idsession
codetitle

182

C-1-1

A Fundamental Study and Factor
Analysis on System Health
Monitoring of Wind Turbines Using
SCADA

C-1-2

Key Performance Indicators forWind Farm's Operation andProf., Dr. Mario Orestes Aguirre
Maintenance: a sistematic literature
Gonzálezreview and proposal of a conceptualmodel for implementation

Federal University of Rio Grande doNorte

174

C-1-3

Lowering Cost of Energy by
expanding the live time of windturbines – live time extension from a Mr. Frank Weisecertification and manufacturing
perspective

Deutsche WindGuard

50

C-1-4

Technologies for the structuralhealth management of Hitachi-wind Dr. Yasuki Kita
turbine system

Hitachi,Ltd. ,Research &Development Group

C-1-5

Condition Based Maintenance ofWind Turbines by 24 / 7 Monitoringof Oil Quality, Oil Aging and Additive
Dr. Mauntz ManfredConsumption: Identification ofcritical operation conditions &determination of the next oilexchange

cmc Instruments GmbH

198

C-1-6

Development and Evaluation ofVibration-based Anomaly Detection
Dr. Jun Ogata
System Using Actual Wind Turbine
Data

National Institute of Advanced
Industrial Science and Technology(AIST)

112

C-1-7

Life Time Extension: How tomaximize the return of investment

UL International GmbH

168

C-1-8

Failure rate and downtime analysis
for wind turbine by using failure and Mr. Hirotaka Okumoto
accident survey

183

79

Presenter name

Dr. Akihisa Yasuda

Mr. Santiago Lopez Camblor

Affiliation

Research Center for Advanced
Science and Technology, The
University of Tokyo

E&E Solutions Inc1/5

31-Oct. 13:30 - 15:45 [ Operation＆maintenance (1) ]A Fundamental Study and Factor Analysis on System Health Monitoring ofWind Turbines Using SCADAAkihisa Yasuda1, Jun Ogata2, Masahiro Murakawa2, Hiroyuki Morikawa1 and Makoto Iida1
1Research Center for Advanced Science and Technology, The University of Tokyo,
2National Institute of Advanced Industrial Science and Technology

Abstract:
Wind turbines are the major driving force to produce renewable energy and an effective means to reduce CO2 emissions.
They achieve this by converting kinetic energy into electrical energy, but by doing so, the physical friction gradually damages
internal equipment. This may cause internal equipment, such as the gear box to fail, which in term results in stopping the windturbine for a long period of time and causes substantial losses to the turbines owner. Therefore, It is important to detect the fault
symptoms of the wind turbine as soon as possible and notify at which time to apply the appropriate maintenance. In this paper,
we present a low-cost analysis method which uses the data collected by Supervisory Control And Data Acquisition(SCADA)
system as the data source to evaluate the system health of the wind turbines, without using the data collected by an additional
system such as Condition Monitoring System(CMS). When we use only SCADA data, we are limited to low resolution data andunable to use the vibration data which is sensitive to minor damages of the internal equipment. Therefore, it is necessary toselect the appropriate analysis method that matches the characteristics of the SCADA data. Specifically, we regard the windturbine as a macro energy conversion device to build a model which represents the performance condition of the wind turbine
using SCADA data and machine learning. We evaluate the residual between the prediction value and the actual value of timeseries SCADA data. The prediction value is generated by the regression model using the SCADA data taken during normal
operation as the training data. We obtain the correlation result between the change point of operating performance and theperiod in which the wind turbine has failed.

