On-Condition maintenance of wind generators with low cost ...

On-Condition maintenance of wind generators with low cost systems

INÁCIO FONSECA1; J. TORRES FARINHA

2; F. MACIEL BARBOSA

3

Instituto Superior de Engenharia1,2

; Faculdade de Engenharia3

Instituto Politécnico de Coimbra1,2

; Universidade do Porto3

Rua Pedro Nunes - Quinta da Nora, 3030-199 Coimbra1,2

; Rua Dr. Roberto Frias, 4200-465 Porto3

PORTUGAL1,2,3

[email protected]; [email protected]

2; [email protected]

3

Abstract: - This paper describes an integrated wind farm maintenance system, including software, hardware and

some algorithms. The approach will imply the optimization of the maintenance planning among different wind

towers owned by a company, considering the management of situations where the distances among them have a

considerable weight in the maintenance costs. This reason, associated to the generators accessibility and the on-

line maintenance variables acquisition, are very important to justify the necessity of improvement of the

maintenance planning. This objective is achieved through the application of integrated maintenance

management systems to which are associated new functionalities, in parallel with the usual ones in these

systems that fit within the on-condition predictive maintenance, including on-line data acquisition modules.

Usually, the manufactures construct, deploy and give the means to the suppliers to perform the wind system’s

maintenance. In some cases, the owners of wind farms can choose the maintenance company. This is a very

competitive area, where companies hide the development details and implementations. Another important

factor is that, by the first time in last ten years, the three countries with the largest installed power, together,

have fallen to a minimum historic, below 40% of the market for wind power in the EU. Definitively, the other

European countries encouraged the appearing of programs for installing wind power, with prominence for Italy,

Portugal and Netherlands. Within this scenario, the development of maintenance management models for

multiple wind equipments is important, and will allow countries to be more competitive in a growth market.

Key-Words: - Wind energy; renewable energy; predictive maintenance; time series prediction.

1 Introduction The financial and energy crisis will play an

important role in future decisions on energy

production systems and, particularly, in wind

generation. During last year, 2008, the oil prices in

European Union (EU) had a drastic impact on policy

decisions in several EU member states. For

example, in Portugal, since last year, persons had

deductions in taxes to act as significant incentives to

implement renewable energy production systems,

not only in micro-generation systems (household),

but also to implement wind farms. But, the

dynamism of this area had continued to increase

during the last year. The European Wind Energy

Association (EWEA) [1] responsible for

presentation of EU results reported a 14% increase

in installed wind power in EU-15 and 15% in EU-

27. It is noteworthy that the EU-15 is responsible for

98% of installed power. Germany, Spain and

Denmark, the leader countries in the installed

power, had risen below 40% of the market of wind

power with 3.351.120 MW produced, against

5.133.185 MW from the rest of EU. Obviously, this

is due to differences of regional wind, that changes

year after year and due to the differences of the age

of technology and equipment. In this scenario,

maintenance problems will grow in future with the

oldness of equipments and, in a reasonable way, it is

important to study this problem to increase

knowledge. In this work authors developed

maintenance software – SMIT – [2], [3], [4], [5],

[6], [7], integrated with commercial or low cost

acquisition systems.

2 A Wind Maintenance System Maintenance systems of wind generators are a great

asset to ensure a good performance during the

production of electricity. Given the objectives of

this work, in this paper a system capable of

performing the data collection with high

performance at a low cost is presented (Fig.1).

To make the data acquisition, the choice was on

a microprocessor with a communication interface -

in this case the CAN network. The benefit of CAN

is to enable the interconnection of more then two

equipments on the same bus and to be suitable for

Proceedings of the 3rd WSEAS Int. Conf. on ENERGY PLANNING, ENERGY SAVING, ENVIRONMENTAL EDUCATION

ISSN: 1790-5095 128 ISBN: 978-960-474-093-2

real-time operation. The major disadvantage is the

fact that the bandwidth is shared.

