This document provides a summary of an undergraduate study report on adaptive relaying using artificial intelligence techniques. It discusses artificial intelligence methods like expert systems, artificial neural networks, and fuzzy logic that have been applied in power system protection. It also analyzes some key aspects of using these techniques, including the design of neural networks and the challenges of generating comprehensive training sets from large power system data. The document serves as the abstract and introduction to the full study report.
This presentation on Power Quality Improvement Techniques: A Review presented by Sahid Raja Khan student of B. Tech. Electrical Engineering of Compucom Institute of Technology and Management Jaipur. It describes the improvement technique of Power Quality at GSS and other Substations including Generating Stations.
This presentation on Power Quality Improvement Techniques: A Review presented by Sahid Raja Khan student of B. Tech. Electrical Engineering of Compucom Institute of Technology and Management Jaipur. It describes the improvement technique of Power Quality at GSS and other Substations including Generating Stations.
Electrical power protection and relay coordination trainingDEVELOP
DEVELOP Oil&Gas Training Center menyelenggarakan Training Electrical Power System Protection & Relay Coordination Engineering yang akan mengajarkan kepada Anda tentang Prinsip-prinsip Power System Protection Principles,Skema esential untuk Electrical Protection System, Relay Coordination System beserta optimalisasi penerapannya.
Why we need power-system protection? System ProtectionSystem Protection
In addition to this, it is used for the protection of power system and prevent the flow of fault current. It can help in preventing the continuation of flow by quickly disconnecting the short circuit. ... The protection relay should operate on the commissioned condition in the electrical power system.
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International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Electrical power protection and relay coordination trainingDEVELOP
DEVELOP Oil&Gas Training Center menyelenggarakan Training Electrical Power System Protection & Relay Coordination Engineering yang akan mengajarkan kepada Anda tentang Prinsip-prinsip Power System Protection Principles,Skema esential untuk Electrical Protection System, Relay Coordination System beserta optimalisasi penerapannya.
Why we need power-system protection? System ProtectionSystem Protection
In addition to this, it is used for the protection of power system and prevent the flow of fault current. It can help in preventing the continuation of flow by quickly disconnecting the short circuit. ... The protection relay should operate on the commissioned condition in the electrical power system.
We are occupied in providing Reyrolle 7SR11 The offered product is extremely admired by our patrons for its top performance and longer working life.;
Features:
High performance
Long functional life
Easy to use
Longer life service
Compact structure
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Practical Power System Protection for Engineers and TechniciansLiving Online
This workshop has been designed to give plant operators, electricians, field technicians and engineers a better appreciation of the role played by power system protection systems. An understanding of power systems along with correct management will increase your plant efficiency and performance as well as increasing safety for all concerned.
The workshop is designed to provide excellent understanding on both a theoretical and practical level. Starting at a basic level and then moving onto more detailed applications, it features an introduction covering the need for protection, fault types and their effects, simple calculations of short circuit currents and system earthing. This workshop includes some practical work, simple fault calculations, relay settings and the checking of a current transformer magnetisation curve.
WHO SHOULD ATTEND?
Design engineers
Electrical engineers
Electrical technicians
Electricians
Field technicians
Instrumentation and design engineers
Plant operators
Project engineers
MORE INFORMATION: http://www.idc-online.com/content/practical-power-system-protection-engineers-and-technicians-140?id=7086
Alex Arnall: Adaptive Social Protection: Mapping the Evidence and Policy Cont...STEPS Centre
Presentation at the STEPS Conference 2010 - Pathways to Sustainability: Agendas for a new politics of environment, development and social justice
http://www.steps-centre.org/events/stepsconference2010.html
An adaptive protection scheme to prevent recloser-fuse miscoordination in dis...iosrjce
IOSR Journal of Electrical and Electronics Engineering(IOSR-JEEE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electrical and electronics engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electrical and electronics engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
5GHz MIMO System Power Amplifier design with Adaptive Feedforward Linearizati...Ahmed Nasser Agag
- In such transceiver system, we used power amplifier stage in transmitter section and polyphase filter (PPF) in local oscillator (LO) section
- Less linearity of power amplifier causes higher order intermodulation and consequently destroys orthogonality between subcarriers in OFDM signals.
