Wind Power Cluster Research Articles

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters.

A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network

The uncertainty of the wind process leads to the randomness of its regional propagation, and the spatial correlation between nearby wind farms also shows a dynamic change tendency under the influence of wind direction. To consider the influence of dynamic spatial correlation on wind power prediction modeling, a short-term wind power prediction method based on a dynamic graph neural network is proposed. First, the topology graph of the wind farm cluster was established to represent the correlation of wind farms based on graph theory. Then, a dynamic spatiotemporal graph neural network was constructed to adapt graph topology by node embedding, which can explore the changing characteristics of spatial correlation among wind farms. Finally, we proposed a decoupling error model that can quantify the proportion of errors caused by the modeling process, which can assist in evaluating the performance of predictive models. We conducted experiments using data provided by the wind farm cluster in Inner Mongolia, and the average normalized Root Mean Square Error for the 12 wind farms was 0.1424, which verified the effectiveness of the proposed model.

Exploring spatio-temporal dynamics for enhanced wind turbine condition monitoring

Efficient wind power cluster management and sustainable industry development require an optimized maintenance approach integrating condition monitoring. However, the intricate spatiotemporal correlations and non-stationarity of the data continue to pose significant challenges in extracting valuable insights from the supervisory control and data acquisition (SCADA) system. Hence, this paper proposes a novel approach for Dynamically Fusing Spatio-Temporal information with Difference Excitation (DFST-DE) in wind power systems’ condition monitoring. First, it extracts spatial dependencies by integrating sparse graph structures with Gumbel Softmax regularization and incorporates temporal information using a hybrid adaptive self-attention mechanism. This combined approach effectively captures comprehensive spatiotemporal correlations. Second, it proposes a Spatio-Temporal Difference Excitation module to mitigate non-stationarity in time series data by smoothing trend changes. The proposed DFST-DE approach achieves superior performance by effectively fusing spatial, temporal and excitation information. Finally, it optimizes anomaly detection accuracy and efficiency by dynamically adjusting the threshold based on anomaly score statistics. Experimental results on real-world wind farm datasets demonstrate that the proposed approach outperforms other established methods in detecting early abnormal conditions in wind turbines.

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Integrating renewable wind power (WP) into the grid exacerbates variability and challenges reliability. Establishing an effective forecasting system is crucial for risk avoidance, but achieving effective, interpretable, and accurate predictions remains an obstacle due to the high volatility and uncertainty of WP. This study proposes an innovative and trustworthy wind power forecasting system combined with point and interval predictions. Specifically, the adaptive multi-scale convolutional receptive field (AMSCF) combined with gated recurrent unit (GRU) for WP point prediction is proposed to capture the spatiotemporal dependencies of WP generation. This AMSCF method uses different size receptive fields, fuses them using channel dimension concatenation, operates pooling layer and adaptive fusion through field attention, which extracts hierarchical features for WP forecasting. Then conditional mixture Copula (Con-mCopula) function integrated with multiobjective optimizer is established for WP interval prediction, which reduces the WP interval prediction accuracy error as some information neglected. The wind power cluster dataset from Germany was selected for experiments. Based on the results, for both datasets, the designed prediction system can achieve better point and interval prediction performance resulting in a reduction of mean absolute error (MAE) by approximately 40 % compared to other existing components in the market. This study deconstructs the “Black Box” in deep network for energy forecasting through interpretability analysis, which accelerates the development of AI technology in WP forecasting system and provides decision-makers with trustworthy WP forecasting assistance.

Impact of Gale Weather Events on Wind Power Generation

Abstract Due to the influence of global warming, extreme wind weather occurs frequently, especially in extreme weather such as typhoons and cold waves, problems such as wind turbine shutdown, cutting out, and sudden changes in power generation power will occur, which brings great challenges to wind power forecast and safe and stable operation of the power grid. At present, the research and analysis of strong wind weather mostly stay at the single fan level, and the causes of abnormal changes in wind power output are mostly analyzed from the physical level. However, with the large-scale access of wind power to the power grid, the impact of extreme weather on power grid operation is becoming more and more significant, and it is particularly important to study the impact of strong wind weather events on wind power cluster output. This paper summarizes various types of wind power generation weather events, then screens key meteorological factors affecting power generation under different wind weather conditions based on Gaussian search and grey correlation weighting, and finally analyzes the meteorological impact on the actual operation of wind farm stations and clusters under typhoons and cold waves respectively, aiming at the problem of huge deviations in wind power forecast caused by wind. The next research direction and suggestions are put forward.