Fig. 1 - A low cost wind maintenance system

However, in this work, the monitored signals do

not need a high bandwidth, because they are

acquired at regular intervals with large amplitude

like one second, five seconds or more. In a high

demanding situation the system can be duplicated to

ensure the bandwidth needs. In the present

implementation the CAN bus can run from 250

Kbits/s to a rate of 1Mbit/s.

Fig. 2 – Setup for the Luminary and PIC

microntrollers acting as Ethernet - CAN gateways.

Validation is based on MAC address

For low cost instrumentation, the prototype

hardware developed by SMIT team includes:

• A board based on the ENC28J60 [8]. The

ENC28J60 implements the physical interface to

Ethernet allowing any microcontroller to use

TCP/IP communications. The connection from

MCU to ENC28J60 is performed by SPI. The

Ethernet speed is 10 MHz half-duplex.

• An acquisition board based on the PIC 18F2685

[8]. This MCU allows the acquisition of

10bits/channel, at a speed of 100 kHz. It also

includes a CAN 2.B controller with maximum

speed of 1 Mbps. MCU frequency clock is 40

MHz. The programming is done in C using C18

from Microchip.

• An acquisition board for high speed was also

developed, being powered by the

dsPIC30F4012 [8]. This MCU includes SPI, and

CAN 2.B, with 10bits acquisition at a speed of

1Msps, and the possibility to perform

synchronous acquisition of four channels. The

CAN operates at a maximum speed of 1 Mbps.

MCU frequency clock is 40 MHz. Programming

is done in MPLAB C30.

• A board using Microchip digital potentiometers

[8] implements a Butterworth low pass filter of

4th order with cut-off frequency set between

100Hz and 50 kHz. The board also incorporates

two cascade amplifiers with gain ranges from

0.1 to 10. The frequency and gains are

programmed by software using the SPI interface

from digital potentiometers MCP42100,

MCP608 and operational amplifier LM358.

• A board based on the microcontroller Luminary

LM3S8962 [9], which implements an ARM

Cortex-M3 with support for Ethernet packet

time stamping. This processor includes two

interfaces: Ethernet at 10/100 MHz full/half-

duplex, and CAN 2.B with maximum speed of

1Mbps (it will operate as a gateway Ethernet-

CAN). LM3S8962 frequency clock is 50 MHz.

Programming is done in C using open source

GNU GCC ToolChain for ARM Cortex-M3,

with gcc-4.3.3, Binutils 2.19.1, and newlib-

1.17.0 under Linux or Cygwin.

One of the biggest challenges of this architecture

is about the problematic of synchronizing the data

acquisition time between different equipments and,

in last case, among different wind generators. Thus,

it is important that the same type of sensorial

information be acquired simultaneously, with a very

low deviation time.


ISSN: 1790-5095 129 ISBN: 978-960-474-093-2

Fig. 3 - Measuring the delay in CAN bus

transmission media.

Fig. 4 – Ethernet Normal operation - CAN

gateways. Using this technique a deviation of 10

microseconds is achieved in different boards.

Fig. 5 – Blue: Theoretical travel time (ns) for

transmitting extended Can Messages with 1 byte to

8 bytes of length. Red: Delay time measured in the

real system between Ethernet - CAN gateway and

one CAN Slave. This time is exactly half of the sum

of the time presented in Fig. 3.

To solve this problem a local clock is

maintained, and synchronized with SMIT Linux

Server master clock through SNTP [31] in the case

of PICs and through PTP [32] in the case of the

ARM. The use of different protocols is justified by

the Ethernet packet time stamping facility of the

Luminary micro-controller that is full explored by

the PTP.

For the acquisition through synchronization in

CAN bus, the master board (PIC18F+ENC28J60 or

LM3S8962) with Ethernet and CAN connectivity

sends a special CAN message demanding a data

acquisition cycle (the message ID pronounces what

slave(s) should acquire data, Fig 4). In the setup

stage of this firmware the gateway receives from the

SMIT server the configuration: acquisition

timings/periods and CAN setup parameters (Fig. 2).

