Wind Farm Reliability Research Articles

Multi-step prediction of offshore wind power based on Transformer network and Huber loss

In the context of the burgeoning expansion of renewable energy sources, the precise prediction of offshore wind power assumes a pivotal role in safeguarding the reliability, economic viability, and sustainable progression of offshore wind farms. The present study introduces a novel methodology for offshore wind power prediction, predicated upon the synergy of the Transformer network and Huber loss function. Empirical validation is conducted utilizing authentic data from a European offshore wind farm. The resulting analyses delineate a discernible superiority of the Transformer network over classical LSTM and GRU models in capturing the intricate long-term dependencies intrinsic to the time series. Furthermore, the inclusion of the Huber loss function effectively mitigates the challenges posed by the high volatility often characteristic of offshore wind power data. The study also demonstrates the beneficial integration of autoencoder reconstruction for denoising and slime mould optimization algorithm to augment prediction performance. Distinctively diverging from traditional single-step prediction paradigms, the multi-step prediction model constructed within this research offers a more comprehensive and precise prediction of wind power. Such an innovative approach represents a valuable contribution to the field, with tangible implications for the dependable operation and future advancement of wind power.

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

A PDMS coating with excellent durability for large-scale deicing

The icing of wind turbine blades leads to a decrease in output power, seriously jeopardizing the economic benefits and operational reliability of wind farms. Conventional deicing techniques require expensive equipment and consume a large amount of energy. Low-interfacial toughness coatings without energy dissipation are believed to be a highly potential passive deicing technology. However, the durability in service is facing challenges. Herein, low-interfacial toughness PDMS coatings were prepared by physical blending. Through the optimization of added plasticizers and SiO2, PDMS coatings with excellent large-scale deicing performance were obtained. The constant deicing force and ice adhesion strength were reduced to 14.69 N/cm and 12.63 kPa, respectively. Moreover, a systematic durability assessment of the PDMS coating was carried out to address the actual operating conditions of wind turbines. Fortunately, the results showed that the PDMS coating could withstand 200 icing/deicing cycles while maintaining constant deicing force and ice adhesion strength of less than 50 N/cm and 30 kPa, respectively. After long-term thermal aging (21 days), UV irradiation (42 days) and salt spray corrosion (20 days), the PDMS coating still retained superior icephobicity and outstanding large-scale deicing performance. This work contributes to the research and development of low-interfacial toughness materials for large-scale deicing applications on wind turbine blades.

Markov reliability model of a wing farm

A Markov model of wind farm reliability based on the example of the station on the island of Anholt, Denmark, is constructed. Reliability indexes of equipment of one turbine as a function of wind velocity are calculated. Based on hourly measurements of wind speed and electricity consumption, the durations of periods of satisfied and unsatisfied demand are estimated. It is found that the distributions of these periods can be approximated by a mixture of exponential distributions. The plant operation process is approximated by a Markov process with 5 states and continuous time. As a result, estimates of non-stationary and stationary probabilities of electricity demand being met by wind power are obtained.

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

SummaryHigh penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short‐term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM‐based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms.

Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100% for false data. With a recall of 100%, no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.

A Fault and Capacity Loss Prediction Method of Wind Power Station under Extreme Weather

Extreme weather events can severely affect the operation and power generation of wind farms and threaten the stability and safety of grids with high penetration of renewable energy. Therefore, it is crucial to forecast the failure and capacity loss of wind farms under extreme weather conditions. To this end, considering the disaster‐causing mechanism of severe weather and the operational characteristics of wind farms, this paper first uses the density‐based spatial clustering of applications with noise algorithm to cluster the units in the wind farm based on the operating characteristics affected by the weather, and uses correlation analysis methods to extract key disaster‐causing factors in extreme weather; then proposes a prediction model based on feature‐weighted stacking integration. The model adopts the stacking‐integrated learning architecture to support multiple learners and performs feature weighting according to the prediction accuracy of each learner in the base learner, thereby improving the training effect of the meta‐learner and improving the prediction accuracy of the model. The prediction model is used to predict each wind turbine group based on the extracted key features and to predict the failure and capacity loss of the wind farm. Finally, an example analysis is performed based on actual data from a wind farm, and the results show that the proposed prediction method can effectively predict the operational reliability of wind farms.