- Phase error in quadrature LO signal causes crosstalk between I and Q signals and results unavoidable demodulation errors.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
ARTIFICIAL INTELLIGENCE IN POWER SYSTEMSvivatechijri
In today’s world we require a continuous & definitive supply of electricity for proper functioning in
modern and advanced society. AI (AI) may be a field that was found on the idea of human intelligence where AI
precisely simulates natural intelligence. AI (Artificial Intelligence) is the mixture of expert task, mundane task
and formal task. Power Systems were used from the late 19th century and that they are one among the essential
needs that we'd like in our modern, developing day to day life. Power systems are used for transmission and
delivering the electricity to all or any machines. AI (Artificial Intelligence) plays a serious role in power systems
where they solve different problems in power systems like scheduling, calculating, statistics, forecast. As AI
(Artificial Intelligence) was being developed in several fields we could see the impact that it made on the facility
systems also, the humanly solved mathematical functions were solved by machines and every one the tasks are
performed by the machines.AI techniques became popular for solving different problems in power systems like
control, planning, scheduling, forecast, etc. These techniques can affect difficult tasks faced by applications in
modern large power systems with even more interconnections installed to satisfy increasing load demand. The
appliance of those techniques has been successful in many areas of power grid engineering
Reliable and Efficient Data Acquisition in Wireless Sensor NetworkIJMTST Journal
The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a cluster head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF, PSO-MV) in terms of network lifetime and energy consumption.
Wireless sensor networks, clustering, Energy efficient protocols, Particles S...IJMIT JOURNAL
Wireless sensor networks (WSN) is composed of a large number of small nodes with limited functionality.
The most important issue in this type of networks is energy constraints. In this area several researches have
been done from which clustering is one of the most effective solutions. The goal of clustering is to divide
network into sections each of which has a cluster head (CH). The task of cluster heads collection, data
aggregation and transmission to the base station is undertaken. In this paper, we introduce a new approach
for clustering sensor networks based on Particle Swarm Optimization (PSO) algorithm using the optimal
fitness function, which aims to extend network lifetime. The parameters used in this algorithm are residual
energy density, the distance from the base station, intra-cluster distance from the cluster head. Simulation
results show that the proposed method is more effective compared to protocols such as (LEACH, CHEF,
PSO-MV) in terms of network lifetime and energy consumption.
Fault Diagonosis Approach for WSN using Normal Bias TechniqueIDES Editor
In wireless sensor and actor networks (WSAN), the
sensor nodes have a limitation on lifetime as they are equipped
with non-chargeable batteries. The failure probability of the
sensor node is influenced by factors like electrical dynamism,
hardware disasters, communication inaccuracy and undesired
environment situations, etc. Thus, fault tolerant is a very
important and critical factor in such networks. Fault tolerance
also ensures that a system is available for use without any
interruption in the presence of faults. In this paper an
improved fault tolerance scheme is proposed to find the
probability of correctly identifying a faulty node for three
different types of faults based on normal bias. The nodes fault
status is declared based on its confidence score that depends
on the threshold valve. The aim is to find the Correct
Recognition Rate (CRR) and the False Fear Rate (FFR) with
respect to the different error probability (pe) introduced. The
techniques, neighboring nodes, fault calculations, range and
CRR for existing algorithm and proposed algorithm is also
presented.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
1. SWITCH GEAR AND PROTECTION
STUDY REPORT ON
ADAPTIVE RELAYING
Submitted in partial fulfillment of the requirements for the award of the degree of
Bachelor of Technology
In
Electrical Engineering.
Under the guidance of
Dr.Ashok S
By
NAME ROLL NO.
SURABHI VASUDEV (B110556EE)
Department of Electrical Engineering
NATIONAL INSTITUTE OF TECHNOLOGY CALICUT
DECEMBER 2014
2. ABSTRACT
A reliable, continuous supply of electrical energy is essential for the functioning
of today's modern complex and advanced society. Electricity is one of the prime
factors for the growth and determines the value of the society.
