Presentació renovables

Sistemes de Supervisió i Control de Plantes d’Energies Renovables

Jordi Cusidó Roura

Arquitectura Turbina


Arquitectura Turbina- Control -


Arquitectura Turbina-Control-


Arquitectura Camp Solar

Arquitectura Comunicacions-Parc Eòlic-

Arquitectura Comunicacions-Camp Solar-

IEC-61400-25Model de dades eòliques

Àmbit d’aplicació de la Norma

Introducció

- LD: “Logical Device” Correspón a un dispositiu (Aerogenerador, Torre

meteorológica,…)

- LN: “Logical Node”Correspón a una parte del dispositivo (Torre, Góndola,

Convertidor,…)

Conceptes

61400-25-2 Model de informació



61400-25-2 Exemple variables node (LN)

Nodes Furhländer (LN)

WGDC Grid

WTRM Transmission

WNAC Nacelle

WGEN Generator

WTOW Tower

WROT Rotor

WCNV Converter

WMET Meteorological

Construcció nom variables

LN.[NomVar].[TipoVar]

LN: Nombre del nodo lógico.

[NomVar]: Nombre de variable. Puede contener uno o varios niveles, cada uno de ellos separados por puntos (.), dependiendo de la variable.

[TipoVar]: Tipo de la variable. Puede contener uno o varios niveles, cada uno de ellos separados por puntos (.), dependiendo del tipo de la variable.

Ex: WGDC.TrfGri.PhV.phsA.cVal.avgVal.f

WGDC: Nodo GridTrfGri.PhV.phsA: Grid Trifásico.Voltaje de fase. Fase AcVal.avgVal.f: Valor de variable. Valor medio. Float

Algunas variables Furhländer

WNAC.Wdir1.avgVal.fWNAC: Nodo NacelleWdir1: Wind direction 1avgVal.f: Valor medio. Float

WNAC.Wdir1.minVal.fWNAC: Nodo NacelleWdir1: Wind direction 1minVal.f: Valor mínimo. Float

WTUR.ExtPwrReactSp.maxVal.fWTUR: Nodo Wind TurbineExtPwrReactSp: External Power Reactive SpeedmaxVal.f: Valor máximo. Float

WTUR.ExtPwrReactSp.maxVal.fWTUR: Nodo Wind TurbineExtPwrReactSp: External Power Reactive SpeedmaxVal.f: Valor máximo. Float

Cloud, El futur dels Sistemes SCADA

SMARTSCADA

Les Futures Plantes d’Energia

Arquitectura del sistema

SmartCastData Server

(Web)

SmartCastLocal Server

(OPC)

Client

Local Network

Interficies d’Usuari- Inteligent Platforms-

Serveis Cloud, Cloud-Diganosis

SMARTCAST

Arquitectura del sistema: El Motor Predictiu

Technologia

Physical Variables:Vibration AnalysisMotor Current Signature AnalysisVoltage MeasurementsAcoustic Emission MeasurementsTemperature Monitoring

Signal Processing Techniques:Frequency AnalysisTime analysisTime-Frequency Analysis

Decision Support Systems:Neural NetworksFuzzySVMRandom Forest Algorithms

Current Condition Monitoring Techniques

Technologia

• Instrumentation

• Time based Signal Processing

• Frequency based Signal Processing

• Time-Frequency (Wavelets, Hilbert Huang)

• Fault Factor Feature Extractions (220)

• Advanced Neuro Fuzzy –ANFIS-

• Genetic Algorithms –GA-

• Selection & Extraction

• Collaborative Systems

Sistemes Experts de Classificació

SVM

f x

a

yest

denotes +1

denotes -1

f(x,w,b) = sign(w x + b)

How would you classify this data?

w x + b=0

w x + b<0

w x + b>0

Classificadors Lineals

f x

a

yest

denotes +1

denotes -1




f x

a

yest

denotes +1

denotes -1




f x

a

yest

denotes +1

denotes -1


Any of these would be fine..

..but which is best?


Datasets that are linearly separable with some noise work out great:

But what are we going to do if the dataset is just too hard?

