Signal Processing for Disturbance Identification in Power ...
Transcript of Signal Processing for Disturbance Identification in Power ...
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Signal Processing for Disturbance Identification in Power Systems
Dr Ghanim Putrus BSc, MSc, PhD, CEng, MIET Reader in Electrical Power EngineeringDirector of Training Programmes for Industry
School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon Tyne NE1 8ST, UK E-mail: [email protected]
Acknowledgment: Dr Janaka WijayakulasooriyaDr Peter MinnsDr Chong NgMr Edward BentleyReyrolleReyrolle Protection (Siemens)Protection (Siemens)NaRECNaREC
EM Day, National Physical Laboratory, Middlesex, 29th November, 2007
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Presentation Outline• Power Quality (PQ)
– Definition and introduction
• Disturbances Classification• PQ Monitoring Techniques• Signal Processing for Disturbance Identification• Intelligent PQ Monitoring System (IPQMS)• Summary
Signal Processing for Disturbance Identification in Power Systems
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What is a Power Quality Disturbance?• Deviation (steady-state or transient) of voltage or
current waveforms from a pure sinusoidal form of a specified magnitude.
So what?– Signal distortion is normally associated with
relatively high frequency components, which flow in the system, at relatively great distance from their point of origin.
– This “non-ideal condition” create problems for the power system, depending on the components that cause the distortion, their magnitude, frequency and duration.
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Power Quality Disturbances
• Why now?– Modern electrical equipment are sensitive to PQ
disturbances e.g. microprocessor-based controllers, power electronic devices such as SMPS, variable speed drives, etc.
– Modern equipment (same equipment!) largely employ switching devices and hence have become the major source of degradation of PQ.
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Power Quality Events• Steady-State Events
– These are long term abnormalities in the voltage/current waveform.
– Information are best presented as a trend of disturbance level over a period of time (relatively long), and then analysed.
Voltage Flicker(Voltage Modulation)
0.05 0.06 0.07 0.08 0.09 0.1-3
-2
-1
0
1
2
3
Time, s
Current, A
Harmonics: Periodic waveforms having integer multiple of the fundamental frequencyInterharmonics: Periodic waveforms which are not integer multiple of the fundamental frequency.
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Power Quality Events• Transition Events
– These are sudden abnormalities of relatively short duration, occurring in the voltage/current waveform.
– They are normally detected when the instantaneous value of the voltage/current exceeds a certain threshold.
– These events occur between two steady-state events or superimposed on a steady-state event.
Oscillatory transient
Change its polarity rapidlyFrequency 20Hz to 200 kHz
0 0 . 0 5 0 . 1 0 . 1 5 0 . 2- 1
- 0 . 5
0
0 . 5
1
1 . 5
2
Impulsive Transient
Unidirectional polarity < 50ns to >1ms
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Power Quality
Disturbances
Disturbance Type TypicalDuration
Typical VoltageMagnitude
Nanosecond < 50 nsMicro second 50 ns~1 msImpulsiveMillisecond > 1 msLow freq. 0.3~50 ms 0 ~ 4 puTransientsMedium freq. 20 μs 0 ~ 8 puOscillatoryHigh freq. 5 μs 0 ~ 4 pu
Instantaneous 0.5~30 cycle 0.1~ 0.9 pu
Momentary 30 cycl.~3 s 0.1~ 0.9 puSagTemporary 3s ~1 min 0.1~ 0.9 pu
Instantaneous 0.5~30 cycl. 1.1~1.8 pu
Momentary 30 cycl.~3 s 1.1~1.4 puSwell Temporary 3 s ~ 1 min. 1.1~ 1.2 pu
ShortDurationVariation
Momentary 0.5 cycl.~3 s < 0.1 puInterruption Temporary 3 s ~ 1 min. < 0.1 pu
Sustained Interruption > 1 min. 0.0 pu
Under Voltages > 1 min. 0.8~ 0.9 puLongDurationVariation Over Voltages > 1 min 1.1~ 1.2 pu
Magnitude Imbalance Steady stateVoltageImbalance Phase Imbalance Steady state
DC Offset Steady state 0 ~ 0.1%
Harmonics Steady state 0 ~ 20%
Interharmonics Steady state 0 ~ 2%WaveformDistortions
Notching Steady state
Noise Steady state 0 ~ 1%
Voltage Flicker Intermittent 0.1 ~ 7%
Power Frequency Variations < 10 s .95~1.05 pu
Based on IEEE Standards 1159
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PQ Monitoring Equipment
Handheld Portable
Fixed PQ monitor/analyser Networked multipoint PQ monitors
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PQ Monitoring Techniques• Time Domain
- Using filters or DSP techniques- Straightforward design, but inflexible, complex and response can be slow.-Does not provide much insight into the signal (e.g. frequency information of the signal is not directly observable)
• Time and/or Frequency Domain- Frequency analysis (e.g. FFT)- Time/frequency analysis (e.g. Wavelet transform) - Good extraction capability for PQ analysis, flexible, but require large DSP computational power. FFT response is limited to one cycle of the signal.