Conventional Power System analysis become difficult due to:
1. Complex versatile and large amounts of data that are used in calculation,
diagnosis and learning.
2. The increase in the computational time period and the accuracy due to
extensive system data handling.
The modern power system operates close to their limits due to the increasing
energy consumption and impediments of various kinds, and the extension of
existing electric transmission networks. This situation requires a significantly less
conservative power system operation and control regime which, in turn, is
possible only by monitoring the system states in much more detail than was
necessary previously.
Sophisticated computer tools have become predominant in solving the
difficultproblems that arise in the areas of Power System planning, operation,
diagnosis and design of the systems. Among these computer tools Artificial
Intelligence has grown extensively in recent years and has been applied in the
areas of the power systems. The most widely used and important ones of Artificial
Intelligent tools, applied in the field of Electrical Power Systems are the Artificial
Neural networks and the so-called Fuzzy systems. The details of the important
applications are discussed. Finally the major achievements of this soft computing
technique in power system areas are commented and the future scopes of these
methods in the modern power system are analyzed.
3. CONTENTS
CHAPTER TITLE PAGE NO.
ABSTRACT
1 INTRODUCTION 1
2 ARTIFICIAL INTELLIGENCE METHODS 2
3 ANALYSIS OF THE TECHNIQUES 4
4 APPLICATIONS 9
5 CONCLUSION 14
REFERENCES 15
4. 1. 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 . Practically, it includes the artificial neural network approach
(pattern recognition), as well as the expert system and fuzzy logic methods
(decision making).
Thus three major families of AI techniques are considered to be applied in
modern power system protection :
• Expert System Techniques (XPSs),
• Artificial Neural Networks (ANNs),
• Fuzzy Logic systems (FL).
5. 2.ARTIFICIAL INTELLIGENCE METHODS
AI is a subfield of computer science that investigates how the thought and action
of human beings can be mimicked by machines . 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.
A. Expert Systems
The first expert systems included a few heuristic rules based on the expert's
experience. In such systems, the knowledge takes the form of so called
production rules written using the If... then... syntax (knowledge base). The
system includes also the facts which generally describe the domain and the state
of the problem to be solved (data base).A generic inference engine uses the facts
and the rules to deduce new facts which allow the firing of other rules. The
knowledge base is a collection of domain-specific knowledge and the inference
system is the logic component for processing the knowledge base to solve the
problem. This process continues until the base of facts is saturated and a
conclusion has been reached .To guide the reasoning and to be more efficient,
these systems may incorporate some strategies known as meta knowledge. Rule
based systems represent still the majority of the existing expert systems. There
are few applications of XPS to power system protection reported, but all of them
solve the off-line tasks such as settings coordination, post-fault analysis and fault
diagnosis . As yet there is no application reported of the XPS technique employed
as a decision making tool in an on-line operating protective relay. The basic
reason for this is that there is no extensive rule base that describes the reasoning
process applicable to protective relaying. Instead, only a few rules or criteria are
collected .
6. B. Artificial Neural Networks
The ANNs are very different from expert systems since they do not need a
knowledge base to work. Instead, they have to be trained with numerous actual
cases. An ANN is a set of elementary neurons which are connected together in
different architectures organized in layers what is biologically inspired .An
elementary neuron can be seen like a processor which makes a simple non linear
operation of its inputs producing its single output. A weight (synapse) is attached
to each neuron and the training enables adjusting of different weights according
to the training set. The ANN techniques are attractive because they do not
require tedious knowledge acquisition, representation and writing stages and,
therefore, can be successfully applied for tasks not fully described in advance. The
ANN are not programmed or supported by a knowledge base as are Expert
Systems. Instead they learn a response based on given inputs and a required
output by adjusting the node weights and biases accordingly.The speed of
processing, allowing real time applications,is also an advantage.
