Showing papers on "Turbine published in 2021"

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Abstract: Wind energy is contributing to more and more portions in the world energy market However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented

A review of multiphase energy conversion in wind power generation

Abstract: As an important renewable energy source, the scale of wind energy utilization is growing rapidly worldwide in recent decades. The increasing capacity of both onshore and offshore wind power generation calls for higher requirements for the power level and reliability of generators and converters. Compared to the traditional three-phase wind power generation, multiphase wind power generation systems have obvious advantages in low-voltage high-power operation, enhanced fault-tolerant ability and increased degrees of control freedom, which help them gaining increasing popularity in modern wind power generation. This paper presents an overview on the multiphase energy conversion of wind power generation and introduces the pertinent technology advances, including the design of multiphase wind turbine generators, multiphase converter topologies, modeling and control of multiphase generators. Besides, this paper offers prospects of novel solutions brought by the redundant control freedom in multiphase system to cope with some difficult technical problems, such as Common-Mode Voltage (CMV) suppression, Low Voltage Ride Through (LVRT), and vibration & noise reduction.

Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts

Abstract: Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

Installation of offshore wind turbines: A technical review

Abstract: The installation phase is a critical stage during the lifecycle of an offshore wind turbine. This paper presents a state-of-the-art review of the technical aspects of offshore wind turbine installation. An overview is first presented introducing the classification of offshore wind turbines, installation vessels, rules and regulations, and numerical modelling tools. Then, various installation methods and concepts for bottom-fixed and floating wind turbines are critically discussed, following the order of wind turbine foundations and components. Applications and challenges of the methods are identified. Finally, future developments in four technical areas are envisioned. This review aims to guide research and development activities on offshore wind turbine installation.

A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine

Abstract: A fast wind turbine abnormal data cleaning algorithm via image processing for wind turbine power generation performance measurement and evaluation is proposed in this paper. The proposed method includes two stages, data cleaning and data classification. At the data cleaning stage, pixels of normal data are extracted via image processing based on pixel spatial distribution characteristics of abnormal and normal data in wind power curve (WPC) images. At the data classification stage, wind power data points are classified as normal and abnormal based on the existence of corresponding pixels in the processed WPC image. To accelerate the proposed method, the cleaning operation is executed parallelly using graphics processing units (GPUs) via compute unified device architecture (CUDA). The effectiveness of the proposed method is validated based on real data sets collected from 37 wind turbines of two commercial farms and three types of GPUs are employed to implement the proposed algorithm. The computational results prove the proposed approach has achieved better performance in cleaning abnormal wind power data while the execution time is tremendously reduced. Therefore, the proposed method is available and practical for real wind turbine power generation performance evaluation and monitoring tasks.

Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis

Abstract: Utility-scale wind turbines are equipped with a supervisory control and data acquisition (SCADA) system for remote supervision and control. The SCADA system accumulates a large amount of data that contains the health conditions of the wind turbines. Thus, it is interesting to mine the health status-related information from SCADA data for wind turbine condition monitoring. In this article, an ensemble approach is proposed to detect anomalies and diagnose faults in wind turbines. Historical SCADA data collected from healthy wind turbines are used to model their normal behaviors and build a Mahalanobis space as a reference space. By comparing the predicted behavior of the wind turbine by a trained model with the reference space, anomalies can be detected. Finally, wind turbine faults are diagnosed through the analysis of the distributions and correlations of their SCADA data. The proposed approach is validated by using the SCADA data collected from two field wind turbines. Results show that it can detect anomalies and diagnose the corresponding failure components before the wind turbines have to be shut down for maintenance.

Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm

Abstract: Fault diagnosis technology is key to the safe and stable operation of wind turbines. An effective fault diagnosis technology for wind turbines can quickly identify fault types to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. Currently, most wind farms obtain operation and maintenance data via supervisory control and data acquisition (SCADA) systems, which contain rich information related to the operation characteristics of wind turbines. However, few SCADA systems provide fault diagnosis functionality. Support vector machines (SVMs) are a popular intelligence method in the fault diagnosis of wind turbines. SVM parameter selection is key for accurate model classification. The sparrow search algorithm (SSA) is a novel and highly efficient optimization method used to optimize the penalty factor and kernel function parameter of SVM in this paper and to construct the SSA-SVM wind turbine fault diagnosis model. Data are acquired from a wind farm SCADA system and form a faulting set after preprocessing and feature selection. Experiments show that the SSA-SVM diagnostic model effectively improves the accuracy of wind turbine fault diagnosis compared with the GS-SVM, GA-SVM and PSO-SVM models and has fast convergence speed and strong optimization ability. Moreover, the SSA-SVM diagnostic model can be used to diagnose faults in practical engineering applications.

Study the influence of using guide vanes blades on the performance of cross-flow wind turbine

Abstract: A cross-flow wind turbine has a high torque coefficient at a low tip speed ratio; therefore, it is a good candidate for a self-starting turbine. This study aims to investigate the best configuration between guide vanes and cross-flow vertical axis wind turbine. The experiment test was carried out to determine the turbine with the highest power coefficient. The cross-flow turbine has 14, 18, and 22 blades with using 6,10 and 14 blades of guide vane (GV) was developed in this study, employing 15°, 25°, 35°, 45°, 55°, 65°, and 75° of tilt angles in fifth different wind speed conditions 4 m/s, 6 m/s, 7.5 m/s, 9.20 m/s, and 11 m/s. The turbine has 22 blades with 14 GV blades at 55° of tilt angle blades producing more remarkable turbine performance improvement than other blades. The highest power coefficient (CP) of cross-flow using 14 GV blades at 55° was 0.0162 at 0.289 TSR, which increased around 59% of the turbine's performance using GV.

Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines

Abstract: Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O\&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the UK is considered with samples from thirteen months before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.

Performance enhancement and multi-objective optimization of a double-flash binary geothermal power plant

Abstract: Initiating cost-effective and technological frameworks to exploit geothermal energy is a fundamental incentive for scientists and engineers; in this regard, flash-binary geothermal power plants are plausible for high-temperature geothermal resources. Stemming from an in-depth literature review, it is conspicuous that the thermal and exergy losses of the expansion process are significant. To shed light on this matter, an ejector-expander is integrated into the conventional expansion valve of a double-flash binary geothermal power plant to enhance the plant performance. The feasibility of the contrived notion is scrutinized from energy, exergy, thermal, and cost balance standpoints and results converged in the increase in turbine output power, energy efficiency, and exergy efficiency of approximately 7.66%, 7.64%, and 7.69%, respectively. Among all components, the condenser constituted a 45.06% share of the overall exergy destruction, whilst the turbine had an exergy destruction ratio of 22.07%. An intensive parametric study is implemented that outlined that the turbine output power, energy efficiency, and exergy efficiency of the ejector-expander power system have a maximum in a specified pressure value of the first and second separator. Another merit was a marked decrease in the unit cost of power when ejector-expander is employed.

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Abstract: During the past decade, the development of offshore wind energy has transitioned from near shore with shallow water to offshore middle-depth water regions. Consequently, the energy conversion technology has shifted from bottom-fixed wind turbines to floating offshore wind turbines. Floating offshore wind turbines are considered more suitable, but their cost is still very high. One of the main reasons for this is that the system dynamics control method is not well-adapted, thereby affecting the performance and reliability of the wind turbine system. The additional motion of the platform tends to compromise the system’s performance in terms of power maximization, power regulation, and load mitigation. To provide a recommendation based on the advantages and disadvantages of different control methods, we systematically analyze feasible control methods for existing floating offshore wind turbine designs. Based on a brief overview of floating offshore wind turbine system dynamics, we present several promising control methods by classifying them as blade-pitch-based and mass–spring–damper-based. Furthermore, we emphasize on the incoming wind and wave forecasting associated with the control methods. We then compare different methods by evaluating a matrix involving platform motion minimization, load mitigation, and power regulation and identify the advantages and disadvantages. Finally, recommendations and suggestions for further research are provided by integrating the advantageous control algorithm and forecasting technologies to reduce costs.

Boundary Feedback Control of a Nonhomogeneous Wind Turbine Tower with Exogenous Disturbances

Abstract: This paper studies a vibration and disturbance rejection problem of a wind turbine tower under exogenous perturbations. The tower dynamics is captured by a nonhomogeneous Euler-Bernoulli beam model. The dissipativity of the system is realized by a boundary feedback control solution with a multi-valued symbolic function. A Lyapunov-based stability analysis is established to assess that the deflection of the tower is uniformly bounded even subject to exogenous disturbances. The extended Filippov-framework and Galerkin approximation scheme are introduced to tackle the existence of the solution to the system with a discontinuous control input. Simulation results demonstrate the performance of the proposed control scheme.

A Reference Open-Source Controller for Fixed and Floating Offshore Wind Turbines

Abstract: . This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip-speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide a controller that is comparable to industry standards. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5-MW reference wind turbine and International Energy Agency 15-MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by nearly 14 % compared to the NREL 5-MW reference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by slightly more than 35 % when using the platform feedback routine instead of a more traditional low-bandwidth controller.

Simultaneous fault diagnosis of wind turbine using multichannel convolutional neural networks.

Abstract: Wind turbine technology is pursuing the maturation using advanced multi-megawatt machinery equipped by powerful monitoring systems. In this work, a multichannel convolutional neural network is employed to develop an autonomous databased fault diagnosis algorithm. This algorithm has been evaluated in a 5MW wind turbine benchmark model. Several faults for various wind speeds are simulated in the benchmark model, and output data are recorded. A multichannel convolutional neural network with multiple parallel local heads is utilized in order to consider changes in every measured variable separately to identify subsystem faults. Time-domain signals obtained from the wind turbine are portrayed as images and fed independently to the proposed network. Results show that the multivariable fault diagnosis scheme diagnoses the most common wind turbine faults and achieves high accuracy.

Wind turbine blade material in the United States: Quantities, costs, and end-of-life options

Abstract: Wind energy has experienced enormous growth in the past few decades; as a result, there are thousands of wind turbines around the world that will reach the end of their design lifetimes in the coming years. Much of the material in those turbines can be recycled using conventional processes, but the composite material that is the main component of the blades is more challenging to recycle. In the United States, turbine blades may be disposed of in landfills, adding a new solid waste stream to the material already being landfilled. This paper presents a spatially resolved estimate of the mass and volume of wind turbine blade waste in each state by 2050 and compares these amounts to estimates of the remaining landfill capacity by state. We estimate costs for each stage of the disposal process to indicate cost levels for alternatives. Assuming a 20-year turbine lifetime, the cumulative blade waste in 2050 is approximately 2.2 million tons. This value represents approximately 1% of remaining landfill capacity by volume, or 0.2% by mass. We also find that the current cost of disposing of blades in large segments or through grinding is relatively low in comparison to the overall life-cycle cost of energy. Based on these findings, landfill space constraints and disposal costs appear unlikely to motivate a change in waste handling strategies under current policy conditions. Instead, more profound shifts in recycling technologies, blade materials, or policy may be needed to move towards a circular economy for wind turbine blades.

Deep Learning Method for Fault Detection of Wind Turbine Converter

Abstract: The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.