• Artificial Intelligence- Using Artificial Neural Network (ANN)- Good extraction capability, flexible, fast response, can adapt to changes in the system. Need appropriate training.
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Capture and Extract disturbance waveformCapture and Extract disturbance waveform
Categorize disturbance (steadyCategorize disturbance (steady--state or transient)state or transient)
Extract disturbance features and Identify componentsExtract disturbance features and Identify components
Classify the disturbance Classify the disturbance (according to IEEE standards 1159)(according to IEEE standards 1159)
An Ideal PQ Monitoring System would be able to:
Do a Trend analysisDo a Trend analysis
Do Contribution analysis and Locate the sourceDo Contribution analysis and Locate the source
PQ Report and Advice
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Extracted Waveforms
Sampled Voltage and/or Current Waveforms
Captured Transition Events
EventClassification Transition Event Classification Steady-State Event Classification
Steady-State Disturbance Feature VectorTransition Disturbance Feature Vector
OscillatoryTransient
VoltageSag
ImpulsiveTransient
VoltageSwell
MomentarySupply
Interruption
Transition Disturbance Types
Power QualityReports
Storage Display
Over Voltage
SupplyInterruption
HarmonicDistortion
Under Voltage
Steady-State Disturbance Types
Feature Extraction Transition Feature Extractor Steady-State Feature Extractor
Captured Steady- State Events
Event Categorization
Disturbance Extraction
Block Diagram of the “Intelligent PQ Monitoring System”
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Identify the presence of a disturbance and extracting its components
0 50 100 150 200 250 300 350 400-30
-20
-10
0
10
20
30
40
50
0 50 100 150 200 250 300 350 400-4
-3
-2
-1
0
1
2
3
4
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30.95
0.96
0.97
0.98
0.99
1
1.01
Extracted DisturbanceExtracted Disturbance
RMS VariationRMS Variation
Voltage WaveformVoltage Waveform
e(t)e(t)
rr
( )
dtTtVrtvjtVrtvC
wherer
C
TtVrCtVrCtvte
T
⋅⎥⎦⎤
⎢⎣⎡ −⋅⋅+⋅=
=
−⋅+⋅−=
∫0
)4
()()()(
, C
)4
()Im()(Re)()(
Disturbance Extraction
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0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .8-2
0
2
Vol
tage
(pu
)
Vo lta g e W a ve fo rm
0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .8-2
0
2
Vol
tage
(pu
)
E x tra c te d No is e
0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .80 .5
1
1 .5
2
rms
(pu)
R M S Vo lta g e
Example
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Steady State
Steady State
Transition State
Transition State
IntermediateSteady State
IntermediateSteady StateIntermediate
Transition StateIntermediate
Transition State
Captured EventCaptured Event
Captured EventCaptured Event
Captured EventCaptured Event
Event Categorization
State Model
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0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-5
0
5
(a) Sampled voltage waveform
Vs
(p.u
.)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-5
0
5
(b) Extracted disturbance waveform
Ve
(p.u
.)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.21
2
3
4
(c) State transition
STA
TE
0.1 0.102 0.104 0.106 0.108 0.11 0.112 0.114 0.116 0.118
-2
0
2
(d) Captured PQ event waveform time(s)
Vc
(p.u
.)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-2
0
2
(a) Sampled voltage waveform
Vs
(p.u
.)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-0.2
0
0.2
(b) Extracted disturbance waveform
Ve
(p.u
.)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.451
2
3
4
(c) State transition
STA
TE
0.17 0.18 0.19 0.2 0.21 0.22 0.23
-0.1
0
0.1
(d) Captured PQ event waveform time(s)V
c (p
.u.)