How about… mapping data to a higher-dimensional space:

0 x

0 x

0 x

x2

Classificadors No-Lineals, SVM

General idea: the original input space can always be mapped to some higher-dimensional feature space where the training set is separable:

Φ: x → φ(x)

Classificadors No-Lineals, SVM

Processat de Senyal, Anàlisis de Variables

Relevant Frequencies Gear

• Broken Bearings. (R.R Schoen

and Others 1994)

where nb number of balls, fi,0 fault vibration frequencies, fr rotating frequency Hz, bd ball diameter, pd Race diameter, & β ball angle.

Relevant Frequencies Bearing

o,isbng mfff

cospd

bdf

nf rbo,i 1

2

Relevant Frequencies Generator

Fault frequencies analyzed on the Gearbox can be complemented by measurements on the generator.

Generator fault frequencies shall be analyzed using vibration or current (Motor Current Signature Analysis) measurements.

Fault condition on the gearbox usually appears as an eccentricity fault on the generator, this fault is usually one of the most relevant indicators to address fault condition analysis.

Further results are related to MCSA condition monitoring results.

Relevant Frequencies Generator • Eccentricity fault (Thomson 1988)

where m=1,2,3,… harmonic number, p is the pair of poles, s the slip, y fs electric frequency.

p

smff secc

11

Relevant Frequencies Generator • Broken rotor bars, just for induction (Kliman 1988, Benbouzid 1995)

where l/p= 1,5,7,11,13,… are harmonic motor characteristics

s

p

slff sbrb

1

0 25 50 75 100 125 150 175 200 225 250 0

0.05

0.1

0.15

0.2 2 . 8 A

Mag

nitu

de (

A)

Frequency (Hz)


• Shortcircuits (Thomson 1988, 1995)

– Low frequencies

k=0,1,3,5,...

ks

p

mff sstl 1

0 50 100 150 200 250 300 350 0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Frequency (Hz)

Mag

nitu

de (

A)

Ia Ib Ic

2.76 A 2.38 A 2.62 A


400 500 600 700 800 900 1000 1100 12000

0.002

0.004

0.006

0.008

0.01

Frequency (Hz)

Mag

nitu

de (

A)

IaIbIc

p

smZff ssth

11 2

• Short Circuits (Rosero - Cusidó 2006)– Medium frequencies

where Z2 is the number of rotor slots & k=0,1,3,5,...

Time- frequency Transformation

Applied to transient analysis improving the resolution and accuracy for the fault detection

• Short Time Fourier Transform: is the time dependant fourier transform

• It applies a temporal window in wich the FT is performed

dtttfbfG bwdwwdw ,:,

tjwdw ebttfbfG

wdw

,,

wdw

2

wdw2

wdw

2

2b

0b

2

wdw2

wdw

2

wdw2

1b t

0

1


The output of the STFT is an Spectrogramm


• Wavelet Transform: Wavelet transform decomposes the signal as a sum of different wavelet signals shifted and scaled. Those signals are know as “mother” wavelet.

• The Decomosition algorithm decomponds the signal in a diadic way

• The output of the transformation is the time evolution of each decomposition or detail.

ndnanxJ

jjkj

kkj

kkjkj

1

,,,,

0

00

g[n]

h[n]

2

x[n]2

g[n]

h[n]

2

2

g[n]

h[n]

2

2

Level 1 detail coefficientsScale 2J-1




g[n]

h[n]

222

x[n]222

g[n]

h[n]

222

222

g[n]

h[n]

222

222





Approx.Level 3

DetailLevel 3

DetailLevel 2

DetailLevel 1

fs/2fs/4 ffs/8fs/160

Approx.Level 3

DetailLevel 3

DetailLevel 2

DetailLevel 1

fs/2fs/4 ffs/8fs/160

Signal Processing Techniques

Collaborative Algorithms

Sistemes Integrats de Gestió

SMARTOPEX

SmartOpexGMAO and Operations Platform

SmartOpex is the low cost implementation for monitoring operations at a wind farm. It is completely customizable to the clients, which has an ERP or not, providing solutions to the needs of management.

It consists in two parts, one with internet access via a computer follows all the activities in the site and allows the assignment of these tasks to the maintenance teams. Using this application and monitoring the operations, Smart Opex gives typical indicators such as MTTR, MTBF, failure rates, delays in preventive, downtime and lost track of the hours of work. Smart Opex also gives the working hours in the site.

[email protected]

www.smartive.eu

+34 620 602 495

Presentació renovables

Engineering