Cooperative game-based energy storage planning for wind power cluster aggregation station

It is possible to cut down the investment costs in energy storage and enhance the utilization of energy storage by planning the shared energy storage in the wind farm collection station to replace the dispersed energy storage of each wind farm. Considering the cluster complementary effects of multiple wind farms, this article proposes a cooperative game-based plan for the hybrid energy storage of battery and supercapacitor in the wind power cluster. Firstly, charging and discharging strategy for batteries follows the peak shaving and valley filling approach, while the strategy for supercapacitors aims to flatten power fluctuations and track power generation plans. Then, a dual-layer planning model for the shared energy storage station is established, and evaluation indicators for the energy storage configuration results are constructed. Finally, based on the improved Shapley value method, the profits of each wind farm are allocated, and the impact of energy storage investment costs on the results is analyzed. The case study results demonstrate that under this cooperative scheme, not only can the real-time output deviation of wind power be reduced, but also the investment costs of wind farms in energy storage systems can be reduced.

Environmental and economic scheduling for wind-pumped storage-thermal integrated energy system based on priority ranking

With the rapid access of wind power clusters (WPC), it is difficult for the traditional active scheduling mode to take into account the security, economy and environmental protection of the power system. Pumped storage power station (PSPS), with its flexible regulation characteristics, can reduce the volatility of wind power and enhance the capacity of wind power accommodation to a certain extent. Therefore, this paper proposes an environmental and economic scheduling strategy for the wind-pumped storage-thermal integrated energy system, and rationally arranges the sequence of the units based on the principle of energy saving and emission reduction. The scheduling strategy involves optimizing operational decision-making for pumped storage units to enhance their regulation performance. Firstly, a pre-scheduling of the wind-pumped storage bundling system is carried out, aiming at achieving the highest wind power accommodation rate, the smallest output fluctuation and the lowest pumped storage scheduling cost. Next, the joint scheduling of the wind-pumped storage-thermal system is carried out, and the priority ranking method is used to arrange the generation schedule of the thermal units according to the optimal wind-pumped storage joint output. Finally, the proposed environmental-economic scheduling model is solved by the improved NSGA-II algorithm to verify the effectiveness of the scheduling strategy.

Ultra-short-term wind power forecasting based on feature weight analysis and cluster dynamic division

Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability.

Multitime Scale Reactive Power and Voltage Optimal Regulation for Transmission Network With Wind Power Cluster Based on Model Predictive Control

In order to solve the problem of voltage fluctuation caused by the grid integration of wind power cluster, a multitime scale reactive power and voltage optimal regulation method based on model predictive control (MPC) is proposed in this paper. In the day‐ahead stage, the reduction of network active power loss is mainly achieved through the regulation of discrete reactive power compensation devices and generator units, focusing on the economic operation of the power system. In the intraday stage, considering the rapid response characteristics of continuous reactive power compensation devices, the optimal regulation aiming to minimize voltage control deviation is carried out under 15‐ and 5‐min time scale based on the MPC algorithm with the adjustment of continuous reactive power compensation devices. In the feedback correction stage, a fast calculation method considering reactive power partitioning is adopted instead of solving the 5‐min optimization model for 288 periods, avoiding excessive adjustment of reactive compensation devices. While effectively improving voltage fluctuations caused by wind power prediction deviation, it also alleviated the computational and communication burden on the system. Finally, the effectiveness of the proposed regulation method in this paper is verified with the improved IEEE‐39 test case.

Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation

The intermittency and uncertainty of wind energy affect the accuracy of wind power prediction, which is not conducive to the safe and stable operation of the power system. Aiming at this problem, a short-term power prediction method for multi-Stacking wind power clusters based on dual feature extraction and quadratic decomposition aggregation is proposed. Firstly, a graph network matrix is built based on the spatial relationship of wind farm clusters to extract spatial features, and then a subset of features is filtered using a feature selection method combining filter and wrapper. The original signal is then decomposed using a quadratic decomposition method combining variational modal decomposition and swarm decomposition to reduce the volatility of the signal; the wind power signals are reconstructed using multiscale permutation entropy, and finally the reconstructed signals are fed into multi-Stacking models for information modelling depending on the frequency to predict rolling multi-step power of wind farm clusters. A real wind farm cluster arithmetic example is used for validation. The experimental results show that NRMSE, NMAE, and MAPE are improved by 15.8 %, 8.99 %, and 6.79 % compared with the traditional model. The method has important reference value for both grid scheduling and stable operation.

Analysis of medium-frequency oscillation based on bifurcation theory and joint modelling of SVG and direct-drive wind turbine

With the expansion of wind power clusters in power system, medium and high frequency oscillations in wind farm area are preliminarily emerging. At present, linear analysis such as impedance analysis are mainly used to analyse the grid-connected oscillation of renewable energy generation. However, the explanation of the oscillation mechanism is not intuitive and does not take into account the nonlinearity of converter. Considering the influence of external loop control of SVG and direct-drive wind turbine on the medium-frequency characteristics of the parallel system, a reduced-order nonlinear model under voltage time scale is established. The conjoint analysis of Jacobi matrix, bifurcation diagram and phase space trajectory are applied to study the influence rule of machine-side output power and short-circuit ratio on the oscillation behaviour of the parallel system. In addition, the oscillation evolution process is analysed from the dynamic standpoint. It is found that the system operates on the limit-loop state when the medium-frequency oscillation occurs, which reveals the mapping relationship between the oscillation amplitude and the limit-loop trajectory. The experimental results ultimately validate the theoretical analysis conducted on the StarSim-HIL (hardware-in-the-loop) experiment platform.

Steady-state deduction methods of a power system based on the prediction of large-scale wind power clusters

The integration of a high proportion of wind power has brought disorderly impacts on the stability of the power system. Accurate wind power forecasting technology is the foundation for achieving wind power dispatchability. To improve the stability of the power system after the high proportion of wind power integration, this paper proposes a steady-state deduction method for the power system based on large-scale wind power cluster power forecasting. First, a wind power cluster reorganization method based on an improved DBSCAN algorithm is designed to fully use the spatial correlation of wind resources in small-scale wind power groups. Second, to extract the temporal evolution characteristics of wind power data, the traditional GRU network is improved based on the Huber loss function, and a wind power cluster power prediction model based on the improved GRU network is constructed to output ultra-short-term power prediction results for each wind sub-cluster. Finally, the wind power integration stability index is defined to evaluate the reliability of the prediction results and further realize the steady-state deduction of the power system after wind power integration. Experimental analysis is conducted on 18 wind power farms in a province of China, and the simulation results show that the RMSE of the proposed method is only 0.0869 and the probability of extreme error events is low, which has an important reference value for the stability evaluation of large-scale wind power cluster integration.

Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy.

Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales

To improve the control of active power in wind power clusters, an active power hierarchical predictive control method with multiple temporal and spatial scales is proposed. First, the method from the spatial scale divides the wind power clusters into the cluster control layer, sub-cluster coordination layer and single wind farm power regulation layer. Simultaneously, from the temporal scale, the predicted data are divided layer by layer: the 15 min power prediction is deployed for the first layer; the 5 min power prediction is employed for the second layer; the 1 min power prediction is adopted for the third layer. Secondly, the prediction model was developed, and each hierarchical prediction was optimized using MPC. Thirdly, wind farms are dynamically clustered, and then the output power priority of wind farms is established. In addition, the active power of each wind farm is controlled according to the error between the dispatch value and the real-time power with feedback correction so that each wind farm achieves cooperative control with optimal power output. Finally, combined with the simulation of practical wind power clusters, the results show that the wind abandonment rate was reduced by 2.13%, and the dispatch of the blindness was overcome compared with the fixed proportional strategy. Therefore, this method can improve the efficiency of cooperative power generation.