Frequency regulation optimization for wind storage based on frequency regulation reliability and state of charge in isolated island off‐grid

AbstractTo further improve the frequency regulation stability of wind farm, and optimize the state of charge (SOC) basepoint, charge and discharge rate and recovery capacity of energy storage. In an isolated, off‐grid state, a two‐layer optimization method is proposed, taking into account the frequency regulation reliability and SOC adaptive adjustment of the wind storage. In the upper layer, the model predictive control theory is adopted to optimize the energy‐storage frequency regulation output power with the goal of minimizing the wind storage frequency regulation power deviation. While solving the problem of low‐frequency regulation reliability of wind farm, the SOC recovery basepoint and frequency regulation power of energy storage are optimized. At the SOC optimization layer, based on the upper SOC recovery basis point, we propose a dynamic recovery method for energy‐storage SOC that considers both the SOC recovery demand and grid‐bearing capacity, to determine the energy‐storage recovery power. By determining the frequency regulation or recovery power, we propose a calculation method to optimize the energy‐storage charge and discharge coefficients as per the SOC for avoiding excessive charging and discharging. The simulation results show that, under continuous disturbance, the root‐mean‐square deviations of the proposed method is 80.13% and 62.63% lower than those of the fixed K and SOC methods, respectively. Furthermore, the proposed method exhibits the best SOC maintenance effect.

Organization and Reliability Testing of a Wind Farm Device in Its Operational Process

This article deals with the importance of simulation studies for the reliability of wind farm (WF) equipment during the operation process. Improvements, upgrades, and the introduction of new solutions that change the reliability, quality, and conditions of use and operation of wind farm equipment present a research problem during study. Based on this research, it is possible to continuously evaluate the reliability of WF equipment. The topic of reliability testing of complex technical facilities is constantly being developed in the literature. The article assumes that the operation of wind farm equipment is described and modeled based on Markov processes. This assumption justified the use of Kolmogorov–Chapman equations to describe the developed research model. Based on these equations, an analytical model of the wind farm operation process was created and described. As a result of the simulation analysis, the reliability of the wind farm was determined in the form of a probability function (R0(t)) for the WPPs system.

A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks

Wind turbine condition monitoring (WTCM) plays an important role in reducing operation & maintenance (O&M) cost and improving the reliability of wind farms. Supervisory control and data acquisition (SCADA) data have advantages such as easy access and strong timeliness and are used widely for WTCM. However, it is difficult to distinguish and label historical SCADA data as healthy or faulty accurately during the model training process. Therefore, a De-ambiguous Condition Monitoring scheme with Transfer layer (DCMT) based on SCADA data is proposed to overcome this problem. This scheme provides a fault early warning for wind turbines. In this scheme, an improved auto-encoder (AE) network with a transfer layer is designed to eliminate the effect of SCADA data in the ambiguous status (ambiguous data) and enhance the reliability of a training dataset. Meanwhile, a structure of Siamese encoder is designed to calculate the residuals between latent features, i.e., the outputs of the Siamese encoders. These residuals can be utilised to identify wind turbine operational conditions. Further, least squares generative adversarial networks (LSGAN) is introduced to learn the distribution of health data while restricting the discriminator and realising the augmentation of health data for model training. The proposed method is applied to two cases of generator winding and gearbox bearing over-temperature faults of wind turbines from northwest China. Compared with other methods, the proposed method effectively detects potential abnormal conditions in advance without raising false alarms.

Fault-Tolerant Cooperative Control of Large-Scale Wind Farms and Wind Farm Clusters

Large-scale wind farms and wind farm clusters with many installed wind turbines are increasingly built around the world, and especially in offshore regions. The reliability and availability of these assets are critically important for cost-effective wind power generation. This requires effective solutions for online fault detection, diagnosis and fault accommodation to improve the overall reliability and availability of wind turbines and entire wind farms. To meet this requirement, this paper proposes a novel active fault-tolerant cooperative control (FTCC) scheme for large-scale wind farms and wind farm clusters (WFCs). The proposed scheme is based on a signal correction method at wind turbine level that is augmented with two innovative “control reallocation” mechanisms at wind farm and network operator levels. Applied to a WFC, this scheme detects, identifies and accommodates the effects of both mild and severe power-loss faults in wind turbines. Various simulation studies on an advanced WFC benchmark indicate the high efficiency and effectiveness of the proposed solutions.

Machine-Learning-Based Intelligent Mechanical Fault Detection and Diagnosis of Wind Turbines

Wind power has gained wide popularity due to the increasingly serious energy and environmental crisis. However, the severe operational conditions often bring faults and failures in the wind turbines, which may significantly degrade the security and reliability of large-scale wind farms. In practice, accurate and efficient fault detection and diagnosis are crucial for safe and reliable system operation. This work develops an effective deep learning solution using a convolutional neural network to address the said problem. In addition, the linear discriminant criterion-based metric learning technique is adopted in the model training process of the proposed solution to improve the algorithmic robustness under noisy conditions. The proposed solution can efficiently extract the features of the mechanical faults. The proposed algorithmic solution is implemented and assessed through a range of experiments for different scenarios of faults. The numerical results demonstrated that the proposed solution can well detect and diagnose the multiple coexisting faults of the operating wind turbine gearbox.