C. Fuzzy Logic
The fuzzy logic approach to protective relaying assumes :
• The criteria signals are fuzzified in order to account for dynamic errors of the
measuring algorithms. Thus, instead of real numbers, the signals are represented
by fuzzy numbers. Since the fuzzification process provides a special kind of flexible
filtering, faster measuring algorithms that speed up the relays may be used.
• The thresholds for the criteria signals are also represented by fuzzy numbers to
account for the lack of precision in dividing the space of the criteria signals
between the tripping and blocking regions.
• The fuzzy signals are compared with the fuzzy settings.The comparison result is
a fuzzy logic variable between the Boolean absolute levels of truth and false.
• The tripping decision depends on multi-criteria evaluation of the status of a
protected element. Additional decision factors may include the amount of
available information,or the expected costs of relay maloperation.
7. 3.ANALYSIS OF THE TECHNIQUES
3.1 Neural Network based Applications
The most of the applications related to neural network is based on multilayer
perceptron. Here the error back scheme is widely used. Fundamental aspects of
Multilayer Perceptron networks are random initial start up state and convergence
of connection weights to produce minimum error. However there are no set rules
for parameter selection associated with these algorithms. So in using ANN models
some trial and error is required.
3.1.1 Design of Network
As discussed in practical applications Multilayer Perceptron with at least one
hidden layer is used. It has been reported that using greater number of hidden
layer improve the overall performance. But some experimentation is required to
select the number of hidden layers and nodes. Generally at least twice of as many
nodes in the hidden layer has been taken as Inputs.
Some of the researchers gave an empirical formula as H = ni (ni-1) to calculate
hidden layer where 'H' is the number of the hidden layer and 'n i' the input. But
still some trial and error is needed to produce quick convergence and acceptable
results.
The introduction of the concept of structured ANNs (e.g.Perceptrons, Hopfield
Network, and SOM) designed for specific tasks simplify the design process. Also
research results are available for dynamically designs hidden layers. Cascaded
correlation's begins with minimal network, then automatically trains and adds
new hidden units one by one. Once the hidden layer is added it becomes a
permanent feature detector in ANN. This architecture learns quickly.
8. 3.1.2 Training Set Generation
In many applications, there is no efficient way of generating a complete training
set to cover all possible operating states. This will be of greater concern in dealing
with a problem of large on line data handling. For example, In the cases of power
system security problem most of the literatures reports about offline simulation
to obtaining the training sets. It is possible to analyze if the samples chosen are
small in size. If the sample is large (500 buses, which are the case of the practical
system,) the analysis will be extremely difficult. Moreover its not easy to obtain
good performance on training data followed by much worse performance on test
data. There can be improvement if some knowledge can be incorporated about
the domain into the network architecture.
3.1.3 Hopfield Network
Hopfield Networks can be very useful in solving the optimization problems very
quickly and efficiently by minimizing energy function, defined in terms of its
weights and thresholds. However, this energy function has many local minima.
This is not acceptable especially in contingency screening. The reason is that we
should get the best rather than the feasible ranking of contingencies. Another
drawback is that the weights and thresholds are calculated based on the
optimization process, which has to be repeated if any of the input parameters
change.The enhancement in the recent development of the architecture reduces
thesedrawbacks. Also a mapping method is formulated from which the weights
and thresholds forthe particular optimization problem can be easily computed.
3.1.4 Training the Inputs
Many of the ANN models (like perceptron, SOM, ART Networks heavily rely on the
information retained to the input features. In any power system applications the
input patterns space consists of a large number of features. So feature selection is
necessary to reduce this pattern space to a reasonable size. These processes
make loss of information.
9. 3.1.5 Knowledge Consistency and Interaction with the User
Knowledge Consistency is an important concern in the training set of ANN
research.The AI implementations are considered complete when they match with
human competence and thus further research is needed in this area.
In many cases AI technique is required to interact to demonstrate the validity of
the decision to the User. For example in the diagnosis of faults in the system, the
operator might want to ascertain the validity of the reasoning employed. Similarly
in preventive control an explanation might be necessary to validate and verify the
control strategy.
