Turbine Maintenance Research Articles

Wind power system reliability and maintenance optimization considering turbine and wind uncertainty

PurposeWind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.Design/methodology/approachWind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.FindingsThe wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.Originality/valueThis paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.

A DEMATEL Approach Based on Fuzzy Sets for Evaluating Critical Factors of Gas Turbine in Marine Engineering

In power production, gas turbines are commonly used components that generate high amount of energy depending on size and weight. They function as integral parts of helicopters, aircrafts, trains, ships, electrical generators, and tanks. Notably, many researchers are focusing on the design, operation, and maintenance of gas turbines. The focal point of this paper is a DEMATEL approach based on fuzzy sets, with the attempt to use these fuzzy sets explicitly. Using this approach, the cause–effect diagram of gas turbine failures expressed in the literature is generated and aimed to create a perspective for operators. The results of the study show that, “connecting shaft has been broken between turbine and gear box” selected the most important cause factor and “sufficient pressure fuel does not come for fuel pump” is selected the most important effect factor, according to the experts.

The effect of harness suspension on a simulated maintenance task efficacy in the renewable energy industry

Building, bridge or wind turbine maintenance requires manual dexterity tasks by a specialist rope-access trained workforce via two principal means: harness suspension of individual workers from above, or deployment of a suspended platform or cradle from which workers access the structure to be maintained. Currently no published research compares accuracy and efficiency of simulated maintenance tasks between these modalities. This study investigated manual dexterity task performance of peg placement and shape delineation in seated, standing and suspended environments in 16 healthy controls and 26 professional rope-access trained individuals. Both seated and standing assessments were superior to those suspended, and height of suspension, total mass and years of experience had no influence on the task outcome. These findings suggest that, where feasible, cradle suspension mechanisms which permit standing maintenance are favourable in terms of task efficacy and where feasible, should be considered for deployment in wind energy and other engineering applications.

Wind turbine performance analysis for energy cost minimization

The use of wind energy worldwide has overgrown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location, but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy data in the north and south of Oman and to provide well-informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0–200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to decarbonization of the energy sector in Oman, with a potential reduction of CO2 emission approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

An Adaptive Bayesian Approach With Subjective Logic Reliability Networks for Preventive Maintenance

This paper explores a Bayesian update strategy applying the experts' subjective viewpoints concerning the wind turbines' preventive maintenance. The approach is general enough to be employed in other than wind turbine maintenance applications. Although extant literature has examined the implementation of optimal adaptive Bayesian update strategies relating to the preventive maintenance time, they have not expanded the findings to the subjective views of wind farm managers or technicians. Against this backdrop, subjective opinions have been successfully deployed herein for Bayesian updates in the meantime the experts explore key distribution parameters without any prior expertise of statistics by merely putting forth opinions as disbelief, belief, or ambiguity. The choices, along with their concomitant effect on statistical parameters, including the minimized time of maintenance and new approach of costs, are presented before the wind farm manager and technician directly in the form of quantitative data, whereas their inputs are considered as opinion. Notably, such an approach adjuncts the quantitative data from turbine supervisory control and data acquisition (SCADA). As an integral component of turbine failures, the preventive maintenance pitch control device has been explored in this paper, which demonstrates the viability of this approach. A reliability networks Bayesian update strategy that develops the subjective opinions of variable and random costs are formulated and systematically investigated for preventive maintenance while several expert opinion has been implemented by the author in an earlier preliminary study. The reliability network is implemented for components where the SCADA does not provide clear data where the technician (expert) has opinion.