Example
Identification of a voltage disturbance during a capacitor switching
Identification of a voltage sag event generated by a remote fault on the
power network
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Steady State Disturbance Feature Vector( 2 elements )
Transient Disturbance Feature Vector( 63 elements )
FeatureExtraction
Transient FeatureExtractor
( Using DWT )
Steady State FeatureExtractor
( Using FFT )
CapturedTransientEventWaveforms
CapturedSteady-stateEventWaveforms
Feature Extraction
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Example: Oscillatory Transient
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0 .6 0 .7 0 .8 0 .9 1 1 .1 1 .2 1 .3 1 .40
0 .05
0 .1
0 .15
0 .2
0 .25
0 .3
0 .35
0 .4
Harmonic Distortion
Under VoltageOver Voltage
No disturbance
Example: Steady-state Feature Space
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Time Domain Harmonic Extraction
0 50 100 150 200 250 3000
0.2
0.4
0.6
0.8
1
| H(jω
)|
Hz
n=2
n=4
n=6n=8
Frequency Response Time response
0 0.01 0.02 0.03 0.04 0.05 0.06 0.070
0.2
0.4
0.6
0.8
1
1.2
1.4
n=8
n=6
n=4n=2
t (sec)
Am
plitu
de
Filter characteristic
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Frequency Domain Harmonics Extraction
Mag
nitu
deA
mplitude
oftheSt ep
Func tion
0
50
100
150
200
250
0
2
4
6
8
10
0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
t (sec)
StepFunction
Output of FT(Magnitude of the 7thorder harmonic voltage)
∫+
−⋅=0
0)(1)(0
0
Tt
t
tjn dtetfT
nF ωωFourier Transform:
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Fast Individual Harmonic Extraction (FIHE)
)()(23)( 1 tttabc mabcm βα ⋅⋅= −
mth order harmonic reconstruction
)(tabcα
dt
ntnM
ntnM
tnMmtmmtmtm
mtmmtmtm
Tt
Tt
t kn
n
n
n
m ∫ ∑+
∞
−=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
+
−⋅
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
+−
+−
=6
12
0
0
0
000
000
0
0
)3
2sin(
)3
2sin(
)sin(
21
21
21
)3
2cos()3
2cos()cos(
)3
2sin()3
2sin()sin(
4)(
πω
πω
ωπωπωω
πωπωω
β
mth order Harmonic Extraction
n≠3 k
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ω1
ω5
ω7
idc=ih5
C
A
B
ih7
ih5
ih1
ω7+ω5
ω1+ω5
ih7
ih1
1st
5th
7th
11th13th
100H
z
250 H
z
200H
z
50 250
350
550
650
Freq(Hz)
Freq(Hz)
5th
300H
z
07th
17th
600
13th
23th
900
300 H
z
300 H
z
1st
11th
300
Fast Individual Harmonic Extraction (FIHE)
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Evaluation of Response Time
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
2
4
6
8
10
t (sec)
Am
plitu
de
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-400
-200
0
200
400
t(sec)
Am
plitu
de
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
50
100
150
200
t (sec)
Am
pli t u
de0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
-200
-100
0
100
200
t (sec)A
mpl
itud e
β’m (t) of the FIHEStep function
Extracted harmonic component (abcm)Distorted signal with step function added
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ndBy filter (2 order filter)
By FT
By FIHE
00.02
50
100
150
200
t (sec)
Am
p litu
de
0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07
With low order filter
0
50
100
150
200
t (sec)A
mpl
i tude
0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07
By filter (6 th order filter)
By FT
By FIHE
With higher order filter
Performance Analysis