The CAN slaves, while in setup mode, will try to

communicate in 125 kHz, 250 kHz, 500kHz and

1MHz and will stay in this setup mode until a valid

CAN message is received. After this stage they will

start the normal cycle, waiting for a message asking

for an acquisition. While in this waiting state for

acquisition, they can forward packets for measuring

CAN propagation delay time, or receive messages

for firmware upload (just the slaves based on PIC

micro-controllers). The method to measure the delay

propagation in CAN bus is similar to the one used in

precision time protocol (see Fig. 3). A packet is sent

by the LM3S8962/PIC gateway, and retransmitted

only by one slave. Transmission propagation delay

is measured by dividing the time used by two.

Special care is taken by measuring this time while

using CAN message with data from one to eight

bytes and the results are saved in a table (different

data size CAN messages spend different time to

travel in the CAN bus). The CAN acquisition

message sent by the gateway to ask slaves to acquire

should be sent at (see Fig. 3):

2

321 TTTtimet requested

++−= (1)

For more details on SMIT architecture see [10].

3 Wind systems sensorial data Maintenance of wind turbines uses many techniques

similar to other maintenance objects. In this field

many authors [11], [12], [13], [14] are working

using acoustic techniques, vibration techniques,

infrared images, stress measurement, zero crossing

current analysis, artificial intelligence, only to name

a few. Under this work the techniques used for

monitoring the wind systems condition are based in

the following aspects:

• Measuring the wind speed, using an

analogue anemometer and a ultrasonic


ISSN: 1790-5095 130 ISBN: 978-960-474-093-2

anemometer WMT50 from Vaisala

Company;

• Active power measurement;

• Classification using artificial intelligence

(Fig. 6);

• Vibration monitoring on generator and

gearbox;

• Weather forecast using information from

weather sites, tracking the wind velocity

(using time series analysis);

• Time series analysis using regression

techniques;

• Weather monitoring station (future

developments).

Fig. 6 – Up: Wind velocity versus active power

produced, for a wind system in a wind farm.

Down: classification using a SVM classification.

For numerical data processing, the SMIT

incorporates the Octave distribution version 3.0.3

and version 2.8.0 of R [15]. The R and Octave

scripts are stored in the SMIT database, enabling the

users to modify/change them as needed. The R

scripts can be executed from a PHP script or from a

database command that starts the PHP script and

indicates the Octave or R script that shall be

executed.

3.1 SVM classification From wind and power measurement it is possible to

predict the power curve. The main idea is to relate

the power curve with normal or faulty condition in a

first step. The first algorithm uses SVM - Support

Vector Machine. The SVM after being trained will

be able to classify a new sample vectorn

x ℜ∈r

. This

method, developed initially for binary decisions

(true/false), can be adapted for n classifications.

Under training phase, a set D of vectors is

considered:

( ) ( ) nn yxyxD ,..., 11

rr= , with 1,1, −∈ℜ∈

→

i

n

i yx

Depending on the choice of the kernel function,

the SVM will be able to produce good or bad

classifications. The function that usually gives better

results is the RBF; more details can be seen in [16],

[17], [18] and [19].

Fig. 7 – Results of a SVM classifier for another

plant, using a RBF kernel function with a parameter

of 0.06. Red color represents uncharacteristic

operation.

The proposed vector xr

is defined in 6ℜ by:

−

=

conditionBreaking

poweractive

rotorhighvelocity

rotorlowvelocity

directionnacelledirectionWind

VelocityWind

xturbine

_

_

__

__

__

_

r

Figure 7 shows some results of a SVM classifier.

The difference between this graphic and of Fig. 6,


ISSN: 1790-5095 131 ISBN: 978-960-474-093-2

resides on some points bad classified in abnormal

operation on Fig. 6. In this case, good operation

points like situation where the system is turned off

should be considered as a good operation situation

like showed in this graphic.