Offshore wind turbine reliability and operational simulation under uncertainties

AbstractThe fast‐growing offshore wind energy sector brings opportunities to provide a sustainable energy resource but also challenges in offshore wind turbine (OWT) operation and maintenance management. Existing operational simulation models assume deterministic input reliability and failure cost data, whereas OWT reliability and failure costs vary depending on several factors, and it is often not possible to specify them with certainty. This paper focuses on modelling reliability and failure cost uncertainties and their impacts on OWT operational and economic performance. First, we present a probabilistic method for modelling reliability data uncertainty with a quantitative parameter estimation from available reliability data resources. Then, failure cost uncertainty is modelled using fuzzy logic that relates a component's failure cost to its capital cost and downtime. A time‐sequential Monte Carlo simulation is presented to simulate operational sequences of OWT components. This operation profile is later fed into a fuzzy cost assessment and coupled with a wind power curve model to evaluate OWT availability, energy production, operational expenditures and levelised cost of energy. A case study with different sets of reliability data is presented, and the results show that impacts of uncertainty on OWT performance are magnified in databases with low components' reliability. In addition, both reliability and cost uncertainties can contribute to more than 10% of the cost of energy variation. This research can provide practitioners with methods to handle data uncertainties in reliability and operational simulation of OWTs and help them to quantify the variability and dependence of wind power performance on data uncertainties.

Design of a Multi-Robot System for Wind Turbine Maintenance

The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.

A Power Performance Online Assessment Method of a Wind Turbine Based on the Probabilistic Area Metric

This paper presents an approach for creating online assessment power curves by calculating the variations between the baseline and actual power curves. The actual power curve is divided into two regions based on the operation rules of a wind turbine, and the regions are individually assessed. The raw data are filtered using the control command, and outliers are detected using the density-based spatial clustering of applications with noise clustering method. The probabilistic area metric is applied to quantify the variations of the two power curves in the two regions. Based on this result, the variation in the power curves can be calculated, and the results can be used to dynamically evaluate the power performance of a wind turbine. The proposed method is verified against the derivation of secondary principal component method and traditional statistical methods. The potential applications of the proposed method in wind turbine maintenance activities are discussed.

Operations & Maintenance Optimization of Wind Turbines Integrating Wind and Aging Information

Operations & maintenance (O&M) of wind turbines (both onshore and offshore) are heavily affected by weather conditions, particularly wind conditions. Current O&M models focused mainly on negative impacts of wind conditions on turbine reliability and maintenance, while ignoring potential maintenance opportunities emerging from dynamic wind velocities. This article addresses this issue by constructing a novel weather-centered O&M framework, integrating wind impacts on: (a) energy production, and (b) maintenance plans. Both the positive (maintenance opportunities) and negative impacts (maintenance delays) of wind conditions are quantified in the framework. Accordingly, a weather-centered opportunistic maintenance policy is developed to enable a flexible maintenance resource allocation. The maintenance model is formulated, and analytical properties regarding optimal maintenance ages are discussed. Furthermore, the net revenue of wind turbines is evaluated using the performance-based contracting (PBC), which captures dynamics of both power generation and operational costs. Experimental results demonstrate the superior performance of our framework in revenue improvement, particularly when facing high failure risks and production losses.

An analysis of the burning accident of the tower bottom cabinet of G58-850KW wind turbine group

In March 2020, an accident happened in a wind farm in Jilin Province, which caused huge economic losses. Based on the investigation of the operation process at the scene of the accident, the burning condition of the equipment and the historical data of the relevant faults, it is found that the cause of this accident was the insulation aging between the low-voltage side turns of the transformer in the power converter cabinet. In the process of opening the main circuit of the equipment, the over-voltage phenomenon of the transformer occurs, which leads to the phenomenon of electric spark explosion. Through the accident analysis, a series of rectification measures are obtained, which will play a reference role for the subsequent maintenance and operation of wind turbines.

