Reliability Of Turbines Research Articles

Testing of hydrophobically coated composite materials with marine energy applications

Tidal turbine applications will place composite materials such as glass-fibre-reinforced polymers (GFRPs) and carbon-fibre-reinforced polymers under surprisingly high pressure due to the significant water depths they operate in (to depths of 50 m). The technical reliability of tidal turbines must be guaranteed despite the high dynamic loads resulting from the combination of plant operation, wind and waves with irregular swell. These external effects could lead to operation downtime and accelerated degradation. Research has already established that (unpressurised) moisture saturation is detrimental to both the tensile and fatigue strengths of a GFRP material compared with its dry laminate performance. This degradation has been shown to be stress dependent. It is possible that hydrophobic coatings could help protect the material while in service. Therefore, two commercially available coatings were applied to five variations of composite materials. These materials are commonly used in tidal and wave turbine blades, as they have excellent corrosion resistance and mechanical properties. All five were subjected to the same conditioning processes: non-pressurised and pressurised immersion conditioning in water. A glass-fibre epoxy (Ampreg) composite, along with a glass-fibre powdered epoxy composite, a glass-fibre polyether ether ketone composite and a carbon-fibre powdered epoxy composite, was investigated. The glass-fibre epoxy (Ampreg) was also subjected to static/fatigue tensile tests.

An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

Reliability model and sensitivity analysis for wind turbine with failure types based on non-identical correlated components

ABSTRACT Today, wind turbines have a high cost of maintenance. Therefore, designing them with reliable and efficient systems is essential for longer lifespans. When wind turbine components fail, it becomes harmful in various aspects such as economic and environmental pollution, therefore, accurate estimation of wind turbine components reliability is very important. Previous methods to calculate wind turbine reliability assumed that components failures are statistically independent. This paper presents a new technique for the reliability of wind turbine with dependent components failures. The results show that existing components correlation has a major impact, and ignoring it has a significant effect on wind turbine safety.

Analysis on operation reliability of wind power units in China

As an important direction of renewable energy, wind energy has developed rapidly, but there are also many problems. The reliability of wind turbines has received wide attention. This paper mainly studies and analyzes the development of wind turbines, basic structure, operation and maintenance of the unit, and then puts forward suggestions for improving the reliability of wind turbines in China.

ПОВЫШЕНИЕ ЭНЕРГЕТИЧЕСКОЙ И ЭКОЛОГИЧЕСКОЙ ЭФФЕКТИВНОСТИ СИСТЕМ ГАЗООЧИСТКИ НА ТЭС

Актуальность исследования обусловлена необходимостью повышения энергетической и экологической эффективности систем очистки выбросов систем пылеприготовления и дымовых газов ТЭС при угольной генерации и систем подготовки газа при газовой генерации, во исполнение Федерального закона № 261-ФЗ от 23.11.2009 г. (ред. от 29.07.2017) «Об энергосбережении и о повышении энергетической эффективности и о внесении изменений в отдельные законодательные акты Российской Федерации», а также требований по контролю за выбросом парниковых газов (напр., приказа № 330 от 29.06.2017 года «Об утверждении методических указаний по количественному определению объёма косвенных энергетических выбросов парниковых газов» и др.) выбросов систем пылеприготовления и дымовых газов ТЭС с угольной генерацией и в системе подготовки газа с газовой генерацией. Цель: разработка устройства циклонной фильтрации для повышения эффективности систем газоочистки на ТЭС, снижения выбросов взвешенных частиц в атмосферу от систем пылеприготовления и дымовых газов при угольной генерации и увеличения надежности работы агрегатов газотурбинных и парогазовых установок ТЭС вследствие снижения износа рабочих поверхностей поршневых и винтовых дожимающих компрессоров дожимных компрессорных станций, предотвращения попадания продуктов внутренней коррозии газопроводов в газотурбинных установках, в соответствии с п.п. 93, 94 ФНиП в области промышленной безопасности «Правила безопасности сетей газораспределения и газопотребления» (Серия 12, вып. 13). Объект: модель циклона на базе ЦН-11-200, обеспечивающая повышение энергетической и экологической эффективности систем газоочистки на ТЭС. Методы: стендовые испытания циклонного фильтра, изготовленного путем модернизации циклона ЦН-11-200, аэродинамические расчёты; численное моделирование аэродинамических параметров работы циклона-фильтра на основе методов вычислительной гидродинамики. Для математического моделирования процесса сепарации использована система уравнений, состоящая из осредненных по Рейнольдсу уравнений движения однофазного потока Навье–Стокса и уравнения движения частиц, основанного на законе Ньютона. Для определения эффективности отделения взвешенной части потока в циклоне-фильтре использован безразмерный комплекс Rer, с помощью которого численные значения параметров осаждения взвеси из многофазного потока в сепараторе найдены расчетным путем. Результаты. Анализ литературных источников показал отсутствие конструкций циклонов-фильтров, которые обеспечивали бы надлежащую (более 98 %) степень очистки выбросов без резкого увеличения энергозатрат на обработку выбросов. Проведенные опыты показали целесообразность конструктивного дополнения возвратно-поточных циклонов фильтрующей вставкой в зоне, где происходит инерционное осаждение пыли. Для определения эффективности отделения взвешенной части потока в данном циклоне-фильтре был использован безразмерный комплекс Rer. Расчеты показали соответствие результатов теоретических и натурных исследований.

