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«Energies 2014, 7, 2595-2630; doi:10.3390/en7042595 OPEN ACCESS energies ISSN 1996-1073 Review Wind Turbine Condition ...»

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Energies 2014, 7, 2595-2630; doi:10.3390/en7042595

OPEN ACCESS

energies

ISSN 1996-1073

www.mdpi.com/journal/energies

Review

Wind Turbine Condition Monitoring: State-of-the-Art Review,

New Trends, and Future Challenges

Pierre Tchakoua 1,2,*, René Wamkeue 1,2, Mohand Ouhrouche 1, Fouad Slaoui-Hasnaoui 2,

Tommy Andy Tameghe 1,2 and Gabriel Ekemb 1,2

Department of Applied Sciences, University of Quebec, Chicoutimi, QC G7H 2B1, Canada;

E-Mails: rene.wamkeue@uqat.ca (R.W.); mohand_ouhrouche@uqac.ca (M.O.);

tattameghe@uqac.ca (T.A.T.); gabriel.ekemb@uqat.ca (G.E.) School of Engineering, University of Quebec, Rouyn-Noranda, QC J9X 5E4, Canada;

E-Mail: fouad.slaoui-hasnaoui@uqat.ca * Author to whom correspondence should be addressed; E-Mail: pierre.tchakoua-takoutsing@uqat.ca;

Tel.: +1-819-762-0971 (ext. 4382); Fax: +1-819-797-4727.

Received: 14 February 2014; in revised form: 23 March 2014 / Accepted: 24 March 2014 / Published: 22 April 2014 Abstract: As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today’s WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.

Keywords: wind turbines (WTs); condition monitoring; fault detection; destructive tests;

non-destructive tests; subsystem monitoring techniques; overall system monitoring techniques;

state of the art; new trends; future challenges Energies 2014, 7 2596

1. Introduction Energy conversion and efficiency improvement have become a worldwide priority to secure an energy supply and address the challenges of climate change, greenhouse gas emission reduction, biodiversity protection, and renewable technology development. In 2011, renewable sources accounted for nearly 50% of the estimated globally added electric capacity evaluated at 208 GW [1]. Among all renewable energy sources, wind energy is the fastest-growing sector in terms of installed capacity.

As shown in Figure 1, the cumulative installed wind power capacity reached 283 GW in 2011, which represents nearly 3% of global electricity production. Furthermore, the contribution of wind power to the world total generation capacity is expected to reach 8% by 2018 [1–3].

Figure 1. Wind energy world market forecast for 2013–2017 [1].

Reprinted/Reproduced with permission from [1]. Copyright 2013, Global Wind Energy Council (GWEC).

18.7% 14.4% 13.9% 14.1% 13.4% 12.89% 10.8% 14.0% 12.6% 10.2% 8.90% −11.6% Wind turbines (WTs) are unmanned, remote power plants. Unlike conventional power stations, WTs are exposed to highly variable and harsh weather conditions, including calm to severe winds, tropical heat, lightning, arctic cold, hail, and snow. Due to these external variations, WTs undergo constantly changing loads, which result in highly variable operational conditions that lead to intense mechanical stress [4]. Consequently, the operational unavailability of WTs reaches 3% of the lifetime of a WT. Moreover, operation and maintenance (OM) costs can account for 10%–20% of the total cost of energy (COE) for a wind project, and this percentage can reach 35% for a WT at the end of life.

A preventive-centered maintenance strategy that avoids machine shutdown can considerably reduce these costs [5–7]. Therefore, WTs require a high degree of maintenance to provide a safe, cost-effective, and reliable power output with acceptable equipment life. The state-of-the-art method for determining the maintenance strategy in the WT industry is reliability-centered maintenance (RCM), which consists of preventive maintenance based on performance and/or parameter monitoring and subsequent actions.

In this strategy, condition-monitoring (CM) is used to determine the optimum point between corrective and scheduled maintenance strategies [8–11]. The recurrent and commonly used Energies 2014, 7 2597 condition-monitoring techniques (CMTs) are: (i) vibration/acoustic-controlled and OM techniques for the turbine; and (ii) optical strain gauges for the blades.

The WTs are typically designed to operate for a period of 20 years [12,13]. As with other mechanical systems, time-based maintenance assumes that the failure behavior of WTs is predictable.

Fundamentally, three failure patterns describe the failure characteristics of WT mechanical systems [14].

The bathtub curve shown in Figure 2 illustrates the hypothetical failure rate versus time in a mechanical system [15–18], where β ˂ 1 represents a decreasing failure rate, β = 1 represents a constant failure rate, and β ˃ 1 represents an increasing failure rate.





Figure 2. The “bathtub” curve illustrating the reliability of technical systems.

Guo et al. [19] developed a three-parameter Weibull failure rate function for WTs, and their results corroborate the bathtub curve. Echavarria et al. [12] published results of a remarkable 15-year research study on the frequency of failures versus increasing operational age for various WT power ratings (Figure 3).

Figure 3. Number of incidents per wind turbine (WT) per operational year; WTs are categorized by rated power [12].

Reprinted/Reproduced with permission from [12].

Copyright 2008, American Society of Mechanical Engineers.

6.0 5.0 4.0 3.0 2.0 1.0

–  –  –

The frequency of failures in WTs also varies with the scale and type. Spinato et al. [18,20] carried out a failure analysis based on onshore WT types, as specified in the Schleswig Holstein Landwirtschaftskammer (LWK) database. The work displayed a general trend of an increasing failure rate with turbine size. Because turbine capacity continues to grow, we can assume that it will be difficult to decrease the initial failure rate. Several research studies considered the distribution of WT failures in the main components [13,20,21]. Haln et al. [13] reported a survey of 1500 WTs over 15 years and found that five component groups, i.e., electrical system, control system, hydraulic system, sensors, and rotor blades, are responsible for 67% of failures in WTs, as shown by the pie chart in Figure 4.