Investigation of E-Core Modular Permanent Magnet Wind Turbine

Under the adverse trend of fossil energy attenuation and air pollution, wind power effectively alleviates the global energy crisis and environmental pollution. For wind turbines, especially large offshore wind turbines, their transportation, installation, and maintenance are very inconvenient. In order to solve this problem, this paper presents an E-core stator modular machine which inserts stator gap into the unwounded teeth of the fractional-slot concentrated winding (FSCW) permanent magnet (PM) machine. The winding factor of the new stator structure machine was derived. The electromagnetic models of 12-slot/10-pole and 12-slot/14-pole modular FSCW PM machines and traditional FSCW PM machines were established using the finite element analysis (FEA) software, and the open-circuit flux density, cogging torque, load torque, loss, and efficiency were simulated and analyzed. The results showed that the modular structure of E-core stator not only simplified the transportation, installation, and maintenance of wind turbines, but also optimized the electromagnetic performance of the 12-slot/14-pole machine, i.e., improved the output torque and operation efficiency.

Automated wind turbine maintenance scheduling

While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.

Short-Term Reliability Prediction of Key Components of Wind Turbine Based on SCADA Data

In this paper, the Principal Component Analysis (PCA) method combined with the Radial Basis Function (RBF) neural network is used to establish a short-term reliability prediction model for wind turbines based on the SCADA data. The PCA method is used to reduce the dimensionality of the SCADA data and extract the principal components as the input data of the RBF neural network. The RBF neural network is used to predict the running state of key components of the wind turbine. Finally, a short-term reliability prediction model of wind turbines based on PCA-RBF is established. With the real wind farm SCADA data, the short-term reliability of wind turbine gearbox is predicted. The result shows that the short-term reliability prediction model can better reflect the reliability of key components and provide reference for the operation and maintenance of wind turbines.

Flume experiments on the impact of a cross-flow turbine on an erodible bed

Understanding the effect of tidal turbines on local erosion of the estuarine bed is crucial for design and maintenance of turbines with stable foundations and assessment of their environmental impacts. This report describes the results of flume experiments on clear-water scour caused by a single cross-flow turbine in steady flow conditions. The turbine investigated is a Momentum Reversal Lift (MRL) turbine originally designed in collaboration with the University of Exeter. Results show that the turbine can cause significant bed scour, particularly when it was not spinning and in a particular orientation of blades. This is opposite to the previous findings for axial flow turbines. The bottom plate of the turbine, although increasing scour depth, was found to increase the turbine performance and reduce adverse effects on the downstream flow. The findings highlight the importance of regular monitoring and taking immediate repair actions for a tidal installation.

Thermal Spray Processes for the Repair of Gas Turbine Components

Gas turbine components are often operated in harsh conditions, which can lead to severe damage. As it is highly desirable from both an economical and an ecological point of view to restore these worn areas instead of manufacturing new components, repair technologies are of huge interest for companies supplying maintenance and overhaul of gas turbines. In this article, two thermal techniques are described that can be used for this application: cold gas spraying (CGS) and vacuum plasma spraying (VPS). The CGS process allows the deposition of metallic coatings with excellent mechanical properties; several examples including γ‐TiAl, Inconel (IN) 718, and IN 738 are given. Essential for the deposition of high‐performance coatings in CGS is to exceed the so‐called critical velocity. This is discussed also with experimental findings. As a final topic, experiments that use VPS for the repair of single‐crystal alloys are described.

