Wind Farm Operation Research Articles

Validation of a methodology for post-construction Energy Yield Assessment of an operational wind farm

The uncertainty associated with the prospective Energy Yield Assessment (EYA) of a wind farm may be reduced by re-estimating the energy yield after it enters normal operation. This study aims to validate a simple methodology for conducting post-construction EYA of an operational wind farm. The proposed methodology derives a linear relationship between a historical source of wind speed data and the observed wind farm production on a monthly basis.In the first stage, the impact of different data sources on the accuracy of the Long-Term energy yield estimate was assessed. Results suggest that the determination coefficient R is a reliable indicator for selecting the most adequate source of historical wind speed data to be used in the Long-Term energy yield estimate.In the second stage, the model was validated from a statistical point of view by testing the premises of the linear regression model, namely the significance of the linear correlation (ANOVA test), and the normally-distributed (Shapiro-Wilk test), non-self-correlated (Durbin-Watson), and homoscedastic (Breusch-Pagan test) residuals. Results show these premises are verified for most test cases, indicating that the model is statistically robust for most test cases.

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

ABSTRACTAccurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

Drafting a picture of wind farms during very hot daytime conditions

Results can help wind-farm operators adjust their wind farms to maximize energy extracted throughout the day.

Innovative Benchmarking of Offshore Wind O&M Strategies via AIS Data Analysis

Abstract By analyzing AIS data, this study provides independent insights into the diverse logistic concepts deployed by wind farm operators. The benchmarking framework developed herein facilitates the evaluation of logistic efficiency, resilience, and sustainability, thereby contributing to the identification of best practices in offshore wind operations and maintenance (O&M) logistics. The methodology encompasses data collection, preprocessing, and analysis phases. By providing a transparent and data-driven mechanism for benchmarking offshore wind O&M logistics, this study aids in the identification of inefficiencies and the formulation of strategies to enhance logistic operations. The results indicate significant variations in logistic efficiency across different offshore wind farms (OWF), suggesting that the adoption of best practices could lead to substantial improvements.

AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity

The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.

Reliability assessment method of wind power DC collection system based on MLFTA-SMC

Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority.

Analysis of Wind Power Fluctuation in Wind Turbine Wakes Using Scale-Adaptive Large Eddy Simulation

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach

Wind turbine wake poses a significant challenge in wind farm operations, affecting power generation efficiency. This study introduces a Dynamic Wake Flow Estimation (DWFE) framework designed to predict wind turbine wake evolution from sparse measurement data. The framework integrates Gaussian Process Regression (GPR), Proper Orthogonal Decomposition (POD), and Long Short-Term Memory (LSTM) networks. Specifically, GPR plays a pivotal role in DWFE by enabling the transformation of flow fields discrete sensor signals into coherent, low-dimensional flow fields which is crucial for accurate flow field predictions, while POD aids in dimensionality reduction and LSTM forecasts temporal wake dynamics, improving both predictive accuracy and computational efficiency. Parametric analysis demonstrates that the robustness and adaptability of the framework improve prediction accuracy with increased sensor density and flexible POD modes. The DWFE framework demonstrates high accuracy in wake flow estimation even with limited data, effectively capturing dynamic wake behavior. Future work will extend this approach to various topographic and climatic conditions, optimizing computational efficiency, and improving interpretability. These advancements will expand the framework's applicability in wind energy and other engineering fields requiring precise flow prediction, emphasizing DWFE's potential in advancing wind farm design, operation and renewable energy optimization.

Influence of the sound propagation models to predict far field wind turbine noise

During the development process of a wind farm, the noise impact on neighboring residents is of crucial importance to limit annoyance and comply with local regulations. In France, this assessment is carried out by modelling the propagation of sound from each wind turbine to residents. For wind farm owners, this phase will in fine define the operation of the wind farm and therefore its electricity production. However, there are major concerns and uncertainties during this modelling phase. A concrete example of the influence of the choice of propagation model on the production of a wind farm is presented, giving an idea of its economic impact. Comparisons of the impact of wind farm noise modelled by engineering models with measurements taken on wind farms in operation provide a better understanding of the choice of certain methods or parameters. Taken together, these observations suggest areas for further research and development, with the aim of perhaps defining a more restricted framework for the sound propagation modelling stage, in order to help operators predict wind farm noise more accurately.

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.