Figure 4. Share of the main components of the total number of failures [13].

Reprinted/Reproduced with permission from [13]. Copyright 2007, Springer Science + Business Media.

To establish the impact of component failure on WT reliability, research centered on the availability of WTs was presented in [8,22–25]. The results published by Fischer et al. [8] indicated that 75% of the annual downtime is caused by only 15% of the failures in WTs. This result corroborates the conclusions of Haln et al. [13], regarding the average failure rate and average downtime per component. The results of this study are also in agreement with the conclusions of Crabtree et al. [24], regarding the comparison of failure rates and downtime for different WT subassemblies based on surveys of European wind-energy conversion systems (WECSs). The chart in Figure 5 summarizes the failure rate and downtime of different WT subassemblies. The reliability and downtime data of the Egmond aan Zee wind farm in Germany also produced similar results, i.e., the gearbox failure rate is low but the downtime and resultant costs are high. As a result, the percentage of electricity production lost due to gearbox downtime is the highest of all subassemblies [24].

A statistical analysis of WT faults demonstrates that their reliability and availability depend on multiple factors, i.e., age, size, weather, wind speed, and subassembly failure rates. However, applying efficient CMTs can greatly increase the reliability of WTs.

Energies 2014, 7 2599 Figure 5. Failure rates and downtime from two large surveys of European WTs over 13 years [13]. Reprinted/Reproduced with permission from [13]. Copyright 2007, Springer Science + Business Media.

In the literatures, few articles have provided a review of wind turbine condition monitoring (WTCM) and/or fault diagnosis [7,21,26–29]. The goal of this paper is to provide a review of methods and techniques for WTCM with a classification of: (i) intrusive and nonintrusive techniques;

and (ii) destructive techniques and non-destructive techniques. This work also focuses on trends and future challenges in the WTCM industry. The paper is organized as follows: Section 2 is dedicated to CM-related concepts and definitions and outlines the relationships among CM, fault diagnosis, and fault prognostic and maintenance strategies; Section 3 presents a review of techniques and methods used in WECSs and CM, subdividing them into subsystem techniques and overall system techniques as well as destructive and non-destructive techniques; Section 4 discusses the new trends and future challenges that will enable the industry to address the WT challenges of the future, including reducing operational costs and improving reliability; finally, Section 5 provides conclusions to the work.

2. Concepts and Definitions

2.1. Maintenance Approaches As in most industries, maintenance approaches in the WT industry can be widely classified into

three main groups [30,31]:

• Reactive or corrective maintenance (run to failure);

Preventive maintenance (time-based);

• Predictive maintenance (condition-based).

–  –  –

ICC ⋅ FCR + OM COE = (1) E where ICC and FRC are fixed parameters; and OM is a variable parameter that can affect the COE during the lifetime of the project. Therefore, the profit from wind energy is highly dependent on the ability to control and reduce this variable cost. The OM cost of equipment will notably depend on the maintenance strategy adopted by the user.

The cost associated with traditional maintenance strategies is presented in Figure 6 [30]. In a preventive maintenance strategy, the prevention cost will be quite high, whereas the repair cost will be low because many potential failures will not occur. In other words, preventive maintenance will considerably reduce the number of failures that occur but will be expensive. In a reactive maintenance strategy, a greater number of faults will occur and will lead to a high cost of repair and low cost of prevention. As shown on the graph, a combination of preventive and reactive maintenance strategies can improve the reliability, availability, and maintainability of WTs while simultaneously reducing the maintenance cost [4,6,30,36].

–  –  –

of techniques, i.e., vibration analysis (VA), acoustics, oil analysis (OA), strain measurement (SM), and thermography [27]. Data are sampled at regular time intervals using sensors and measurement systems.

Using data processing and analyses, CMSs can determine the states of the key WECS components.

By processing the data history, faults can be detected (diagnosis) or predicted (prognostic) and the appropriate maintenance strategy can be chosen.

Maintenance includes any actions appropriate for retaining equipment in or restoring it to a given condition [31]. Maintenance is required to ensure that the components continue to perform the

functions for which they were designed. The basic objectives of the maintenance activity are to:

(i) deploy the minimum resources required; (ii) ensure system reliability; and (iii) recover from breakdowns [41]. The applied maintenance strategy can be preventive if a predicted failure is avoided or corrective when a detected failure is repaired [42].

A description of and models for CMSs can be found in [27,39,43,44]. This description can be combined with concepts definitions provided in [14,31,45–48], which address maintenance techniques and methods. The diagram relating technical concepts and words used in the domain of WTCM and fault diagnosis emerges from the aforementioned combination. As shown in Figure 7, CM is performed in three main steps: data acquisition using sensors, signal processing using various data processing techniques, and feature extraction via the retrieval of parameters that will aid in establishing the current status of the monitored equipment. Using both: (i) current information sources;

and (ii) information on the system’s past status obtained from stored data, the system’s present state is obtained via online monitoring such that a fault can be detected or predicted. After a fault is diagnosed, corrective maintenance is carried out. Two approaches to corrective maintenance can be distinguished, i.e., palliative maintenance, which consists of provisional solutions to failures, and curative maintenance for standing solutions to failures. If a fault is predicted, preventive maintenance is carried out before the fault can occur. In this case, four different approaches can be used: time-based or scheduled maintenance, current-state based or conditional maintenance, parameter-projection-based or forecasting maintenance, and status-based or proactive maintenance.

–  –  –

3. Review of Concepts and Methods for WTCM According to the Swedish standard SS-EN 13306 [49], monitoring can be defined as an activity performed either manually or automatically that is intended to observe the actual state of an item.



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