3.1.6 Practical Implementation
In the hardware part most of the present day ANN schemes are single-processor
simulations of the massively parallel ANN models. When using the multilayer
perceptron model, most of the implementations use a sequential algorithm on
conventional computer to train the ANN, in node by node manner. Ideally ANN
schemes should be implemented in parallel processing machines to fully reap the
benefits of their massively parallel structure. There is mainly two way of
implementation of ANN in the parallel computers.
1. Direct Implementation in which there is a physical-processing element for each
neuron in the neural network. This approach can potentially provide a very good
performance.However it can support only a specific ANN model since it is fixed in
the hardware.
2. Virtual implementations (with general-purpose neuro computer) in which a
processingelement takes charge of multiple neurons and simulates them in a
time-sharing fashion.
3.2 Fuzzy Logic
3.2.1Requirements of Fuzzy based Applications
The main characteristics and requirement for a problem suitable for fuzzy logic
applications are
10. 1. The problem has to be solved by human experts for daily operation and
planning. Thusfunctional knowledge in terms of heuristic rules are available.
2. If the methodology cannot be expressed in terms of mathematical form.
3. If the modeling of mathematical problem requires various many assumptions to
be made,leading to an inaccurate models.
4. If the problem involves uncertainty, vague constraints and/or multiple
conflicting objectives.
3.2.2 Advantages of Fuzzy Logic Applications
The main advantages of the fuzzy systems are
1. Speed
2. Computationally less expensive and simpler tools.
3. Flexibility
4. Ease of computation
Creation of fuzzy logic
Creation of fuzzy logic is mostly through experts, which lacks in knowledge
engineering.That means it depends on expert opinion and cannot decide the rule
networks Genetic Algorithms and fuzzy clusters.
Common sense knowledge Representation
It’s difficult to represent and manipulate common sense knowledge and there are
no effective and sufficient methods to do so.
Fuzzy Logic Controller Stability
Stability of the FLC cannot be assessed and there are no established methods to
do that. This needs to be analyzed before they can be considered as alternative
for conventional controller.
11. Fuzzy inference allows to approximate nonlinear functions with finite fuzzy
rules.The main advantage of a rule-based system over the neural network is to
capture cause and effect in the inference process. Each subspace is described by a
fuzzy if-then rule based on the patterns of training set as shown in fig.3.2.1 in the
application of transformer fault diagnosis.
Fig.3.2.1.fuzzy subspaces with membership functions
12. 4.APPLICATIONS
4.1.Transformer Differential Relaying
Conventional differential relays may fail in discriminating between internal faults
and other conditions (inrush current, over-excitation of core, CT saturation, CT
ratio mismatch, external faults,..).Detection of 2nd
and 5th
harmonics is not
sufficient (harmonics may be generated during internal faults)by ordinary relays.
Multi-Criteria Differential Relay based on Self-Organizing Fuzzy Logic is used.
One differential relay per phase.
12 criteria are used and integrated by FL.
Examples of criteria: (ID=differential current)
4.2.Distance Relaying
Changing the fault condition, particularly in the presence of DC offset in current
waveform, as well as network changes lead to problems of underreach or
overreach.Conventional schemes suffer from their slow response. Using ANN
schemes with samples of V&I measured locally, while training ANN with faults
inside and outside the protection zone.Same approach but after pre-processing to
get fundamental of V&I through half cycle DFT filter.Combining conventional with
AI: using ANN to estimate line impedance based on V&I samples so as to improve
the speed of differential equation based algorithm.
Pattern Recognition is used to establish the operating characteristics of zone-I.
The impedance plane is partitioned into 2 parts: normal and fault. Pre-classified
records are used for training.Application of adaptive distance relay using
ANN,where the tripping impedance is adapted under varying operating
conditions. Local measurements of V&I are used to estimate the power system
condition.
13. 4.3.Transmission Line Fault Classification
Conventional schemes: cannot adapt to changing operating conditions, affected
by noise& depend on DSP methods (at least 1-cycle).Single-pole
tripping/autorecloser SPAR requires the knowledge of faulted phase (on detecting
SLG Single-pole tripping is initiated, on detecting arcing fault recloser is initiated).
