expert system for power plants
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07-02-2010, 05:15 PM

please let me know more about the topic in detail. please provide me power point presentation about the topic"expert system for power plant". please let me know about the advantage, dis advantages , applications of the system..[/font]
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07-02-2010, 06:25 PM

.doc   AN EXPERT SYSTEM FOR POWER PLANTS.DOC (Size: 101 KB / Downloads: 124)

.pdf   An-Expert-System-For-Power-Plants-Paper-Presentation.pdf (Size: 182.44 KB / Downloads: 108)

Abstract: An intelligent fault diagnosis and operator support system targeting in the
safer operation of generators and distribution substations in power plants is
introduced in this paper. Based on Expert Systems (ES) technology it incorporates a
number of rules for the real time state estimation of the generator electrical part
and the distribution substation topology. Within every sampling cycle the estimated
state is being compared to an a priori state formed by measurements and digital
signaling coming from current and voltage transformers as well as the existing
electronic protection equipment. Whenever a conflict between the estimated and
measured state arises, a set of heuristic rules is activated for the fault scenario
inference and report. An included SCADA helps operators in the fast processing of
large amounts of data, due to the user-friendly graphical representation of the
monitored system. Enhanced with many heuristic rules, being a knowledge based system,
the proposed system goes beyond imitation of expert operatorsâ„¢ knowledge, being able
to inference fault scenarios concerning even components like the power electronic
circuits of generator excitation system. For example, abnormal measurements on
generatorâ„¢s terminals can activate rules that will generate fault hypothesis possibly
related to an excitation thyristors abnormal switching operation.
Artificial Intelligence is a branch of informatics that was widely
adopted in industrial automation during the past fifteen years. AI programs are
developed and used in computer science since the early days of digital computers.
Only during the last two decades though industry has taken advantage of those special
features that make AI so unique in modeling and representing knowledge, as well as
imitating the common sense reasoning. The continuous augmentation of available
computational strength and the low cost of modern microprocessors on one hand, and
the software tools recently developed on the other, leaded in a remarkable expansion
of AI applications in the domain of electrical power systems and power electronics.
Expert Systems:
Among others is a very popular AI technique in industry. According to the
working group D10 of the line protection subcommittee , An Expert System (ES) is a
computer program that uses knowledge and inference procedures to solve problems that
are ordinarily solved through human expertise. The main components of an ES are: a)
inference engine, b) database, c) user-interface. ES incorporate rule kind of
programming. They are currently being used in many applications in the area of power
systems and power electronics. Several systems for the short or long term load
forecasting have been already introduced based on ES technology .Intelligent SCADA
and offline training systems for non-expert operators is another application where ES
are often used. All these offline applications are nevertheless not critical for the
power system robustness and stability. More and more applications are currently using
ES in real time monitoring and/or control, and AI turns to be a common practice in
industrial automation. Regarding the category of real time monitoring and control
systems, many applications have already been proposed, focusing mainly on topology
estimation and fault diagnosis in distribution substations , and on the fault
diagnosis and restoration strategies for transmission networks.
Knowledge Based Systems: Go beyond Expert systems in sense that except for imitating
the expertsâ„¢ problem solving behavior, they enrich problem solving strategy with
methods that are not originally employed by human experts. Systems that use domain
knowledge to guide searches that differ from the expertsâ„¢ are known as Knowledge
Based Systems (KBS).
Intelligent Decision Support Systems: Decision Support Systems (DSS) are
computerized tools derived from decision theory used to enhance user ability to make
decisions efficiently. They are not intended to offer the final solution, but rather
to explore and seek alternative solutions. The intimate decision is left to the user.
Intelligent Support Systems (IDSS) add intelligence to existing systems to enhance
problem solving
ability and help maintain a broad range of knowledge about a particular domain. They
are used for capturing, organizing and reapplying knowledge including decision rules
and criteria.
Artificial Neural Networks : That simulate the neural activity of the human brain,
deserve the same recognition at the same level as the AI methodologies mentioned
above. ANN have already been broadly classified under the AI domain. They do not have
some of the AI properties but can be placed under the umbrella of AI technologies.
Expert Systems basically mimic the problem solving behavior of experts using domain
knowledge acquired through interviews during the knowledge acquisition phase.
Knowledge based ES as mentioned go beyond in a sense that they enrich problem-solving
strategy with methods that are not ordinarily employed by human experts . The
proposed system is designed for the generators and distribution substations
protection in power plants. Especially in weak interconnected power systems,
operation of plants with over than 1000MVA of installed power can be of great
importance for the stability and efficiency of the whole system. An unhandled fault
can have a significant impact on power availability for an expanded area of the
transmission network. Besides, damage on
a generator would add a very high financial overhead, as generators of this size cost
several million Euros. Such unhandled faults have though been reported in the past
and can lead even to human casualties. The system is designed to instantly recognize
and report abnormalities that can be related to a mechanical equipment failure or to
an electrical, or electronic equipment malfunction, or even to a mistaken human
operator control instruction.
System Overview
Distribution substations are the interlocking connection points of
power plants to the electrical power grid. The state of all substation components
(circuit breakers, disconnectors, protection relays etc.) is monitored and recorded
to Digital Fault Recorders (DFR) while the electrical values of every circuit
breaker, bus, transformer and generator terminal are measured by ad hoc installed
current and Voltage-transformers.
