Seminario Brasileiro de Qualidade da Energia Eletrica Power Quality – An Integrated Perspective P. F. Ribeiro, MBA, PhD, PE Professor of Engineering Calvin

Seminario Brasileiro de Qualidade da Energia Eletrica

Power Quality – An Integrated Perspective

P. F. Ribeiro, MBA, PhD, PE

Professor of EngineeringCalvin College

Engineering Department

Grand Rapids, Michigan 5 0 5

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Outline

Background

What is Power Quality?Complex Issue (need for better definitions)Harmonics - What is your responsibility?Background DistortionMore than Just Voltage Diviations

Power Quality Cost ($$$$) Issues

Advanced in Power Quality SolutionsMonitoringSoftware (the fuzzy/neural evolution)Hardware (active compensators, switches)Concepts (Custom Power, PQ Parks, FACTS)


Legal Issues

Impact of Privatization and DeregulationOpportunities (differentiated power services - premium power, consulting, etc) Reliability, Quality and PriceNew Utility/Manufacturers Approaches (PQ Programs)

Standards

R &D

General Principles - An Advice

Conclusions

Outline


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


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


Advanced Power Quality Solutions

Software

Wavelet Theory

Expert Systems

Fuzzy Logic

Genetic Algorithms

Neural Network



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



Measuring and Monitoring

Artificial Inteligence Instruments

Remote Access

Integrated Diagnostic

Comprehensive System Monitoring

Centralized Monitoring (GPS)



Systems and Concepts

PQ Parks

Custom Power / FACTS / Transfer Switches

Higher Immunity

Special Contracts

Power Management Companies


The PQ Equation

PowerQuality A B C dPd ( ) $

A - Electric parametersB - Economic ParametersC - Structure of the Sector

$

PQ



Computer Modeling and Simulation

Graphical Environment

Inclusion of Artificial Intelligence (fuzzy / neural, wavelets)

Integrated Economic Analysis


Standards


R&D - New Developments


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.


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.


Wavelets: Analysis / Applications

Impulsive transientscommutation 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; * Signal/System Identification; * Non-Invasive Testing/Measurements; * Power System Analysis in General; * Integrated characterization of voltage disturbances


Wavelet TheoryMaking Waves: Big and SmallTransient 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).


-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 ba

f t ht b

adt( , ) ( ) *

11

2

I don’tget it...I’ll try later


Scaled and Translated Wavelets


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).

f tC

Wf a baht b

a

dadb

a

Cwh w dw

h

h

( ) ( , )

( )

1 1

1

2

2

Yes !


f t k Wf m n a g a nbnm

mm( ) ( , ) ( )

00

2 0 0 0

Alternatively, expressing the inverse wavelet transform in a discrete form, we have:


The Wavelet Transform


Illustration of Flexibility

Original Waveform to be analyzed

2 Wavelet Components

Reconstruct function


Impulsive Transient

Commutation Notches


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


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.


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.


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.


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.


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) systemswavelet theory / advanced Signal Processing neural networks genetic algorithms, etc


ConclusionsPQ 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


Input Waveforms

Spectral Analysis

Basic Classification of Disturbancesby Expert System

Load type 1 Load type 2 Cap. Switching

Wavelet Analysis

(non stationary distortions)(periodic distortions)

(periodic)(non stationary)

Genetic Algorithms

.............

Neural Network Trained to Identify

Periodic and Non-Periodic

Waveforms

Advanced Classification of Disturbances By Fuzzy System

Fuzzy Logic Neuro Net

(Trained by Neuro Net)

Documents

Seminario Brasileiro de Qualidade da Energia Eletrica Power Quality – An Integrated Perspective P. F. Ribeiro, MBA, PhD, PE Professor of Engineering Calvin