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

WRC-SDT Based On-Line Detection Method for Offshore Wind Farm Transmission Line

Transmission line fault detection is a complex, high-cost and time-consuming work for deep-sea offshore wind farms. Therefore, rapid intelligent on-line fault detection and classification of submarine transmission lines play a very important role in the offshore wind farm reliability improving and operating costs reduction. This paper presents a novel hybrid on-line detection method that combines Wavelet noise Reduction, Clarke transform, Stockwell transform and Decision Tree (WRC-SDT). First, the measured Wind Turbine (WT) voltage signals are processed through wavelet noise reduction and Clarke transform to get the gradient of the voltage component. The gradient of the voltage component is then monitored in order to detect faults. Second, the recorded WT current date are processed through Stockwell transform with a view to obtaining the transmission line fault eigenvalues. The fault eigenvalues are then taken as the input of decision tree in order to classify different types of faults. To verify the feasibility and performance of the proposed method, the comparison of a detection and classification result is presented based on more than 1600 fault simulation data. The results show that WRC-SDT method is immune to fault resistance, starting angle and location. The proposed method is also robust to measurement noise.

Capacity Credit Algorithm and Index of the New Energy of Wind Energy and Solar Energy based on Big Data

Since twenty-first century, many countries have accelerated their development, and the energy on the earth has been gradually consumed, which not only has caused the lack of energy, but also the environmental pollution. Therefore, people have paid much attention to the related problems. Based on the research of big data theory, the credibility algorithm of new energy was established in this paper. And through the establishment of wind farm power generation model, its reliability and indicators were analyzed. Thus, the feasibility of replacing old energy with new energy sources was explored. And through the analysis of the data, the reliability of wind farms and photovoltaic power plants was evaluated.

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

Impact of Correlation Between Wind Speed and Turbine Availability on Wind Farm Reliability

This paper proposes a new method to evaluate the reliability of a wind farm considering the correlation between wind turbine reliability and wind speed. With increasing integration of wind generation into the grid, the reliability and stability of the grid are increasingly impacted. Although there are obvious benefits from wind generation, the stochastic nature of wind speed causes several operational challenges. Recent research has shown that wind speeds also impact the failure rates of the turbines, thereby compounding the effect of wind speed variation. The objective of this paper is to evaluate the impact of wind speed on the reliability of a wind farm considering the correlation between wind speed and wind turbine failure rate. The method proposed in this paper is implemented using discrete convolution. The effectiveness of the proposed method is proved by comparing reliability indexes of the modified IEEE RTS-79 system with and without considering the impacts of the negative correlation between wind turbine reliability and wind speed.

Reliability and economy assessment of offshore wind farms

Offshore wind power resource, which is relatively stable, abundant, and with modest impact on the environment, is a kind of clean and renewable energy suitable for large-scale development. Offshore wind farms have different topological structures of the collection system, such as AC parallel type, DC parallel type, DC series type, single-ring type, and double-ring type. The purpose of this paper is to analyse reliability and economy for various topologies and provide an insight into wind farm construction. In this study, the topology structure of various offshore wind farms is introduced firstly. Then, the reliability assessment methods are proposed. The reliability of the chain wind farm is analysed with the reliability block diagrams, while the reliability of the ring wind farm is analysed by using the classification method. In addition, the economy is evaluated considering investment costs. The investment cost for each type of component is calculated separately, which contributes to assessing the investment cost of the whole offshore wind farm. Detailed results from a case study, which includes 96 wind turbines, demonstrate that the DC series–parallel wind farm has superiority in reliability and economy, as well as in terms of its potential applications.

A Sequential MCMC Model for Reliability Evaluation of Offshore Wind Farms Considering Severe Weather Conditions

The offshore wind farms (OWF) are susceptible to the severe weather, which can cause the increase of component failure rate and has significant influence on the maintenance process and the reliability of wind farms. This paper proposes a sequential Markov chain Monte Carlo (MCMC) model for reliability evaluation of the OWF considering the impact of severe offshore weather. First, main factors affecting the wind turbine (WT) failure rate are analyzed. Second, a time-varying analytical model for the WT failure rate affected by wind speed and lightning is established. Three types of WT failure rates are considered: the failure rate under normal weather, that under strong wind, and that affected by lightning. Moreover, a time-varying analytical model for the repair time of main components of OWF is established by considering the influence of severe weather on offshore transportation time and maintenance efficiency after component failure. The MCMC model takes into account the temporal correlation of the weather and the repair process of failed component in the reliability evaluation. The model enables simultaneous simulation of the weather intensity and component state. For each system state generated by the MCMC model, a breadth-first search (BFS) method is applied to analyze the connectivity of the WTs and the sink node. Finally, the output of the wind farm is determined based on the wind speed data at this state. The expected energy not supply (EENS) and the generation ratio availability (GRA) indices of the OWF are evaluated to demonstrate the effectiveness of the proposed models. Further, the effects of other factors such as the enhanced protection for WT, the use of helicopter, and the weather characteristics of the OWF location on the reliability of OWF are discussed.