Wind turbine reliability data review and impacts on levelised cost of energy

AbstractReliability is critical to the design, operation, maintenance, and performance assessment and improvement of wind turbines (WTs). This paper systematically reviews publicly available reliability data for both onshore and offshore WTs and investigates the impacts of reliability on the cost of energy. WT failure rates and downtimes, broken down by subassembly, are collated from 18 publicly available databases including over 18 000 WTs, corresponding to over 90 000 turbine‐years. The data are classified based on the types of data collected (failure rate and stop rate) and by onshore and offshore populations. A comprehensive analysis is performed to investigate WT subassembly reliability data variations, identify critical subassemblies, compare onshore and offshore WT reliability, and understand possible sources of uncertainty. Large variations in both failure rates and downtimes are observed, and the skew in failure rate distribution implies that large databases with low failure rates, despite their diverse populations, are less uncertain than more targeted surveys, which are easily skewed by WT type failures. A model is presented to evaluate the levelised cost of energy as a function of WT failure rates and downtimes. A numerical study proves a strong and nonlinear relationship between WT reliability and operation and maintenance expenditure as well as annual energy production. Together with the cost analysis model, the findings can help WT operators identify the optimal degree of reliability improvement to minimise the levelised cost of energy.

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.

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

Method on condition assessment of pitch system based on fuzzy matter-element analysis

In order to improve the real-time reliability of wind turbine and solve the effect of artificial factors, fuzzy matter-element analysis is proposed. The pitch system, which has high failure rate, is researched in this paper. The condition assessment model of pitch system is built in two steps: (1) To avoid the subjective judgment of the parameter distribution, this thesis uses 3σ rule and quartile analysis method to get the boundary data sets. The ANFIS algorithm is used to train the data to reduce the extreme value, and results of multi-feature fault detection are obtained. (2) Results from fault detection are applied in condition assessment model based on fuzzy matter-element analysis to realize the unification of fuzzy value of detection result and grade assessment. The method is applied to test the actual operation state of wind turbine. The results show that it can obviously reflect the pitch system and has better assessment effects.

Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks.

A Statistical State Analysis of a Marine Gas Turbine

This paper describes the analysis, from a statistical point of view, of a maritime gas turbine, under various operating conditions, so as to determine its state. The data used concerns several functioning parameters of the turbines, such as temperatures and vibrations, environmental data, such as surrounding temperature, and past failures or quasi-failures of the equipment. The determination of the Mean Time Between Failures (MTBF) gives a rough estimate of the state of the turbine, but in this paper we show that it can be greatly improved with graphical and statistical analysis of data measured during operation. We apply the Laplace Test and calculate the gas turbine reliability using that data, to define the gas turbine failure tendency. Using these techniques, we can have a better estimate of the turbine’s state, and design a preventive observation, inspection and intervention plan.

