptive adjustment strategy, which is an approach that is designed to adjust the prediction interval (PI) labels, are combined for short‐term interval predictions of wind power. First, the point model for subsequence data based on the MGM network is proposed. Subsequently, the interval model is proposed to obtain the final PIs of wind power using the improved interval width adaptive adjustment strategy. Finally, with the purpose of verifying the prediction performance of the proposed model, two datasets and four representative benchmark models are implemented for comparative experiments. The superiority of the proposed model is demonstrated via experimental results, which show that the proposed model can obtain suitable wind power intervals with high confidence and quality.