Research on fault detection for three types of wind turbine subsystems using machine learning

2020 by the authors. In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective

Reliability modeling and analysis for a novel design of modular converter system of wind turbines

Converters play a vital role in wind turbines. The concept of modularity is gaining in popularity in converter design for modern wind turbines in order to achieve high reliability as well as cost-effectiveness. In this study, we are concerned with a novel topology of modular converter invented by Hjort, Modular converter system with interchangeable converter modules. World Intellectual Property Organization, Pub. No. WO29027520 A2; 5 March 2009, in this architecture, the converter comprises a number of identical and interchangeable basic modules. Each module can operate in either AC/DC or DC/AC mode, depending on whether it functions on the generator or the grid side. Moreover, each module can be reconfigured from one side to the other, depending on the system\u27s operational requirements. This is a shining example of full-modular design. This paper aims to model and analyze the reliability of such a modular converter. A Markov modeling approach is applied to the system reliability analysis. In particular, six feasible converter system models based on Hjort\u27s architecture are investigated. Through numerical analyses and comparison, we provide insights and guidance for converter designers in their decision-making

Optimization of maintenances following proof tests for the final element of a safety-instrumented system

2019 The Authors Safety-instrumented systems (SISs) have been widely installed to prevent accidental events and mitigate their consequences. Mechanical final elements of SISs often become vulnerable with time due to degradations, but the particulars in SIS operations and assessment impede the adaption of state-of-art research results on maintenances into this domain. This paper models the degradation of SIS final element as a stochastic process. Based on the observed information during a proof test, it is essential to determine an optimal maintenance strategy by choosing a preventive maintenance (PM) or corrective maintenance (CM), as well deciding what degree of mitigation of degradation is enough in case of a PM. When the reasonable initiation situation of a PM and the optimal maintenance degree are identified, lifetime cost of the final element can be minimized while keeping satisfying the integrity level requirement for the SIS. A numerical example is introduced to illustrate how the presenting methods are used to examine the effects of maintenance strategies on cost and the average probability of failure on demands (PFDavg) of a SIS. Intervals of the upcoming tests thus can be updated to provide maintenance crews with more clues on cost-effective tests without weakening safety

Developing an optimum maintenance policy by life cycle cost analysis - a case study

This paper focuses on developing maintenance policies for critical assets to improve the production performance based on life cycle cost (LCC) analysis. A general approach is adopted for conducting the LCC analysis. The investigation is based on a case study to demonstrate how an optimum maintenance policy is determined. The relevant LCC structure in the case study is defined for the decision process which involves determination of the optimum life, repair limit and selection of materials, and trade-off between repair and replacement. The LCC analysis is based on statistical data modelling which facilitates decision-making on the optimal replacement of an asset and its remaining life. Based on the optimization and remaining life criterion, the optimal maintenance policy can be made. The results obtained from this case study include selection of the best lining material for use, determination of the optimal time for refractory lining replacement, the hot repair sequence required for maintaining the optimum condition and the repair limit for doing cold repairs before replacement, for one type of electric arc furnaces used in the steel industry

Managing maintenance resources for efficient asset utilization

Asset productivity is concerned with how an asset is efficiently and effectively deployed and utilized. It is related to maintenance resource management. The purpose of this paper is to discuss development of policies for managing integrated maintenance resources. These resources include human resource and supporting material required to perform maintenance activities for a complex maintenance system. Here, human resource management encompasses policy for recruitment, training, and outsourcing. Meanwhile, supporting material management includes policy for parts purchasing and inventory. Good asset productivity can be achieved by attaining a better performance of the asset using the same amount of maintenance resources or by reducing the amount of maintenance resources used for the same asset performance. A maintenance department may manage each kind of resources and have its own policy to achieve better asset productivity. In this way, an integrated policy with all related departments is required. In this research, a model to determine an integrated optimum policy with associated departments is developed. It consists of three sub models representing three different departments in an organisation including Maintenance, Human Resource, and Inventory and Purchasing department. Through the model, some combinations of the policies can be made and tested to find the best combined policy that, in turn, can help to generate better asset productivity

Selective Loss of Cysteine Residues and Disulphide Bonds in a Potato Proteinase Inhibitor II Family