Figure 1. Snapshot of the system GUI applied on a 350MVA unit of a thermoelectric
From the operator perspective an alarm situation arises when a monitored
value exceeds a predefined upper or lower limit, activating a sound or light alert on
control panel. An expert operator would handle this situation by first checking the
control panel indications, trying then to locate the faulted area, according to the
theoretical state of the switching equipment and the current values of the
measurement points. This procedure may take some time especially when operators act
under stress conditions. On the other hand inference process can be a very
complicated task when some input data or measurements are faulted. For example, a
very difficult fault to diagnose has been reported in the past, when after a voltage
transformer explosion a bypass switch broke and caused short-circuit, supplying the
generator with an unbalanced load. In this case the switch position was mistakenly
reported and the operator could not easily detect the real current flow path.
Figure 2. Fault recognition and analysis algorithm
The time between the fault appearance and its recognition and
restoration inference can be critical for the equipment and personnel safety.
A sophisticated fault diagnosis and monitoring system can detect similar
contradictions and point out the optimal restoration sequence. The proposed expert
system uses a dedicated module for the topology and state estimation of the generator
and the distribution substation. This module considers as known inputs the voltages
and currents measured on the arriving from the network transmission lines, as well as
the generator and transformer current and voltage. Also known is considered the state
of the circuit breakers, disconnectors, protection relays etc. Based on the above
values the system composes an estimated state regarding the voltage and current flow
at all measuring points. Another module composes the same state based on the acquired
measurements at the same points. The estimated and measured states are being compared
till a conflict arises between the estimated and measured values of a certain
measurement point. Then the fault locating module locates the faulted area, and the
fault scenario module inferences the fault hypothesis. The system then activates the
restoration module in order to propose the restoration sequence bringing the process
back to its normal operation.
Figure 3. Basic system architecture diagram
System Architecture
The proposed knowledge based expert system runs on a dedicated x86
based computer. Extra data acquisition and digitization hardware is required
connected to the PCI bus for fast data acquisition of the various measured or
reported values of generator and substation components. The core of the system is the
running software. It is consisted of three main subprograms running simultaneously
and using three different threads
Data acquisition and monitoring System: This program is responsible for the data
acquisition, interfacing the external acquisition hardware. It passes all acquired
information to the inference engine and displays some defined data to the system
monitor. It also displays some selected by the operator data, implementing thus the
system GUI input and output. Selected data are sent to the system Data Base for
history logging.
Data Base: The system database is consisted mainly by two modules:
-The knowledge database keeps all the knowledge acquired during the system
design phase via exhausting interviews with the station expert operators. This
database is designed in a way that allows knowledge modification and update, offering
to the system flexibility and upgrade capability.
-The history recording and logging data base which is used for the storage of
selected values that can be accessed by the inference engine in real time, or can be
even used offline for data further processing and evaluation.
Inference Engine: This program is the heart of the whole system. It is an intelligent
function based on rule-base programming. Using the current data values of the data
acquisition module and the knowledge stored in the knowledge base, it inferences
knowledge imitating the expert operator reasoning. In the same time it performs
advanced checks that an operator cannot do in real time, using special rules that
offer a quality process monitoring and analysis. When a fault is diagnosed the engine
inferences the fault scenario and proposes the necessary restoration actions.
Alternatively, the inference engine can produce not only message output but control
signaling as well.
This work introduces a knowledge based expert system for the generator
and substation monitoring and fault diagnosis in power plants. The fault detection is
based on a comparison algorithm polling for specific measurement values, comparing
them to the corresponding estimated values, according to the system current inputs,
and then checking for possible conflicts. Whenever a conflict arises the system uses
rule-based reasoning to inference the fault scenario and the optimal restoration
sequence, which is fed back to the control room operator for further action. The
knowledge based expert system efficiency is based on, but not limited to, the expert
operators reasoning.
It can report and analyze faults, even having received partially
mistaken input data, something that for a human operator is very difficult or
impossible in real time, especially under emergency situations. The knowledge base
can be continuously updated with rules, offering thus a learning capability that
enriches the system with new, recent experience. Based on some advanced rules the
system can offer fault scenario inference performing multiple input calculations,
even with strictly restrictive complexity for the human operator real-time
processing. This can lead to a detailed fault diagnosis even when the cause is
indirect. For example, a failure of power semiconductor elements of the generator
field excitation rectifier, can be recognized and be classified indireclty, according
to its effects on the measured and estimated parameters.
[1] M.S Kandil-N.E.Hasanien: Long-Term Load Forecasting for fast Developing utility
using a knowledge based expert system, IEEE Transactions on Power Systems, vol7, No2,
May, 2002
[2] M.Negnevitsky: A knowledge based tutoring system for teaching fault analysis.
Transactions on Power Systems, vol13, No1, May 1998
[3] M.Kezunovic-Z.Ren-D.R.Sevcik-J.Lucey: An. expert system for automated analysis of
circuit breaker operations. ISAP03, Lemnos August 2003
[4] H.Lee-B.AhnY.Park:Afault diagnosis expert system for distribution substations,
Transactions on Power Systems, vol15, No1,January 2000
[5] H.Lee- D.Park- B.shin- Y.Park- J.Park- S.Venkata: A fuzzy expert system for the
integrated fault diagnosis, IEEE Transactions on Power Delivery, vol5, No2 April 2000

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