The adaptiveness is ,hence,incorporated as in fig.4.3.1.The ANN topology and the
relaying scheme are shown in fig.4.3.2 and fig.4.3.3 respectively.
Fig.4.3.1.AI based transmission line
15. 4.4.Machine Winding Protection
If the generator is grounded by high impedance, detection of ground faults is not
easy (fault current < relay setting).Conventional algorithms suffer from poor
reliability and low speed (1-cycle). So,adaptive relay is made as per the algorithm
in fig.4.4.1.
Fig.4.4.1
4.5.Fault Diagnosis
16. ANN’s has recently invaded fault diagnosis, which has been a traditional area for
ES(expert system) implementation. However, at present the ES implementations
outnumber the ANN implementations. The explanatory abilities of ESs and their
more powerful user interface make them a more attractive alternative. However,
still there are certain areas, which require a quick response, and are still open to
ANN implementation. Many applications for the various fault diagnosis problems
have been reported in the literature. Kanoh et al [HMK88] proposed a cascade
structure of three three-layer perceptron networks for the identification of a
faulted transmission section. The ANNs were trained using backpropagation.The
first and the second ANN in the cascade structure identify the candidate’s one
and two for fault selection, using current amplitude and phase angle distribution
patterns.
The third ANN obtains the final fault location using the above candidates one and
two, and acurrent amplitude distribution pattern. Results of this approach
indicates that this method canachieve 98.4 percentage accuracy even when the
measured values differed by thirtypercentage from the EMTP .
C.Rodriguez at el [RRMLMP 96] presented a modular and neural network-based
solution to power systems alarm handling and fault diagnosis described it
overcomes the limitations of ‘toy’ alternatives constrained to small and fixed-
topology electrical networks. In contrast with the monolithically diagnosis
systems, the neural network-based approach presented here fulfills the scalability
and dynamic adaptability requirements of the application.
Mapping the power grid onto a set of interconnected modules that model the
functional behavior of electrical equipment provides the flexibility and speed
demanded by the problem. The way in which the neural system is conceived
allows full scalability to realsize power systems.
17. 5.CONCLUSION
The importance of the use of the AI tools has been felt in all the areas of the
Power System Relaying and the need is emphasized. The easiness in evaluating
the vague or non-crisp concepts and the ability of these techniques to learn due
to the technological improvement elevated the effect of these soft computing
techniques.
The study presents concepts, survey and the important analysis of typical
applications of AI techniques (ANN and FUZZY LOGIC) in the field of Power
systems. The fundamentals of the Artificial Neural Network and the Fuzzy Systems
are also described. The analysis of these techniques is indicated in a broader
sense and the practical difficulties are narrated. Also the future concentration on
the modification of the techniques is analyzed to obtain better result and making
these techniques competitive to the human brains.
As in the case of Fuzzy Logic applications it can be seen that these techniques can
be blended with the conventional systems as well as with the other techniques
like Neural Networks and Genetic Algorithms. The hybrid systems thus formed
can be the most powerful systems for design, planning and control & Operation of
practical problems.
Hybrid Systems combining the individual strengths of the ESs and ANNs along
with the Fuzzy systems seems to be the most promising area in future and
promising for the most of the Power system Applications.Moreover there are
sufficient scope in the improvement of the various soft-computing techniques to
increase their strengths and capability. The tools for the simulation of these
conditions also need to be enhanced for their limitations. The application fields
combining the conventional and these techniques can remarkably reduce the
difficulties faced in the Power Systems design, operation and control.
18. REFERENCES
Artificial Intelligence Techniques in Power Systems by K. Warwick, Arthur
Ekwue, Raj Aggarwal, Institution of Electrical Engineers.
http://web.stanford.edu/class/cs227/Lectures/lec01.pdf
Computational Intelligence Systems and Applications: Neuro-Fuzzy and
Fuzzy logic By Marian B. Gorzalczany