Transcript FUTURE ANALYSIS TOOLS FOR POWER

Power Quality – An Integrated Perspective
P. F. Ribeiro, MBA, PhD, PE
Professor of Engineering
Calvin College
Engineering Department
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Grand Rapids, Michigan
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Outline
Background
What is Power Quality?
Complex Issue (need for better definitions)
Harmonics - What is your responsibility?
Background Distortion
More than Just Voltage Diviations
Power Quality Cost ($$$$) Issues
Advanced in Power Quality Solutions
Monitoring
Software
(the fuzzy/neural evolution)
Hardware (active compensators, switches)
Concepts (Custom Power, PQ Parks, FACTS)
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Outline
Legal Issues
Impact of Privatization and Deregulation
Opportunities (differentiated power services - premium power,
consulting, etc)
Reliability, Quality and Price
New Utility/Manufacturers Approaches (PQ Programs)
Standards
R &D
General Principles - An Advice
Conclusions
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Power Quality Principles;
1. Understand the problem
2. Find the real cause before you find the best solution
3. The solution must be always cost effective
4. Be open and work closely with the customer
5. Be Pro-Active, Not Reactive
6. Do not go after magic solutions
7. Educate utility and customers
8. Offer alternative PQ Services (US, France)
9. Be aware of legal issues
10. Satisfy the customers - They are alwyas right
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A Complex World:
A Philosophical Reflection
These things are so delicate and numerous that it takes a
sense of great delicacy and precision to perceive them and
judge them correctly and accurately: Most often it is not
possible to set it out logically as in mathematics, because
the necessary principles are not ready to hand, and it
would be an endless task to undertake. The thing must be
seen all at once, at a glance, and not as a result of
progressive reasoning, at least up to a point.
Blaise Pascal, 1650
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Advanced Power Quality Solutions
Software
Wavelet Theory
Expert Systems
Fuzzy Logic
Genetic Algorithms
Neural Network
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Advanced Power Quality Solutions
Hardware
Active Harmonic Filters
Micro SMES for Power Quality
Large SMES for Transmission / Distribution
PWM Based Higher Power Compensators
FACTS Controller, Custom Power Devices
Transfer Switches
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Advanced Power Quality Solutions
Measuring and Monitoring
Artificial Inteligence Instruments
Remote Access
Integrated Diagnostic
Comprehensive System Monitoring
Centralized Monitoring (GPS)
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Advanced Power Quality Solutions
Systems and Concepts
PQ Parks
Custom Power / FACTS / Transfer Switches
Higher Immunity
Special Contracts
Power Management Companies
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The PQ Equation
PowerQuality   ( A  B  C )dPd $
A - Electric parameters
B - Economic Parameters
C - Structure of the Sector
$
PQ
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Advanced Power Quality Solutions
Computer Modeling and Simulation
Graphical Environment
Inclusion of Artificial Intelligence (fuzzy / neural,
wavelets)
Integrated Economic Analysis
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Standards
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R&D - New Developments
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Background
Traditional power quality analysis tools have proven very useful in
power system steady state distortion analysis.
However, considering the ever growing utilization of electronically
controlled loads and system devices, the consequent dynamics of
distortion generation, propagation and interaction with the system,
one would need a more powerful technique to efficiently analyze the
system performance in the presence of non-stationary distortions.
New techniques have recently unfolded which in the future may help
us to better categorize and analyze in a more effective way many
types of voltage distortions or power quality deviations.
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Background
From Precise Electronics to Fuzzy Logic
Twenty years ago the power sector was having some difficulties to
keep up with the growing concerns with power quality due to the
rudimentary monitoring instrumentation.
Now the power sector is having difficulties to cope with the amount
of information generated by the new hardware and software
technologies.
Artificial intelligence and other advance techniques seem to be the
natural way to overcome the difficulties in handling the huge
amount of power quality monitoring data.
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Wavelets: Analysis / Applications
Impulsive transients
commutation notches, etc.
can be represented ,analyzed, and identified more easily and
objectively
-------The following possible applications are envisaged for power system
analysis:
*
Transient Analysis;
*
Non-stationary Voltage Distortions;
*
Power Signature Recognition;
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Signal/System Identification;
*
Non-Invasive Testing/Measurements;
*
Power System Analysis in General;
*
Integrated characterization of voltage disturbances
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Wavelet Theory
Making Waves: Big and Small
Transient Distortion Analysis
The traditional spectral (Fourier) method has some severe limitations in dealing
with the new electrical environment / phenomena.
Wavelets are functions localized both in time(or space) and frequency. These
functions are generated from a single generating (mother) wavelet by translations
and dilations.
These properties make wavelets very attractive for non-stationary power quality
analysis. In many cases, not only are fewer functions required with wavelets than
with Fourier series, but anomalies such "Gibbs" phenomenon are reduced.
Wavelets have been used effectively in applications such as multidimensional
signal processing, video data compression, and reconstruction of high resolution
images and high quality speech.
Considering the analysis of transient signals in power systems, wavelets could be
very well applied with advantages over Fourier methods. The advantage would
rely on the precision and speed of the method to analyze transient signals as well
as the economy of storage space).
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-The Wavelet Theory
Wavelet theory is the mathematics associated with building a
model for a signal with a set of special signals, or small waves,
called wavelets. They must be oscillatory and have
amplitudes which quickly decay to zero.
The required oscillatory condition leads to sinusoids as the
building blocks (particularly for electrical power systems).
However wavelets do not need to be damped sinusoids.
Mathematically speaking, the wavelet transform or
decomposition of a function, f(t), with respect to a mother
wavelet, h(t), is:
Wf (a , b) 
1
a
1
2

 t  b
f (t )h 
 dt
 a 
*
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I don’t
get it...
I’ll try
later
Scaled and Translated Wavelets
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The inverse transform creates the original function by summing
appropriately weighted, scaled and translated versions of the
mother wavelet, as indicated by the following equation .
The weights are the wavelet coefficients, Wf(a,b).
Yes !
1
1  t  b  dadb
f (t ) 
Wf (a , b)
h
 2