3.2 Wind measurements Wind measures are acquired using an anemometer

with sampling frequency of 1Hz, either using

WMT50 ultrasonic sensor from Vaisala or using an

analogue cups anemometer.

Fig.8 – Left: WMT50 from Vaisala. Right: Analogue

cups anemometer

3.3 Gearbox vibration monitoring The second algorithm uses an accelerometer to

monitor vibrations on the gearbox and in the

generator where the line currents are also monitored.

To identify faults, two assays were performed. The

first, an induction motor was used as motor and the

second one as generator. In the first test, four

induction motors were used, one healthy and two

motors with some kind of damage provoked, like

broken bars. The motors were tested with full load,

half load and without any load. The same test was

performed using the motor as generator but

introducing loads.

Many other alerts can be programmed, based on

time series algorithms, if new sensors are added in

the field; otherwise, the user can program it using

Octave language; for more details see [10].

3.4 Time series analysis of sensorial data The time series analysis accompanies and tries to

understand and to predict the evolution of data

through time. The important issues to measure are

the following: trend; seasonality; cyclicality; and

error or random components. In the science field,

time series analysis are welcome on the economics

to maximize profit, but also to forecast important

aspect of humans, like environmental catastrophes,

minimizing human dramas, predicting industrial

production to adjust the production to the estimated

levels of search, and many others.

Many techniques have been presented

throughout history catalogued in three great

methodologies: use of statistical methods; use of

adequate models to the process under question; and

use of artificial intelligence methodologies [20],

[21]. Although, the innumerable methods, more or

less elaborated, all of them present forecasting

errors, being one of the imperatives to minimize a

defined metric adjacent to the measure of these

errors. More details can be seen on [22].

If time series [23], [24], [25], [26], [27], [28],

[29], [30] forecasting can be accomplished by

normal function, the series is called deterministic;

otherwise, if forecasting is only possible by

statistical methods, the time series is called

stochastic. The respective stochastic process can be

represented by ( ) Ω∈∈= θθ ,;, TttyY , where T

represents the time space and Ω represents the space

of a probabilistic event. A stochastic temporal

process is stationary if it is invariant to a time shift,

i.e., ( ) ( ) Ttttyty ∈∆∀∆+= ,,, θθ .

For each observation, a stochastic time series

will apply, represented by ,...3,2,1],[ =kky , where k

represents a variable evenly spaced in time in which

ktt s ⋅∆= . Then, the problem is how to forecast the

values for [ ] 0,ˆ >+ mmky (we will use notation y

for prediction and, in some cases, for better

readability, ][kyyk = ).

[ ] [ ]( ) 1,...,0,1ˆ −=−=+ kiikyfky (2)

The quality of forecasting will be measured by

error indicators: MSE – Mean Squared Error, TIC -

Theil Inequality Coefficient, ME – Mean Average,

STD – Standard Deviation, MAE – Mean Absolute

Error.

The tested algorithms are the following: Moving

average (MAS); Exponential Smoothing (ES);

Adaptive Response Rate Single Exponential

Smoothing (ARRSES); Exponential Smoothing

Algorithm – Holt-Winters forecast (results will be

labeled with HWSas with seasonality and HW

without it); AR, MA, ARMA and ARIMA models,

Box-Jenkins (labeled with ARMA) and Support

Vector Regression (SVR).

Exponential smoothing is also used on this work.

Initialization is achieved by [ ] [ ]11ˆ yy = , and normal

operation by:

[ ] [ ] ( ) [ ]

( )

−−⋅⋅+=+

−⋅−+⋅=

]1[ˆ][][ˆ][ˆ

1ˆ1ˆ

kykymkymky

kykyky

α

αα,

where [ ]1,0∈α . (3)


ISSN: 1790-5095 132 ISBN: 978-960-474-093-2

The value α can be fixed or estimated, namely

through minimizing the MSE, conducting to a non-

linear optimization problem, usually solved by the

Levenberg-Marquardt algorithm.

