06-02-2013, 04:16 PM
Artificial Intelligent Application to Power System Protection
[attachment=49893]
INTRODUCTION
The microprocessor technology brings unquestionable
improvements of the protection relays- criteria signals are
estimated in a shorter time; input signals are filtered-out
more precisely; it is easy to apply sophisticated corrections;
the hardware is standardized and may communicate with
other protection and control systems; relays are capable of
self-monitoring. All this, however, did not make a major
breakthrough in power system protection as far as security,
dependability and speed of operation are considered. The
key reason behind this is that the principles used by digital
relays blindly reproduce the criteria known for decades.
The relaying task, however, may be approached as a pattern
recognition problem - by monitoring its inputs, the relay
classifies on-going transients between internal faults and all
the other conditions. Or, the protective relaying may be considered
as a decision making problem - the relay should decide
whether to trip or retrain itself from tripping. This observation
directly leads to AI application in power system
protection [1-4]. Practically, it includes the artificial neural
network approach (pattern recognition), as well as the expert
system and fuzzy logic methods (decision making) [5-8].
This paper briefly reviews the general problems and constraints
in power system protection and presents the basics of
AI methods as applied to protective relaying. After general
presentation of the problem (Section II), a brief description
of the AI methods is given (Section III). Some examples of
the AI approach to power system protection are presented in
Section IV and V. The test results are also included in the
paper.
PROBLEMS IN POWER SYSTEM PROTECTION
The problems result mainly from the trade-off between
the security demand (no false trippings), and the speed of
operation and the dependability (no missing operations) requirements.
The more secure is the relay (both the algorithm
and its particular settings), the more it tends to misoperate or
operate slowly. And vice versa, the faster is the relay, the
more it tends to operate falsely. The problems listed below
reflect the current practice in power system protection.
There are basically two ways to mitigate the problem of
limited recognition power of the classical relaying principles.
One of them is to improve and extend the measurements
available to a given relay (for example, optical CTs
for improvement and substation integration for extension).
The second way is to improve the recognition process itself
based on what is already available and either:
· search for the new relaying principles, or
· apply several of known principles in one relay to improve
the recognition, or
· apply correction of the CT and CVT transient error, or
· improve a type of fault determination by using of the
ANNs classifier, or
· use self-organizing algorithms such as ANNs to find out
automatically a protection principle.
It always takes certain time to estimate the criteria signals
accurately enough to base the tripping decision on them.
Either they are measured fast or accurately. There is no perfect
digital measuring algorithm that solves this well known
conflict between the speed and the accuracy. Either certain
pre-filtering is applied, or the basic algorithm uses longer
data window; or certain post-filtering is employed (or even a
combination of these three means). There is always a level
of uncertainty in the estimate of the criteria signal at the beginning
of a disturbance when the relay operation is mostly
wanted. In some situations, although unprecise, the value of
the criteria signal enables solid decision, but is other cases,
such as a fault at the end of the protection zone, the relay
must wait for more precise estimate of the criteria signals.
ARTIFICIAL INTELLIGENCE METHODS
AI is a subfield of computer science that investigates
how the though and action of human beings can be mimicked
by machines [5]. Both the numeric, non-numeric and
symbolic computations are included in the area of AI. The
mimicking of intelligence includes not only the ability to
make rational decisions, but also to deal with missing data,
adapt to existing situations and improve itself in the long
time horizon based on the accumulated experience.
Three major families of AI techniques are considered to
be applied in modern power system protection [1,5]:
· Expert System Techniques (XPSs),
· Artificial Neural Networks (ANNs),
· Fuzzy Logic systems (FL).
APPLICATION TO POWER TRANSFORMER PROTECTION
As an examples authors have considered fuzzy logic and
ANN application to differential transformer protection. The
differential relaying principle in the case of a power transformer
shows certain limitations - detection of a differential
current does not provide a clear distinction between internal
faults and other conditions. Inrush magnetizing currents,
stationary overexcitation of a core, external faults combined
with saturation of the CTs and/or CTs and protected transformer
ratio mismatch are the most relevant phenomena
which may upset the current balance causing the relay to
maloperate.
CONCLUSIONS
The paper reviews the AI approaches to power system
protection and focuses on the application of ANN and fuzzy
logic techniques.
A number of novel application and concepts have been
presented including fuzzy logic approach to differential
transformer protection and ANN application to the transformer
protection, CT and CVT transients correction, and.
fault-type classification. Included examples demonstrate application
of the AI methods and their features.