Key Performance Indicators for Wind Farm's Operation andMaintenance: a sistematic literature review and proposal of a conceptualmodel for implementation
Marllen Aylla Teixeira Dos Santos, Thaysa Da Silva Barbosa, Andressa Medeiros Santiso,
Lara Luana Cirilo Silva, Marrison Gabriel Guedes Souza and Mario Orestes Aguirre González
Federal University of Rio Grande do Norte

Abstract:
Brazil has experienced serious energy crisis due to water scarcity in many regions, given that thepredominant source in thenational energy matrix is hydroelectric. In order to reduce this risk, an investment on the diversification of the matrix throughadditional sources is required, such as wind energy, which has had an explosive development in the country in recent years.
Considering this scenario, and in order to improve the performance of the Brazilian wind farms, the application of performancemeasurement is required to enable gains in generation efficiency, making it possible to increase the competitiveness of the wind
power source. This paper proposes a set of key performance indicators that can be used to provide relevant information in
support of decisionmaking processes involving the operation and maintenance of wind farms, enabling the redefinition ofstrategies and promoting a reformulation of policies effective to this sector to support the improvement of the generation. A
systematic literature review in the literature on the characteristics of a performance measurement system and the Brazilian windenergy market, as
well as the performance indicators that are currently used in power plants was performed. It was found that several studies
have been using data envelopment analysis to evaluate the performance of the plants. In addition, the Balanced Score Card
applications and specific mathematical models have also been used. The indicators identified in the research were grouped by
the organizational perspective to which they are related. The proposed conceptual model consists of 30 performance indicators
divided on the perspectives: Capacity Management, Maintenance Management, Workers Management and FinancialManagement.

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31-Oct. 13:30 - 15:45 [ Operation＆maintenance (1) ]Lowering Cost of Energy by expanding the live time of wind turbines– live time extension from a certification and manufacturing perspective
Ines Heger1 and Frank Weise2
1Potsdam Institute on Climate Impact Research (PIK), 2Deutsche WindGuard

Abstract:
Usually, regulations and certification requirements state, that ""the design life of the wind turbine is accepted with at least 20years.""1 And as a rule, onshore wind turbines have been sized for an operating time of exactly 20 years, because every design
life greater than 20 years would theoretically led to increased manufacturing costs and thus higher prices. However, the design
lifetime is a theoretical assumption and is also referred to as the scheduled service life. For this period, evidence on
performance and serviceability are given. After that time, there is an economic and ecological interest to continue turbine
operation instead of dismantling (e.g. scrapping) it after reaching the planned life time. With improvements in efficiency,
process control and continuous optimization strategies in manufacturing, the quality of turbines had increased considerably inrecent years. As a result, aspects of certification and support schemes in regard to turbine live time extension are in discussion
and new regulations have been released, with different intensity depending on the country and its indicative trajectory of windenergy.
The authors provide an overview on the current discussion and insights on manufacturing and certification strategies inregard to that issue, with focus on Germany.

References
Weise, F. (2015): Das Signal geht an alle Hersteller. In: Windindustrie in Deutschland.
Fraunhofer IWES (2014): Windenergie Report Deutschland 2014. Fraunhofer Verlag, Stuttgart.
Fraunhofer ISE (2013): Levelized Cost Of Electricity Renewable Energy Technologies. Fraunhofer Verlag,Stuttgart.

Technologies for the structural health management of Hitachi-wind turbine system
Yasuki Kita, Norio Takeda, Tatsuya Kameyama, Tatsuji Noma, Kazuo Muto, Keiro Muro,
Hiromitsu Nakagawa, Tomoaki Yamashita, Hiroshi Shintani and Yosuke Ueki
Hitachi,Ltd. ,Research & Development Group