Ch
a  a  a
1
2
Ch   h( w) dw
w
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Alternatively, expressing the inverse wavelet
transform in a discrete form, we have:

f (t )  k

  Wf ( m, n )
m
m
2
a 0 g(a 0
m 0 n  0
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 nb0 )
The Wavelet Transform
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Illustration of Flexibility
Original Waveform to be analyzed
2 Wavelet Components
Reconstruct function
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Impulsive Transient
Commutation Notches
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Wavelets in Power Systems?
Same principle:
establishing libraries of waveforms which would fit a certain type of
disturbance or transient. These libraries equipped with fast
numerical algorithms can enable real-time implementation of a variety
of signal processing tasks.
This characterization of the signal provides efficient superposition in
terms of oscillatory modes on different time scales .
Power Systems Applications
-Transient Analysis
-Non-stationary Voltage Distortions
-Power Signature Recognition
-Signal/System Identification
-Non-Invasive Testing/Measurements
-Power System Analysis in General
-Integrated characterization of voltage
disturbances, e.g. transients and harmonic distortions
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Expert Systems
Expert systems are computer systems implemented by methods
and techniques for constructing human-machine systems with
specialized problem-solving expertise.
The rules usually take the form of "IF .... THEN ..." statements
which can be chained together to form a conclusion from the
data. The main drawback with expert systems is that the rules
of inference must be collected from a human expert and
converted to an acceptable form.
Fuzzy Systems
Fuzzy systems are a type of expert system but with fuzzy rules.
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Neural Networks
Neural networks consist of a number of very simple and highly
interconnected processors called neurodes, which are the
analogs of the biological neural cells, or neurons, in the brain.
The neurodes are connected by a large number of weighted
links, over which signals can pass.
As a pattern classifier neural networks can be used for a
number of PQ applications, such as waveform classification,
system identification, etc. Recently neural nets have been
used for waveform classification, and identification of
harmonic sources where sufficient direct measurement data
are not available.
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Fuzzy Logic - Back To Empiricism?
Not Really a Harmonic Distortion.
What is the correlation between high harmonic distortions and the World Cup? This might
not have been a very hard one to figure out.
However, most power quality problems are not well defined or correlated with an specific
cause. In many cases, it is also difficult to assign a specific number to an input value for
any of a variety of reasons.
Consider the set of voltage disturbances (waveform faults) collected at two different points
of the electric system. Also consider that different load conditions and instruments might
be used. How would these qualitative reports correspond to the actual picture of what has
happened to the system.
That is when the concept of fuzzy logic becomes very handy. The fuzzy principle:
everything is a matter of degree, seems to fit well with many of the difficulties one faces in
interpreting power quality signatures.
Fuzzy logic or sets are useful anywhere measurements are imprecise or their interpretation
depends strongly on context or human opinion.
Fuzzy logic thus captures the system condition plus the human / instrument perception, in
place of the objective measurements themselves, and thus contain both more and less
information than the original measurements values.
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Evolutionary Systems - Genetic Algorithms
Imagine that you want to create a database to classify power quality waveform distortions.
Most of the relationships seem initially obvious. But when the database grows into a large
system, establishing relationships becomes an enormous job.
This is corroborated by the fact that not all power quality problems are well defined. The
process to establish correlation has been found to be similar to genetic reproduction.
A GA provides an efficient method of searching through a wide range of possibilities.
Simple GAs use three key operators to explore their search space: reproduction, mutation,
and crossover.
Considering the numerous conditions of the electrical parameters of a power systems it is
desirable to use a mathematical tool which might concentrate on the most significant
cases.
GAs can be used to predict typical voltage quality deviations such as harmonic distortions
and the consequent cumulative or statistical effect of many sources under numerous and
dynamic system and load conditions.
In a power quality case the approach could be to search out for the best combination of
system variables: capacitor bank switching, load impedance levels, and the harmonic
current injections.
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Developing a Comprehensive PQ
Waveform Identification System
An integrated way to develop a comprehensive
PQ identification waveform identification
system would utilize a combination of:
expert (fuzzy) systems
wavelet theory / advanced Signal Processing
neural networks
genetic algorithms, etc
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Conclusions
PQ Will continue to grow in importance as the electric sector operates
within a truly free economy.
Utilities, customers and manufacturers will have to cooperate to
establish a stable model for the power quality industry sector
Differentiated Premium Power will Become a reality (US, France)
Reliability and Quality will become as important as Price
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Input Waveforms
(periodic distortions)
(periodic)
(non stationary distortions)
Spectral Analysis
Wavelet Analysis
(non stationary)
Basic Classification of Disturbances
by Expert System
Advanced Classification of Disturbances
By Fuzzy System
(Trained by Neuro Net)
Neural Network Trained to Identify
Periodic and Non-Periodic
Waveforms
Genetic Algorithms
Fuzzy Logic
Load type 1
Load type 2
Neuro Net
Cap. Switching
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