[attachment=49893]
INTRODUCTION
The microprocessor technology brings unquestionable
improvements of the protection relays- criteria signals are
estimated in a shorter time; input signals are filtered-out
more precisely; it is easy to apply sophisticated corrections;
the hardware is standardized and may communicate with
other protection and control systems; relays are capable of
self-monitoring. All this, however, did not make a major
breakthrough in power system protection as far as security,
dependability and speed of operation are considered. The
key reason behind this is that the principles used by digital
relays blindly reproduce the criteria known for decades.
The relaying task, however, may be approached as a pattern
recognition problem - by monitoring its inputs, the relay
classifies on-going transients between internal faults and all
the other conditions. Or, the protective relaying may be considered
as a decision making problem - the relay should decide
whether to trip or retrain itself from tripping. This observation
directly leads to AI application in power system
protection [1-4]. Practically, it includes the artificial neural
network approach (pattern recognition), as well as the expert
system and fuzzy logic methods (decision making) [5-8].
This paper briefly reviews the general problems and constraints
in power system protection and presents the basics of
AI methods as applied to protective relaying. After general
presentation of the problem (Section II), a brief description
of the AI methods is given (Section III). Some examples of
the AI approach to power system protection are presented in
Section IV and V. The test results are also included in the
paper.
PROBLEMS IN POWER SYSTEM PROTECTION
The problems result mainly from the trade-off between
the security demand (no false trippings), and the speed of
operation and the dependability (no missing operations) requirements.
The more secure is the relay (both the algorithm
and its particular settings), the more it tends to misoperate or
operate slowly. And vice versa, the faster is the relay, the
more it tends to operate falsely. The problems listed below
reflect the current practice in power system protection.
There are basically two ways to mitigate the problem of
limited recognition power of the classical relaying principles.
One of them is to improve and extend the measurements
available to a given relay (for example, optical CTs
for improvement and substation integration for extension).
The second way is to improve the recognition process itself
based on what is already available and either:
· search for the new relaying principles, or
· apply several of known principles in one relay to improve
the recognition, or
· apply correction of the CT and CVT transient error, or
· improve a type of fault determination by using of the
ANNs classifier, or
· use self-organizing algorithms such as ANNs to find out
automatically a protection principle.
It always takes certain time to estimate the criteria signals
accurately enough to base the tripping decision on them.
Either they are measured fast or accurately. There is no perfect
digital measuring algorithm that solves this well known
conflict between the speed and the accuracy. Either certain
pre-filtering is applied, or the basic algorithm uses longer
data window; or certain post-filtering is employed (or even a
combination of these three means). There is always a level
of uncertainty in the estimate of the criteria signal at the beginning
of a disturbance when the relay operation is mostly
wanted. In some situations, although unprecise, the value of
the criteria signal enables solid decision, but is other cases,
such as a fault at the end of the protection zone, the relay
must wait for more precise estimate of the criteria signals.
ARTIFICIAL INTELLIGENCE METHODS
AI is a subfield of computer science that investigates
how the though and action of human beings can be mimicked
by machines [5]. Both the numeric, non-numeric and
symbolic computations are included in the area of AI. The
mimicking of intelligence includes not only the ability to
make rational decisions, but also to deal with missing data,
adapt to existing situations and improve itself in the long
time horizon based on the accumulated experience.
Three major families of AI techniques are considered to
be applied in modern power system protection [1,5]:
· Expert System Techniques (XPSs),
· Artificial Neural Networks (ANNs),
· Fuzzy Logic systems (FL).
APPLICATION TO POWER TRANSFORMER PROTECTION
As an examples authors have considered fuzzy logic and
ANN application to differential transformer protection. The
differential relaying principle in the case of a power transformer
shows certain limitations - detection of a differential
current does not provide a clear distinction between internal
faults and other conditions. Inrush magnetizing currents,
stationary overexcitation of a core, external faults combined
with saturation of the CTs and/or CTs and protected transformer
ratio mismatch are the most relevant phenomena
which may upset the current balance causing the relay to
maloperate.
CONCLUSIONS
The paper reviews the AI approaches to power system
protection and focuses on the application of ANN and fuzzy
logic techniques.
A number of novel application and concepts have been
presented including fuzzy logic approach to differential
transformer protection and ANN application to the transformer
protection, CT and CVT transients correction, and.
fault-type classification. Included examples demonstrate application
of the AI methods and their features.