Abstract:
Ensuring the reliability of the wind turbine system is the most important task not only for the safe operation but also
maximizing the profit of operators. Hitachi has been promoting the development of technologies for visualizing and improving
the reliability of Hitachi-wind turbine system. In this talk, our technologies related to the structural health evaluation for windturbine components such as the tower and the blade will be focused on. One of the key technologies is a wireless strain
measurement system. The biggest difficulty of measuring strain on rotating components such as rotor blades is theestablishment of electric connections. Although a slip ring system provides the connection including data signal transmission
and power supply, the limitations are the number of channel and the complexity of the cable arrangement, which is directly
related to the cost of the implementation. To overcome these problems, we have developed a wireless strain measurement
system consisting of a power saving-sensor nodes, wireless gateways and a central controlling personal computer which isconnected to the internet. We have confirmed the feasibility of this setup in our 5MW downwind turbine in Kashima, Ibaraki,
Japan. In case of rotor blades, sensor nodes were attached to the internal side of blades composed of glass fiber-resin composite
material and the gateways were fixed to the nacelle. It was confirmed that strain signal from rotating blades could be capturedsuccessfully using this system and the signal was revealed to be same as strain observed by a optically wired strain
measurement system, which is already well known methodology for wind turbine blades. As well as sensing technologies asindicated above, we developed a cloud computing-based data analytics platform called as DIRMA (Data-Inspired RiskMAnagement). Obtained data like strain, vibration and SCADA data are transferred to this platform on-line and analyzed tovisualize the true reliability of the wind turbine system in every few minutes. DIRMA provides a risk-based structural healthmanagement solution to the operator. The risk is defined as the product of the failure probability and resulting loss-cost. Itmeans users will be able to know the present and future risks of their wind turbines in price. A main part for calculating thefailure probability in DIRMA is based on the physical models and databases related to the damaging of materials or structuralelements such as welded joints. This approach enables a quantitative reliability evaluation from the beginning phase of theoperation of wind turbine system which would not exhibit any abnormal signals while almost all of the other SHM or CMS
solutions highly rely on the anomaly diagnostic approach. In parallel with the sophistication of the risk management as
mentioned above, we are advancing developments for easy access and operation. A demonstration of graphical user interface of
DIRMA working in a web application will be performed in the presentation.
Hitachi will provide a reliable wind turbine system with integrating technologies cultivated in the field of manufacturing ofother hardware and the information and communication technology.
3/5

31-Oct. 13:30 - 15:45 [ Operation＆maintenance (1) ]Condition Based Maintenance of Wind Turbines by 24 / 7 Monitoring of
Oil Quality, Oil Aging and Additive Consumption: Identification of criticaloperation conditions & determination of the next oil exchange
Mauntz Manfred and Peuser Jörn
cmc Instruments GmbH

Abstract:
A new oil sensor system is presented for the continuous, online measurement of the wear in industrial gears, turbines,generators, transformers and hydraulic systems. The detection of change is much earlier than existing technologies such as
particle counting, vibration measurement or recording temperature. Thus, targeted, corrective procedures and/or maintenancecan be carried out before actual damage occurs. Efficient machine utilization, accurately timed preventive maintenance, areduction of downtime and an increased service life and can all be achieved.
The oil sensor system measures the components of the complex impedances X of the oils, in particular the electricalconductivity, the relative dielectric constant and the oil temperature. All values are determined independently from each other.
Inorganic compounds occur at contact surfaces from the wear of parts, broken oil molecules, acids or oil soaps. These all leadto an increase in the electrical conductivity, which correlates directly with the wear. In oils containing additives, changes indielectric constant infer the chemical breakdown of additives. A reduction in the lubricating ability of the oils, the determinationof impurities, the continuous evaluation of the wear of bearings and gears and the oil aging all together follow the holistic
approach of real-time monitoring of changes in the oil-machine system. By long-term monitoring and continuous analysis of theoil quality, it is possible to identify the optimal time interval of the next oil exchange – condition based. This results inenormous cost reduction, when the oil is still stable and fully functional.
An application example from the wind energy sector will be presented.