It is possible to continuously update α value

(results will be labeled with ESMSE - this is the

method used preferentially in this work) - and

introducing an estimation of α with an implicit

error, considering that the old forecasting values are

independent of α; equation (4) represents the

update. However, it can be used the mean of

equation (4) with the same equation (4) considering

now [ ] [ ]11ˆ −=− iyiy (MSE minimization).

[ ]( ) [ ]( )

[ ]( )

2,

1ˆ][

1ˆ][1ˆ]1[

2

≥

−−

−−⋅−−+

=

∑

∑

=

= n

iyiy

iyiyiyiy

k

ni

k

niα (4)

Fig. 9 – The results of the methods, here is clearly

that smoothing moving average prediction (MAS)

will not work.

“… to forecast the temporal instant where we

will have one probable damage. With this

information it will be launched an working order in

SMIT maintenance software, to prevent the causes

that in the future would give damages, to allow the

correction in the present time…” [22].

The answer of this question stipulates, at first

place, the continuity of observing main variables

and provides instant information to the "health state"

of equipment. After a maintenance intervention, the

time series will be initialized again. This last

procedure will overcome the problem of measuring

accurately the seasonal period and, in practice, will

remove it. But, another feature, the algorithm has

the necessity, for each signal, to indicate the

intervals of tolerance, acknowledgment interval and

the critical interval. When the time series provides

an output of these bauds, the SMIT software

launches a warning to the operator.

Figure 9 shows the first typical time series to

track divided, for better understanding, in 5 different

types of situations that could occur:

1. From 0-200 we have a situation of a well

defined trend;

2. From 200-400 a normal operation;

3. From 400-600 a well defined trend with

different slopes;

4. From 600-800 a high slope trend and,

finally;

5. From 800-1000 a quadratic trend.

Table I, shows the results for the tested methods,

and the proposed method ESMSE achieved the best

performance in the majority of proposed indicators

except in the ME indicator. It should be noticed that

the forecast is for one step ahead. For ten steps

ahead, the best algorithm is HW - Holt Winters, and

ESMSE is the fourth best.

Table I - Prediction of 1 step ahead

4 Conclusion In this paper an integrated computer system based

on different computer systems has been presented.

This system is based on especial firmware to acquire

relevant information. Integration with octave and R

algorithms and SMIT database system is also

relevant for this purpose. SVM classification for

state monitoring is very significant and achieves

good performance. Time series tracking are used for

forecasting problematic situations, having been

compared nine methods to choose the most relevant;

however, experience indicates that for different

situations the method that generates the best

estimators can change. To overcome this situation

the final algorithm decides online the method to be

used based on the MSE indicator. The nine methods

are running in simultaneous for the same variable,

MS

E

TIC

ME

ST

D

MA

E

ARRSE 28.362 0.0627 0.3995 5.3132 2.7672

ES 26.057 0.0601 0.3145 5.0974 2.6602

HW 26.225 0.0600 0.1665 5.1209 2.6537

HWSAS 29.464 0.0636 0.1880 5.4275 3.0439

ESMSE 24.039 0.0575 0.2464 4.8992 2.6514

MAS-2 1029 0.5976 21.096 24.190 21.857

ARMA(2,2) 42.470 0.0762 -0.1165 6.5191 2.7513

SVR-RBF 84.286 0.1099 1.1043 9.1187 4.2527

SVR-LIN 57.268 0.0880 -0.1571 7.5697 2.9946


ISSN: 1790-5095 133 ISBN: 978-960-474-093-2

and at each sample the performance of each method

is evaluated. Time synchronization is a very good

solution for monitoring variables geographical

separated, allowing to measure signals at the same

time, what is demonstrated in this paper.

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http://ptpd.sourceforge.net/


ISSN: 1790-5095 135 ISBN: 978-960-474-093-2

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