An Advanced Extreme Environment Wireless Telemetry System for Turbine Blade Instrumentation

ABSTRACT As advanced natural gas power generation systems evolve, the thrust for increased efficiencies and reduced emissions results in increasingly harsh conditions inside the turbine environment. These high temperatures, pressures, and corrosive atmospheres result in accelerated rates of degradation, leading to failure of turbine materials and components. The University of Arkansas (UA) and Siemens, in collaboration with the DoE's National Energy Technology Laboratory (NETL), are developing a reliable and long-term monitoring capability in the turbine hot gas path in the form of novel ceramic-based thermocouples and integrated wide band gap instrumentation electronics that will contribute to the overall reliability of gas turbines. When equipped with better monitoring and controls, power plants can operate with increased fuel-burning efficiency, improved process dynamics and gas concentrations, and increased overall longevity of the power plant components. This will result in increased turbine availability and a reduction in outages and maintenance costs. One of the key aspects to driving forward turbine monitoring capability is the development of high temperature capable integrated circuit (IC) electronics. Previous papers have described 500 °C + electronics that were developed primarily from a combination of discrete single transistors combined with supporting high temperature passive components. While these circuits have been tested successfully in high temperature spin test environments, the move to an IC approach will greatly increase the performance and reliability of turbine monitoring systems. This program is developing such capability through the implementation of silicon carbide (SiC) based ICs, and this paper details the initial approach and early testing of the developed devices. This research represents an important step towards the realization of a field deployable high reliability turbine condition monitoring system.

In-Service Wind Turbine DFIG Diagnosis Using Current Signature Analysis

Operation and maintenance costs of wind power plants represent a significant share of the total expenditure of a wind farm project. It is therefore vital to optimize maintenance activities in order to reduce costs while improving wind turbine reliability and availability. The induction generator is a major contributor to failure rates and downtime of wind turbines, where doubly fed induction generators (DFIGs) are the dominant variable-speed technology employed. Actual data from operating wind turbines is seldom presented in the scientific literature. This paper analyzes two in-service DFIGs from wind turbines operating in a Spanish wind power plant. One of the generators under study reported excessive temperature on the rotor windings, while the other did not. In order to achieve a diagnosis for the reported excess of temperature, current signature analyses were performed on both machines. Fault-related frequency components were identified in the current spectra of the faulty machine, and were compared against the healthy one. A diagnosis was achieved for the faulty machine: Dynamic eccentricity caused by a rotor fault was identified as the cause of the excessive temperature in the rotor windings.

Flexible dynamic modeling and analysis of drive train for Offshore Floating Wind Turbine

Drive train has a significant influence on the reliability of wind turbines. As for the Offshore Floating Wind Turbine (OFWT), the importance of the drive train is even more prominent due to the more complex operating conditions at the ocean. In this study, dynamic characteristics of the OFWT drive train are investigated based on flexible dynamic model. First, this paper presents a flexible dynamic model of the drive train, which includes not only the full coupling of gear meshing but also the flexibilities of planet carrier and ring gear. Then, a corresponding finite element model is established to verify the reliability of the proposed model by comparing natural frequencies and vibration responses. Afterwards, dynamic characteristics of the drive train are analyzed under different excitations, including the time-varying mesh stiffness, wind turbulence, tower shadow, wind shear and platform motions. Results show that resonant peaks of the system are more likely to appear when the mesh frequency or its multiplication of the gear pair 1–2 is equal to the natural frequency. In addition, it is revealed that the tower shadow is the most significant excitation source for OFWT drive train, followed by platform pitch and surge motions.