Short-term regional wind power forecasting based on spatial–temporal correlation and dynamic clustering model

Short-term regional wind power forecasting (WPF) is essential for enhancing the power grid’s robustness. In the field of regional short-term power forecasting, numerical weather forecasts (NWP) and regional wind-power historical data have been commonly used in power forecasting. In this paper, a regional wind power prediction model based on the spatial and temporal correlation of meteorological resources is proposed to predict the wind power in the next seven days. First, combining variational mode decomposition and Granger causality test (VMD-GCT) to analyze the NWP-related meteorological factors to the wind power components in different bands to obtain the screening of the NWP-related factors. Then, the correlation analysis technique, which uses the physical characteristics of the wind speed time lag in a region, dynamically analyzes the spatial correlation time lag between a wind farm in the region and other wind farms, and uses the DBSCAN (density-based spatial clustering of applications with noise) algorithm to dynamically divide wind power clusters in the wind power forecast cycle, thus laying the foundation for the input data of the prediction model. Finally, the short-term power forecasting in the region is performed by a combined deep learning model. The results show that the proposed method can significantly improve the accuracy and efficiency of the wind power prediction for clusters at different wind speeds. The proposed method is of great significance for the short-term prediction of wind power in a region.

Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration

Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization, and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE Network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

Widening the wind power cluster framework with a regional energy cluster: a research note

The energy industry is identified as a growing business area and the target of development in a case study region. This practitioner’s report describes how the regional energy cluster was defined by using the wind power cluster framework as a basis. The identified energy industry players are positioned in the wind power cluster framework and the actors and operating environment are described and analyzed. According to this study the cluster framework can be a basis for cluster definitions of other industries, especially in the same case region. The practical object of the study is to give regional officials a clear picture of the energy industry operating environment and its cooperation network.

Investigation of Variability of Wind/Solar Resources Properties

Renewable Energy Sources (RES) such as wind and solar energy depend on the climatic conditions, power grid and other comprehensive factors. Areas with sufficient RES also have large-scale wind power clusters and PV plants connected to the grid. In order to reduce the impact of fluctuating energy on power system, it is necessary to investigate variation of wind and solar energy resources in a region. This paper proposes an integrated method based on the xgboost model to study regional renewable energy resources. Characteristics of annual and monthly variation of aggregated powers in a large area are analyzed. The proposed method can be further used for reliability analysis and planning of intermittent power generation in regional grids.

A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control

With large scale wind integration and increasing wind penetration in power systems, relying solely on conventional generators for frequency control is not enough to satisfy system frequency stability requirements. It is imperative that wind power have certain capabilities to participate in frequency control by cooperating with conventional power sources. Firstly, a multi-area interconnected power system frequency response model containing wind power clusters and conventional generators is established with consideration of several nonlinear constraints. Moreover, a distributed model predictive control (DMPC) strategy considering Laguerre functions is proposed, which implements online rolling optimization by using ultra-short-term wind power forecasting data in order to realize advanced frequency control. Finally, a decomposition-coordination control algorithm considering Nash equilibrium is presented, which realizes online fast optimization of multivariable systems with constraints. Simulation results demonstrate the feasibility and effectiveness of the proposed control strategy and algorithm.

SVC damping controller design based on novel modified fruit fly optimisation algorithm

Existing reactive power systems do not readily provide support or anti-disturbance capabilities. This study was conducted to explore the predisposing factor and suppression measures of low-frequency oscillation in large-scale wind power cluster systems by establishing a wind farm cluster mode with wind power fluctuation in a DIgSILENT/power factory. Considering the multiple time scale and the operating characteristics of cluster system, a novel modified fruit fly optimisation algorithm (nMFOA) combined with probabilistic sensitivity indices is proposed to coordinate and optimise static VAR compensator (SVC) damping controller parameters to enhance the power system stability of the wind farm cluster. Adverse effects in the SVC damping controller are eliminated via the nMFOA with probabilistic eigenvalue, which can be used to effectively coordinate and optimise SVC parameters. The proposed scheme was tested on a certain wind farm cluster in Hami, Xinjiang Province.