Sensor Fault Diagnosis for Wind Turbine Generators Using Kalman

Filter

Tej Enosh. MM.Tech Power Electronics and DrivesVIT University.

Guided by:

Dr. R . Saravana KumarProfessorSchool of Electrical Engineering(SELECT)VIT University.

OutlineIntroductionDoubly Fed Induction Generator operation(DFIG)Modeling of DFIGKalman Filter & Filter BankGeneralized observer Scheme & Dedicated Observer SchemeDFIG State Estimation with Kalman FilterFault detection using Dedicated Observer Scheme In DFIGModeling of PMSGFault Detection in PMSG

Objective

• To create a model of DFIG• To Identify the Current Sensor Fault in DFIG with Dedicated

observer scheme using kalman Filter• To create a state space model for PMSG• To Identify the Current Sensor Fault in PMSG with Dedicated

observer scheme using kalman Filter• To Identify the Current Sensor Fault in PMSG with Augmented

kalman Filter

Introduction• Wind energy - the fastest-growing source of energy in the

world

• The doubly feed induction generator (DFIG) is one of the most used drive in wind energy because of its

low cost, simplicity of maintenance, reliability

• When a fault occurs, it must be detected as soon as possible• The data validation is important in this processes

• The control system operates with the information of the system provided by sensors --- can be go through faults.

Cont..

•When a fault occurs, it must be detected as soon as possible, even where all observed signals remain in their allowable limits.

•The fault must then be located and its cause identified

•This aspect becomes more and more investigated because of the construction of high capacity offshore wind parks.

Problem Identified• The control system operates with the information of the

system provided by sensors, which can be subjected to faults• For The isolation of the fault the two following fault scenarios

will be used i) multiple but non simultaneous faults scenario ii) simultaneous faults scenario.• The state observer for fault detection and isolation• Filter bank used to estimate the dynamical behaviors of the

system in order to detect then to isolate the fault.• Previous Method For Study of Current sensor Fault and

Voltage sensor Fault is Luenberger observers method• It has proposed an observer scheme base on Kalman filter to

diagnosticate the current sensor fault of a DFIG because of its discrete property

Methodology

Operating principle of a wind turbine using doubly fed induction generator

Modeling of the doubly fed induction generator The Kalman filter bank based on Generalized Observer

Scheme The Kalman filter bank based on Dedicated Observer Scheme Validation of simulated DFIG & PMSM for current sensor FDI

Kalman Filter

• The Kalman filter uses the dynamical model, the known inputs to that system as well as the measurement (which given by sensors) to estimate the state of the system.

• Widely use in automatic filtering as a mathematical technique to extract a signal from noisy measurements.

xk+1 = Axk + Buk + wk

zk = Hxk + vk

A,B,H are matrices of approximated dimension

• p(w) N(0,Q) Q --∼ process covariance noise • p(v) N(0,R) R-- ∼ measurement covariance noise

w- Process noise and v- measurement noises

• The implementation of Kalman filter could be divided in two steps.

• Prediction step and Correction step.• Prediction Step:

• Correction Step:

• The diagnostic scheme with Kalman filter is capable to detect the fault but it is unable to locate the fault.

• To resolve this problem, a filter bank will be used.

Filter bank for the FDI problem

• To Design state observer for fault detection and isolation is a well known problem.

• Filter bank used to estimate the dynamical behaviors of the system in order to detect then to isolate the fault.

• The first kind of filter bank is Dedicated Observer Scheme (DOS).

• The second one, Generalized Observer Scheme (GOS).• Each filter bank is composed by a number of observers, which

are supplied with all of the input and different subsets of output of the system.

• A Decision unit diagnosticate whether or not faults are presented in the sensors and which one is faulty by comparing the estimated outputs with the measured ones

Generalized Observer Scheme and

Dedicated Observer Scheme

GOSDOS

•Generalized Observer Scheme – Can Detect Single Sensor Fault

•Dedicated Observer Scheme (DOS) – Can Detect a Simultaneous Faults

The structure of a GOSfor a MIMO system

In this scheme each observer is driven by a different single output.

Generalized Observer Scheme

• Structure of GOS for MIMO System

• Supervised System with 4 outputs

• ith observer is driven by the input u and all of the output except yi

• By this way residual vector rG,i depends on all but the ith fault

Dedicated Observer Scheme• In this observer scheme

each observer is driven by a different single output.

• Hence ith observer is only sensitive to the failure of yi

• Then the residual rD,i represents the failure of the ith sensor.

Advantage: It allows to detect and isolate simultaneous faults.

DFIG State Estimation with Kalman Filter

Schema of wind turbine using DFIG

Modeling Of Double Fed Induction Generator

• In this work, we consider that the DFIG operates at a fixed-speed

• Crotor convertor should be considered as control signals.• The generated power is determined by the currents in the

windings of stator and rotor; these currents are to be measured.