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 wind turbine icing prediction data processing and accuracy of machine learning algorithm

Studying the icing problem of wind turbine blades is crucial for optimizing wind farm operation and maintenance. Traditional machine learning algorithms like Random Forest and Support Vector Machines have limitations in handling complex time series data and capturing long-term dependencies, leading to insufficient accuracy and generalization. To address these gaps, this paper proposes a comprehensive approach involving advanced machine learning frameworks and feature engineering techniques. This paper based on the original SACDA dataset, four distinct types of processing are performed on the prediction data via PCA dimensionality reduction and the introduction of new features; meanwhile, the four types mentioned above of datasets are trained and predicted using the Gated Recycling Unit model (GRU), Random Forest (RF), GA-BP Neural Network (BP), and Extreme Learning Machine (ELM) models. The results indicate that the prediction accuracies of all four types of models are more satisfactory, with the RF model having the highest prediction accuracy and the overall accuracy remaining above 99 percent, the dataset + RF model after dimensionality reduction of the original sensitive features having the highest prediction accuracy and speed.

RUL forecasting for wind turbine predictive maintenance based on deep learning

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future-RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models—ForeNet-2d and ForeNet-3d—are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.

Study on the Design of Series-Type All-DC Wind Farms Based on Half-Bridge Voltage Balancing Circuits

Offshore wind farms connected in series, with each wind turbine connected in series with one another, enhance the coupling between them. Significant differences in wind speeds between neighboring DC wind turbines (DCWTs) might result in a substantial disparity in the output voltage, hence posing a risk of overvoltage. Nevertheless, implementing voltage-limiting configurations for DCWTs might lead to the dissipation of wind energy, thereby diminishing the wind farm’s capacity to deliver electricity. This work introduces a half-bridge voltage balancing circuit (HVBC) topology as a solution to the issue of DCWT output voltage changes affecting the stable operation of wind farms. The proposed HVBC topology is designed specifically for large-capacity series-connected all-DC wind farms where wind speed variations occur. This design achieves power decoupling for series-connected all-DC wind farms by providing current compensation to the series-connected DCWTs. A control strategy is devised by examining the decoupling principle and operational characteristics of the HVBC. A 60 kV/48 MW tandem-type all-DC wind farm model consisting of six DCWTs in series is built in Matlab/Simulink. The model is then simulated to evaluate its performance under conditions of unequal wind speed, rapid changes in wind speed, and wind turbine failure shutdown. This research verifies the feasibility of the HVBC topology and improves the stability of the series-type all-DC wind farm.

Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft

Abstract. Continued development of wind farms near populated areas has led to rising concerns about the potential risk posed to general aviation aircraft when flying through wind turbine wakes. There is an absence of experimental flight test data available with which to assess this potential risk. This paper presents the results of an instrumented flight experiment in which a general aviation aircraft was flown through the wake of a utility-scale wind turbine at an operating wind farm. Wake passes were flown at different downwind distances from the turbine, and data were collected on the orientation disturbances, altitude and speed deviations, and acceleration loads experienced by the aircraft. Videos and pilot statements were also collected, providing qualitative information about the disturbances encountered in the wake. Results show that flight disturbances were small in all cases, with no difference observed between flight data inside and outside the wake at distances greater than six rotor diameters from the turbine. At distances closer than six rotor diameters, small load factor and orientation disturbances were noted but were commensurate with those experienced in light or moderate atmospheric turbulence. Overall, the loads and disturbances experienced were far smaller than those that would risk causing loss of control or structural damage.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Optimizing Price Markup: The Impact of Power Purchase Agreements and Energy Production Uncertainty on the Economic Performance of Onshore and Offshore Wind Farms

Wind energy is rapidly expanding its capacity as part of the global energy transition. To ensure economic viability, wind energy projects increasingly rely on risk mitigation strategies. While Power Purchase Agreements (PPAs) manage spot market price variability, wind farms still face energy production uncertainty, directly impacting the quantity of energy generated. This study presents a comprehensive analysis of the price markup for onshore and offshore wind farms, considering energy production uncertainty under PPA and non-PPA scenarios. The study examines key metrics such as equilibrium prices, price markups, Internal Rate of Return (IRR), Net Present Value (NPV), and their correlation to energy production risk. Results demonstrate that PPAs significantly alter the relationship between price markup and variability. Offshore wind farms can potentially benefit more from PPAs compared to onshore, especially at lower levels of wind energy variability. However, as variability increases, the risk mitigation provided by PPAs diminishes, and both onshore and offshore wind farms may require higher price markups for financial viability. These findings highlight the necessity of carefully designed PPA structures and pricing strategies to ensure long-term competitiveness and sustainability of wind energy projects.

Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China

The rapid growth of offshore wind farms (OWFs) is driven by concerns for energy security and climate change mitigation. However, their impact on marine environments remains poorly understood due to limited research. This study analyzes the effects of an OWF along China's Jiangsu Coast on seawater quality using data from different development phases. Results show the major pollutants were different across phases. Heavy metal pollution reached alert levels during construction compared to the safe levels observed in the pre-construction and operational phases, mainly due to increases in Pb, Cd, and Hg concentrations. Eutrophication was mild throughout all periods but exhibited a continuous decrease, primarily attributed to reductions in PH and COD concentrations. As a result, the comprehensive pollution level during construction was increased, but it was improved to a clean level during the operational phase. Besides, significant variations were observed in the spatial distribution patterns of major pollutant indices across different scenarios. These changes may stem from a combination effect of land-based pollution, aquaculture, OWF-induced disturbances to atmosphere and hydrodynamics, OWF-related drain and leakage contamination, and marine management policies. Understanding these effects informs OWF optimization, rational wind resource utilization, and marine ecology protection.

A short-term wind speed prediction method based on the IDBO-BPNN

Wind energy is known for its uncertainty and volatility, necessitating accurate wind speed prediction for stable wind farm operations. To enhance wind speed prediction accuracy, this study proposes a BP neural network (BPNN) short-term wind speed prediction model based on the Improved Dung Beetle Optimization (IDBO) algorithm. Addressing the issue of local optimization and reduced accuracy in the BPNN optimized by the Dung Beetle Optimization (DBO) algorithm, the circle chaotic mapping is utilized for population initialization to achieve a more uniform initial distribution. The improved sine-cosine algorithm, triangle wandering strategy, and adaptive weight coefficient are then employed to optimize dung beetle positions, balancing global exploration and local development capabilities and improving the algorithm’s search performance. Finally, the improved DBO algorithm optimizes the weights and thresholds of the BPNN, and the IDBO-BPNN prediction model was constructed. Simulation experiments were conducted based on wind speed data from a wind farm in Ohio, USA. The IDBO-BPNN model was compared with other prediction models, and error evaluation indexes were introduced to evaluate the experimental results. The findings demonstrate that the suggested model yields the most accurate predictions and achieves the optimal error evaluation indexes. MAE, MSE, RMSE, NSE and R2 of dataset 1 are 0.42247, 0.28775, 0.53642, 88.8785%, 89.161%, those of dataset 2 are 0.28283, 0.14952, 0.38668, 85.7383%, 86.577%, and those of dataset 3 are 0.45406, 0.39268, 0.62664, 84.3859%, 84.931%. In particular, compared with BPNN model, the five evaluation indexes of the IDBO-BPNN model promoted by 41.53%, 57.38%, 34.71%, 24.91%, and 11.44%, respectively in dataset 3. Therefore, that the proposed IDBO-BPNN model exhibits higher accuracy in short-term wind speed prediction, indicating its feasibility and superiority in the realm of wind energy.

Hybrid offshore wind–solar energy farms: A novel approach through retrofitting

Due to the inherent variability of the power output of offshore wind farms, their integration into electrical grids poses a challenge to their stability and leads to significant balancing costs. Combining wind and solar power may be expected to mitigate the power output variability. In addition, a number of synergies can be realised—shared infrastructure, shared crews and vessels for operations and maintenance, and last but not least, optimum use of scarce marine space. The objective of this work is to investigate this hybrid approach and, in particular, to determine the optimum capacity of the solar energy subsystem given a certain installed capacity of the wind energy subsystem. Three case studies are considered in Europe and China. Each of them is an existing wind farm that could be retrofitted with floating solar PV. After assessing the local wind and solar energy resources, the optimal size of the floating solar array is calculated with a view to smooth the aggregated power output of each farm. Subsequently, the benefits of this hybrid approach are demonstrated from various perspectives. It is found that a much larger solar array is required for a wind farm with a large installed capacity, but the specific optimal size depends more on the local wind and solar resources. The novelty of this paper lies in its exploration of retrofitting existing offshore wind turbines with floating solar PV systems from the perspective of addressing the variability challenge for the currently operating wind farms. This study could serve as a guideline for project designs aiming to retrofit existing offshore wind farms with solar PV technology, thus reducing balancing costs and facilitating the penetration of offshore renewable energy into national power grids.