Development and Evaluation of Vibration-based Anomaly Detection
System Using Actual Wind Turbine Data
Jun Ogata1, Masahiro Murakawa1 and Makoto Iida2
1National Institute of Advanced Industrial Science and Technology (AIST), 2The University of Tokyo

Abstract:
Wind energy is one of the most important renewable energy sources and has gained much attention due to the recent energycrisis. However, as wind turbines become increasingly large and more complex in order to provide reliable power generation,
their maintenance and repair becomes more difficult. The focus of this research carried out under Japan’s national “Smart
Maintenance for Wind Energy” project is the development of a reliable and cost-effective condition monitoring system (CMS)
to maintain the availability and improve the reliability of wind turbines. The main task of CMS is to detect mechanical failuresat an early stage such that maintenance can be carried out in a timely manner. Typical condition monitoring techniques for windturbine include vibration analysis, acoustic emission, oil analysis, strain measurement, ultrasonic analysis, and thermography.
One of the more popular tools in the condition monitoring of rotating machinery is vibration analysis.
In this paper, we present the development and evaluation of vibration-based anomaly detection system for wind turbine
condition monitoring. The system developed here is based on artificial intelligence approaches that achieve multi-dimensional
data analysis without deep knowledge about the mechanical behavior. The key modules that influence the performance of thedetection system are feature extraction and unsupervised learning. For feature extraction, the predominant characteristics of thewind turbine vibration signals are extracted by applying Fourier local autocorrelation (FLAC) features. At the anomaly
detection stage, we employ an unsupervised clustering approach based on Gaussian mixture models in consideration of thewind turbine’s dynamic operating conditions.
In the “Smart Maintenance for Wind Energy” project, CMS data including the vibration signals of the main components havebeen continuously measured and collected from 41 2MW-class wind turbines actually working in Japan. For evaluation ofCMS, we have been constructed the database of actual wind turbine failures. In this work, we evaluated the abovementioned
methodology using the database where the 20 failure cases were collected so far. We compared the accuracy of anomaly
detection by our system with several conventional methods. The experimental results showed the effectiveness of using theFLAC features, particularly in the case of the low-speed main bearing where the conventional method with traditional features
cannot detect the anomalies.

4/5

31-Oct. 13:30 - 15:45 [ Operation＆maintenance (1) ]Life Time Extension: How to maximize the return of investment
Santiago Lopez Camblor
UL International GmbH

Abstract:
Currently there are two methods applied within the industry to calculate the Remaining Useful Life (RUL): analytical and/orpractical.
The analytical component is to calculate the RUL with the aid of mathematical models. This mathematical analysis can bebased on a completely new calculations or can be based by updating an existing calculation done in the past. By applying thismethod the remaining useful life of a wind turbine is calculated after the actual damage has been established. New or additional
calculations for the wind turbine are made, taking into account site-specific boundary conditions, such as wind loads. The
structural integrity of a certain turbine is verified for extreme and fatigue loads. The output of this approach will be an expert
statement with a stipulation of RUL
The practical component takes the form of on-site inspection of the relevant Wind Turbines. This inspections arerecommended to be done by an accredited company according to ISO/IEC Standard 17020. The focus of these inspections is toverify the output from the analytical part and proofs on the basis of the actual conditions on the wind Turbines underconsideration.
Based on the results of the analytical and practical components, it is possible to make statements about any potential RUL
and/or about the inspection interval to be complied with.
However this analysis must not be carried without due regard to aspects of structural safety.

Failure rate and downtime analysis for wind turbine by using failure andaccident survey
Hirotaka Okumoto, Hideki Kato and Keiji Nijima
E&E Solutions Inc

Abstract:
Since 2004, failure and accident survey have been conducted by NEDO and carried out for owner of ind generating inJapan to improve the availability and share the information of failure and accident.
In NEDO failure and accident survey collects only failures which results in downtime of over 72 hours. owever, it is said that
half of the failures result in downtime of under 72 hours. Here, we recalculated the failure rates of on-shore wind turbines inJapan taking into account the failures of downtime under 72hours, which, gave a figure of 1.0 failure/WTG/yr, which is similar
to that of the figure in Europe. Simultaneously, we confirmed that the results of the NEDO survey can be used to calculate thereal failure rate. However, for the data before 2011, because of the timing at which the survey was conducted does not represent
the annual data, the failure rate of those years are not reliable, so care must be needed upon using the data of those years.

5/5