• The stator voltages are the voltages of the grid as known external inputs.

• The model of DFIG was transformed in dq reference frame.• The d-axis is chosen to coincide with stator phase r-axis at t = 0 and • The q-axis leads the d-axis by 90 degree in the direction of

rotation.

State-Space Representation of the DFIG

Cont..Discrete State Space representation of DFIG

C & E are the unity 4x4 matrix

•For The isolation of the fault the two following fault scenarios will be used•i) multiple but non simultaneous faults scenario •ii) simultaneous faults scenario.

Fault detection using Kalman filter•Residual rK obtained from the Kalman filter with no sensor’s failure.•The sensor’s faults are detected.• Fault detection and isolation using Generalized Observer Scheme• Fault detection and isolation using Dedicated Observer Scheme Model in the Loop validation

FDI of the Current Sensor Faults

Residual Without Sensor Fault

Residual With Sensor Fault

Event Number

Fault Number

Starting Time

1 F1 50 Sec

2 F2 150 Sec

3 F3 250 Sec

4 F4 350 Sec

Fault Detection

Event Number

Fault Number

Starting Time

1 F1 50 Sec

2 F2 150 Sec

3 F3 250 Sec

4 F4 350 Sec

Fault Detection

Event Number

Fault Number

Starting Time

1 F1 50 Sec

2 F2 150 Sec

3 F3 250 Sec

4 F4 350 Sec

Fault Detection

Event Number

Fault Number

Starting Time

1 F1 50 Sec

2 F2 150 Sec

3 F3 250 Sec

4 F4 350 Sec

Fault Detection

PMSG State Estimation with Kalman Filter

• Variable Speed operation of Modern wind turbine enables • Optimization of the performance• Reduces the mechanical loading• Delivers various options for active power plant control

• Mathematical Modeling of PMSG• Kalman Filter for State estimation in PMSG• Fault detection Using Kalman Filter• Augmented State Kalman Filter for PMSG

Overview

PMSG SystemPMSG System

Weights & initial stateinformation

State Estimation& residual generation

Kalman Estimator bank

EstimatorN

Estimator2

Estimator1

Estimator0

mi Ԑ Z

Estimation, Fault Diagnosis Architecture

Computing Of M( kalman gain)

Residual Generation

DecisionMaking

Fault Detection

Input OutputSensors

Residual

Fault detection

Fault Evaluation method

Mathematical model of PMSG

State Space model of PMSG

} – System Model

0 20 40 60 80 100 120 140 160 180 200-3

-2

-1

0

1

2

3Residual

resid

ual,r

Id0 20 40 60 80 100 120 140 160 180 200

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5Residual

resid

ual,r

Iq

Residual without current sensor fault

0 50 100 150 200-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03PMSM idq1

resi

dual

,r

0 50 100 150 200-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8PMSM idq2

resi

dual

,r

Residual with current sensor fault

rD,1 rD,2

Augmented state kalman filter

Discretized equation set

Augmented state vector of PMSG

A=B=

Augmented PMSM model

Augmented State Vector

0 20 40 60 80 100 120 140 160 180 200-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Augmented Model

resi

dual

-r

Id0 20 40 60 80 100 120 140 160 180 200

-3

-2

-1

0

1

2

3Augmented Model

resi

dual

-r

Iq

Residual of Augmented state PMSG Without fault

Conclusion• In this project, problem of current sensor Fault Detection in DFIG

and PMSM of wind turbine was treated.

• Detection and the isolation of multiple sensor faults was addressed using the Kalman filter bank in a Dedicated observer scheme(DOS).

• All the multiple and simultaneous faults is detected and located with the observer scheme.

• There is no miss detection.

• The employed DOS based FDI processes has shown its capacity to detect and to isolate simultaneous faults

References• H.Chafouk, G.Hoblos, N.Langlois, S.L. Gonidec, and J.Ragot, “Soft computing

algorithm to data validation aerospace systems using parity space approach”, Journal of Aerospace Engineering, vol 20, no .3, pp. 165-171, July 2007.

• R.Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, 2005

• Bolognani, S.; Oboe, R.; Zigliotto, M., "Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position," Industrial Electronics, IEEE Transactions on , vol.46, no.1, pp.184,191, Feb 1999

• D.H.Trinch and H.Chafouk, “Current sensor fdi by generalized observer scheme for a generator in wind turbine”, in International Conference on Communications, Computing and Control Applications(CCCA11), Hammamet, Tunisia, March 2011

• O.Anaya-Lara, N.Jenkins, J.Ekanayake P.Cartwright, and M.Hughes, Wind Energy Generation-Modelling and Control. John wiley sons, Ltd, 2009

Thank You