Predictive analytics as a way to smart maintenance of hydraulic turbines

Today, most energy companies face serious problems with the reliability and safety of large power equipment due to the long-term operation at off-design modes. This is especially true for hydraulic units that are traditionally used to ensure the required level of energy output and maintain the stability of a power grid due to their maneuverability. Off-design operational modes cause increased loads and stresses in the unit’s components, accelerate the growth of defects, stimulate premature failures, and can lead to a grave accident with large losses.Standard diagnostic systems for hydraulic units usually do not allow tracking hazardous defects such as fatigue cracks in the runner blades, guide vines, shaft, etc. The individuality of hydraulic units also excludes the use of any statistical methods to determine the time when the equipment will go to the limit state and its operation will become unreasonably dangerous. Therefore, the predictive analytics system is proposed as an additional approach to forecasting the appearance and growth of dangerous operational defects. The system realizes an analytical algorithm based on evaluating the fatigue strength of hydraulic unit elements under variable operating conditions and allows summing up damage from various external loads and in different time ranges.Input data for this predictive system is information about the actual operating time at every working mode and expected regime parameters for the upcoming period. The output data is information about actual and residual lifetime. All individual features are taken into account by the digital model which is a multidimensional matrix of equipment’s response to external influences. Data is generated in the cells for each unit’s elements with reference to the operating parameters and time scale.The proposed predictive analytics system allows not only to reduce the risk of accidents and unplanned shutdowns but it also is a way to smart maintenance of hydraulic turbines due to the development of the most effective, most reasonable, most lifetime-saving strategy of using the equipment.

A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines

From an operation & maintenance (O&M) point of view, it is necessary to model the aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine but, on the other side, wind farms generally include hundreds of wind turbines. Simply using and linking several advanced, single wind turbine models of dynamics to represent a wind farm can be computationally prohibitive. To this end, this paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable up to a wind farm level. First, a nonlinear AHSE model is used to derive the time-domain response of the wind turbine degrees of freedom (DOFs). The failure mode, its relevant DOF, and the relevant operational conditions during which the failure is likely to occur are identified. A linearisation of the nonlinear aero-hydro-servo-elastic-drivetrain (AHSE-DT) model is then carried out. Subsequently, a number of linear ROMs are developed based on the linear full-order system but excluding high-frequency states using the modal truncation (MT) method. For the targeted DOF (rotor torque signal) and the load cases simulated, the results from the linear ROMs showed that the blade modes are important to capture not only the DOF of extreme values, but also the DOF of high-frequency responses (above 1.5 Hz). The results from nonlinear ROMs showed that the ROM eliminating all the tower modes (rigid tower) is acceptable to capture the DOF of low-frequency response (below 0.5 Hz), as it has almost the same spectral responses as the full-order nonlinear model.

Optimal dynamic imperfect preventive maintenance of wind turbines based on general renewal processes

With the rapid growth in wind turbine technology worldwide, the high operational and maintenance costs of wind turbines have posed a major challenge to wind power operating companies. Considering the high replacement cost, imperfect maintenance measures are employed widely once downtime failures occur. However, as the metric to describe the effect of imperfect maintenance is a non-intuitive variable, the evaluation results obtained from existing research do not conform with actual wind turbine situations. To address this issue, the virtual age factor and failure intensity update factor are expressed by intuitive variables to illustrate the imperfect maintenance effect. Additionally, we propose a failure rate function update model considering the above factors. To minimise maintenance costs while ensuring the availability of the wind turbine, we investigate a periodic dynamic imperfect preventive maintenance decision model based on the proposed failure rate function update model. We also provide a brief illustration of the accuracy and feasibility of the proposed model through optimal solution and sensitivity analyses. The results obtained from the case analysis and strategies comparison, based on actual wind turbine maintenance data, demonstrate the economic advantages of our approach.

Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

The X-Rotor Offshore Wind Turbine Concept

The following paper provides an overview of a novel wind turbine concept known as the X-Rotor Offshore Wind Turbine. The X-Rotor is a new wind turbine concept that aims to reduce the cost of energy from offshore wind. Cost reductions are achieved through reduced capital costs and reduced maintenance costs. The following paper includes results from an early feasibility study completed on the concept. In the feasibility study exemplary designs were created and structural analyses were carried out. Turbine capital costs and maintenance cost of the X-Rotor concept were then roughly estimated. X-Rotor turbine costs and O&M costs were compared to four existing wind turbine types to investigate potential cost savings from the X-Rotor concept. Results show that the X-Rotor has potential to reduce O&M costs by up to 55% and capital costs by up to 32%. The combination of the capital cost and O&M cost savings show potential to reduce the CoE by up to 26%.