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Non-Recursive Technique using ANN
22 )()( nYnYV kkk++=
( ) ⎟⎟⎠
⎞⎜⎜⎝
⎛+=+
ks
kdk fnkVnY φωcos
⎟⎟⎠
⎞⎜⎜⎝
⎛+= k
skdk f
nkVnY φωsin)(
[ ]TnSnSMnSMnSX )()1(...)()1( −−+−=
( )WXfnY k ,)( =
( )WXfnY k ,)( ++ =
X
)(
2)()(
ioldi
newi
WWW
∂∂
−=εα
Least Mean Square (LMS) algorithm used for training the ANN
( ) ( )222 )()()()( nYnYnYnYwhere kdkkdk++ −+−=ε
Actual
Actual
Desired
Desired
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Response to a step increase of 5th
harmonic component
2nd order filter
6th order filter
Kalman Filter
ANN
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Performance Analysis
0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.580.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0.5
FIHEANN IHEFFTButterworth Filter
Response to a step increase of 5th harmonic component
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Maximum Error in FFT Caused by Normal System Frequency Variations
-5
0
5
10
15
20
25
30
35
40
47.5 48 48.5 49 49.5 50 50.5 51 51.5 52 52.5
FUNDAMENTAL FREQUENCY hZ
% P
EAK
ER
RO
R
ERROR INFUNDAMENTALFREQUENCY
ERROR IN 3RDHARMONIC
ERROR IN 5THHARMONIC
ERROR IN 7THHARMONIC
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-0.5
0
0.5
1
1.5
2
2.5
47.5 48 48.5 49 49.5 50 50.5 51 51.5 52 52.5
FREQUENCY Hz
MA
X ER
RO
R % FUNDAMENTAL
3RD HARMONIC
5TH HARMONIC
7TH HARMONIC
Maximum Error in CWT Caused by Normal System Frequency Variations
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Maximum Error in ANN Technique Caused by Normal System Frequency Variations
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Correction for Error due to Normal System Frequency Variations
• The error could be avoided if the sampling period is adjusted digitally to make one wavelength of the adjusted fundamental signal equal to 0.02 s. Measurements with e.g. FFT will then give correct results as far as the effects of frequency deviation are concerned.
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EventClassification
Transient EventClassifier
( SAANN-1 )
Steady State EventClassifier
( SAANN-2 )
Transition Event Feature Vector( 63 elements )
Steady State Event Feature Vector( 2 elements )
OscillatoryTransient
VoltageSag
ImpulsiveTransient
VoltageSwell
MomentarySupply
Interruption
Transient Event Classes
OverVoltage
SupplyInterruption
UnderVoltage
Steady State Event Classes
HarmonicDistortion
NormalCondition
Event Classification
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The ‘Intelligent’ PQ Monitoring System (IPQMS)
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Summary• Use of DSP techniques in PQ monitoring and
analysis results in powerful, accurate and small size equipment.
• DSP based equipment is capable of maintaining accuracy in the “non-ideal environment” of power systems.
• Including Artificial Intelligence in PQ equipment, will help in:–– Classifying and locating the source of distortion and its Classifying and locating the source of distortion and its
contributioncontribution
–– Perform long term feature analysis of disturbance levelsPerform long term feature analysis of disturbance levels
–– Provide methods to identify trends over a period of time Provide methods to identify trends over a period of time and suggest possible solutions!and suggest possible solutions!