An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples

Predictive maintenance has raised much research interest to improve the system reliability of a wind turbine. This paper presents a new model based approach of integrated fault diagnosis and prognosis for wind turbine remaining useful life estimation, especially the cases with limited degradation data. Firstly, a wavelet transform based fault diagnosis method is investigated to analyze the bearing incipient defect signatures, and the extracted features are then fused by the Health Index algorithm to represent the bearing defect conditions. Taking the empirical physical knowledge and statistical model in a Bayesian framework, the bearing remaining useful life prediction with uncertainty quantification is achieved by particle filter in a recursive manner. The integrated fault diagnosis and prognosis approach is validated using bearing lifetime test data acquired from a wind turbine in field, and the performance comparison with typical data driven technique outlines the significance of the presented method.

Thermal and economic optimisation of windfarm export cable

A method is proposed for the economic optimisation of offshore wind cable connections through utilising the thermal inertia of the cable system. Taking account of expected wind yields, turbine reliability and the dynamic thermal environment, the method determines the economically optimal number of turbines for a given export cable system. Analysis has shown that, providing means to actively curtail generation output so as to thermally protect the cable are in place, it is possible to reduce the cost of energy delivered to shore by up to £1/MWhr through the use of “over-planting”, i.e. building a wind farm that is larger than the continuous rating of its export cables.

Aggregate reliability analysis of wind turbine generators

In North America, many utility-scale turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non-parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records.

Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

Bearing is the major contributor to wind turbine gearbox failures. Accurate remaining useful life prediction for drivetrain gearboxes of wind turbines is of great importance to achieve condition-based maintenance to improve the wind turbine reliability and reduce the cost of wind power. However, remaining useful life prediction is a challenging work due to the limited monitoring data and the lack of an accurate physical fault degradation model. The particle filtering method has been used for the remaining useful life prediction of wind turbine drivetrain gearboxes, but suffers from the particle impoverishment problem due to a low particle diversity, which may lead to unsatisfactory prediction results. To solve this problem, this paper proposes an enhanced particle filtering algorithm in which an adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resampling process to solve the particle impoverishment problem. The enhanced particle filtering algorithm is applied successfully to predict the remaining useful life of a bearing in the drivetrain gearbox of a 2.5 MW wind turbine equipped with a doubly-fed induction generator.

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

The growth in the wind energy sector is demanding projects in which profitability must be ensured. To fulfil such aim, the levelized cost of energy should be reduced, and this can be done by enhancing the Operational Expenditure through excellence in Operations & Maintenance. There is a considerable amount of work in the literature that deals with several aspects regarding the maintenance of wind farms. Among the related works, several focus on describing the reliability of wind turbines and many set the spotlight on defining the optimal maintenance strategy. It is in this context where the presented work intends to contribute. In the paper a technical framework is proposed that considers the data and information requisites, integrated in a novel approach a clustering-based reliability model with a dynamic opportunistic maintenance policy. The technical framework is validated through a case study in which simulation mechanisms allow the implementation of a multi-objective optimization of the maintenance strategy for the lifecycle of a wind farm. The proposed approach is presented under a comprehensive perspective which enables the discovery an optimal trade-off among competing objectives in the Operations & Maintenance of wind energy projects.

Cross-entropy based importance sampling for stochastic simulation models

To efficiently evaluate system reliability based on Monte Carlo simulation, importance sampling is used widely. The optimal importance sampling density was derived in 1950s for the deterministic simulation model, which maps an input to an output deterministically, and is approximated in practice using various methods. For the stochastic simulation model whose output is random given an input, the optimal importance sampling density was derived only recently. In the existing literature, metamodel-based approaches have been used to approximate this optimal density. However, building a satisfactory metamodel is often difficult or time-consuming in practice. This paper proposes a cross-entropy based method, which is automatic and does not require specific domain knowledge. The proposed method uses an expectation–maximization algorithm to guide the choice of a mixture distribution model for approximating the optimal density. The method iteratively updates the approximated density to minimize its estimated discrepancy, measured by estimated cross-entropy, from the optimal density. The mixture model’s complexity is controlled using the cross-entropy information criterion. The method is empirically validated using extensive numerical studies and applied to a case study of evaluating the reliability of wind turbine using a stochastic simulation model.