Disulphide bonds between cysteine residues in proteins play a key role in protein folding, stability, and function. Loss of a disulphide bond is often associated with functional differentiation of the protein. The evolution of disulphide bonds is still actively debated; analysis of naturally occurring variants can promote understanding of the protein evolutionary process. One of the disulphide bond-containing protein families is the potato proteinase inhibitor II (PI-II, or Pin2, for short) superfamily, which is found in most solanaceous plants and participates in plant development, stress response, and defence. Each PI-II domain contains eight cysteine residues (8C), and two similar PI-II domains form a functional protein that has eight disulphide bonds and two non-identical reaction centres. It is still unclear which patterns and processes affect cysteine residue loss in PI-II. Through cDNA sequencing and data mining, we found six natural variants missing cysteine residues involved in one or two disulphide bonds at the first reaction centre. We named these variants Pi7C and Pi6C for the proteins missing one or two pairs of cysteine residues, respectively. This PI-II-7C/6C family was found exclusively in potato. The missing cysteine residues were in bonding pairs but distant from one another at the nucleotide/protein sequence level. The non-synonymous/synonymous substitution (Ka/Ks) ratio analysis suggested a positive evolutionary gene selection for Pi6C and various Pi7C. The selective deletion of the first reaction centre cysteine residues that are structure-level-paired but sequence-level-distant in PI-II illustrates the flexibility of PI-II domains and suggests the functionality of their transient gene versions during evolution

Choosing an optimal model for failure data analysis by graphical approach

Many models involving combination of multiple Weibull distributions, modification of Weibull distribution or extension of its modified ones, etc. have been developed to model a given set of failure data. The application of these models to modeling a given data set can be based on plotting the data on Weibull probability paper (WPP). Of them, two or more models are appropriate to model one typical shape of the fitting plot, whereas a specific model may be fit for analyzing different shapes of the plots. Hence, a problem arises, that is how to choose an optimal model for a given data set and how to model the data. The motivation of this paper is to address this issue. This paper summarizes the characteristics of Weibull-related models with more than three parameters including sectional models involving two or three Weibull distributions, competing risk model and mixed Weibull model. The models as discussed in this present paper are appropriate to model the data of which the shapes of plots on WPP can be concave, convex, S-shaped or inversely S-shaped. Then, the method for model selection is proposed, which is based on the shapes of the fitting plots. The main procedure for parameter estimation of the models is described accordingly. In addition, the range of data plots on WPP is clearly highlighted from the practical point of view. To note this is important as mathematical analysis of a model with neglecting the applicable range of the model plot will incur discrepancy or big errors in model selection and parameter estimates. © 2013 Elsevier Ltd. All rights reserved

Variable speed control of wind turbines based on the quasi-continuous high-order sliding mode method

The characteristics of wind turbine systems such as nonlinearity, uncertainty and strong coupling, as well as external interference, present great challenges in wind turbine controller design. In this paper, a quasi-continuous high-order sliding mode method is used to design controllers due to its strong robustness to external disturbances, unmodeled dynamics and parameter uncertainties. It can also effectively suppress the chattering toward which the traditional sliding mode control method is ineffective. In this study, the strategy of designing speed controllers based on the quasi-continuous high order sliding mode method is proposed to ensure the wind turbine works well in different wind modes. First, the plant model of the variable speed control system is built as a linearized model; and then a second order speed controller is designed for the model and its stability is proved. Finally, the designed controller is applied to wind turbine pitch control. Based on the simulation results from a simulation of 1200 s which contains almost all wind speed modes, it is shown that the pitch angle can be rapidly adjusted according to wind speed change by the designed controller. Hence, the output power is maintained at the rated value corresponding to the wind speed. In addition, the robustness of the system is verified. Meanwhile, the chattering is found to be effectively suppressed

Approaches to Dealing with Missing Data in Railway Asset Management

The collection of reliable and high-quality data is seen as a prerequisite for effective and efficient rail infrastructure and rolling stock asset management to meet the requirements of asset owners and service providers. In this paper, the importance of recovering missing information in railway asset management is highlighted, and the advanced models and algorithms that have been applied to recovering the missed data are analyzed and discussed. Through making comparisons among these models and algorithms, a procedure is proposed to guide selecting the appropriate models based on different data missing scenarios. Using the newly developed framework with one dataset from each scenario, new models with different structures are trained and finally, the most suitable model is selected and utilized to recover the missing data and the selected model\u27s performance is evaluated using the data with known or clearly identified missing data mechanisms. Challenges via application of advanced algorithms for recovering missing data are discussed