The document discusses using an artificial neural network (ANN) approach for fault diagnosis in power systems. It provides background on power system faults, protective systems, and artificial intelligence techniques. The key aspects covered are:
1) An auto-configuring radial basis function network (RBFN) type of ANN is proposed for fault diagnosis. RBFN can identify faults faster and more reliably than other methods.
2) A sample power system is modeled and different fault scenarios are used to generate training data for the RBFN.
3) The RBFN is trained and tested on the data to demonstrate its ability to accurately diagnose faults in the power system.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and consist of interconnected artificial neurons that process information. The document describes common ANN architectures like multilayer perceptrons and radial basis function networks. It also summarizes different ANN learning paradigms such as supervised, unsupervised, and reinforcement learning. Specific learning rules and algorithms are mentioned, including the perceptron rule, Hebbian learning, competitive learning, and backpropagation. Applications of ANNs discussed include pattern recognition, clustering, prediction, and data compression.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
This document outlines a project to develop standards for defining functional brain networks using spatial and temporal features extracted from fMRI data. The goal is to distinguish true neural networks from noise and to classify neuropsychiatric disorders based on patterns of functional networks. Over 1,500 independent components were analyzed to identify 124 features that can predict whether a component represents a true network or noise, with 86.75% accuracy. Further work will focus on defining subnetworks within main networks and investigating patterns of subnetwork activity to distinguish disorders. Developing standards for networks will allow automated network labeling and filtering of fMRI data to advance diagnosis and subtyping of neurological disorders.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
VLSI for neural networks and their applications was presented. Biological neural networks refer to networks of biological neurons that perform physiological functions. Artificial neural networks are mathematical models inspired by biological neural networks. Neural networks can be digital, analog, or hybrid and have applications in areas like pattern and speech recognition, economy, sociology, and basic sciences like investigating the impact of treatments over time. In conclusion, artificial neural networks that simulate human biological neurons have potential for wide implementation and can be trained on input data and then apply that knowledge to new cases.
Early detection of adult valve disease mitral stenosis using the elman artifi...iaemedu
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. An ENN was trained on M-mode echocardiography images showing mild, moderate or severe stenosis. The ENN demonstrated good performance classifying images into the three categories. Feature extraction was performed using kernel principal component analysis to reduce image pixels to three values as inputs to the ENN. The ENN architecture included input, hidden, connecting and output layers to classify dynamic patterns over time in the ultrasound images.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and consist of interconnected artificial neurons that process information. The document describes common ANN architectures like multilayer perceptrons and radial basis function networks. It also summarizes different ANN learning paradigms such as supervised, unsupervised, and reinforcement learning. Specific learning rules and algorithms are mentioned, including the perceptron rule, Hebbian learning, competitive learning, and backpropagation. Applications of ANNs discussed include pattern recognition, clustering, prediction, and data compression.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
This document outlines a project to develop standards for defining functional brain networks using spatial and temporal features extracted from fMRI data. The goal is to distinguish true neural networks from noise and to classify neuropsychiatric disorders based on patterns of functional networks. Over 1,500 independent components were analyzed to identify 124 features that can predict whether a component represents a true network or noise, with 86.75% accuracy. Further work will focus on defining subnetworks within main networks and investigating patterns of subnetwork activity to distinguish disorders. Developing standards for networks will allow automated network labeling and filtering of fMRI data to advance diagnosis and subtyping of neurological disorders.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
VLSI for neural networks and their applications was presented. Biological neural networks refer to networks of biological neurons that perform physiological functions. Artificial neural networks are mathematical models inspired by biological neural networks. Neural networks can be digital, analog, or hybrid and have applications in areas like pattern and speech recognition, economy, sociology, and basic sciences like investigating the impact of treatments over time. In conclusion, artificial neural networks that simulate human biological neurons have potential for wide implementation and can be trained on input data and then apply that knowledge to new cases.
Early detection of adult valve disease mitral stenosis using the elman artifi...iaemedu
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. An ENN was trained on M-mode echocardiography images showing mild, moderate or severe stenosis. The ENN demonstrated good performance classifying images into the three categories. Feature extraction was performed using kernel principal component analysis to reduce image pixels to three values as inputs to the ENN. The ENN architecture included input, hidden, connecting and output layers to classify dynamic patterns over time in the ultrasound images.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
This document provides an overview of various deep learning techniques including recurrent neural networks, convolutional neural networks, the universal approximation theorem, and generative adversarial networks. It describes what each technique is used for as well as key aspects of how they work, such as RNNs using sequential data and CNNs being applied to visual imagery. The document also discusses regularization techniques used in CNNs and implications of the universal approximation theorem.
This document discusses biological neurons, artificial neurons, and cellular neural networks (CNNs). It provides an overview of CNNs, including their history, architecture, applications, advantages, and future scope. CNNs were proposed to reduce the number of interconnections between neurons in neural networks by only connecting neurons within a local neighborhood. A CNN is an array of dynamical systems with local connections only. Each cell in the CNN interacts with neighboring cells.
Neural networks are computational models inspired by the human brain. They are constructed of interconnected nodes that mimic biological neurons. Neural networks are trained on large amounts of data to detect patterns and predict outcomes. There are several types of neural networks, including feedforward, convolutional, and recurrent networks. Neural networks have various applications in bioinformatics such as gene identification, protein structure prediction, and sequence analysis. Their advantages include the ability to model complex nonlinear relationships and generalize to new data.
1) A wearable brain cap is presented that can measure EEG signals without requiring electrical contact with the head using integrated contactless electrodes.
2) The cap is made of flexible polymeric material and the contactless electrodes may be obtained using a new electroactive gel that can read the EEG signals.
3) This cap aims to overcome the discomfort of typical EEG caps and electrodes that require electrolytic gel and time-consuming attachment by providing a fully wearable and portable system for brain-computer interface applications.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
IRJET- The Essentials of Neural Networks and their ApplicationsIRJET Journal
This document discusses neural networks and their applications. It begins by explaining the limitations of traditional computers in complex tasks and the need for a more human-like approach using neural networks. The basics of neural network architecture are then described, including the key components of neurons and synapses that operate in parallel like the human brain. Different types of neural networks are classified, including convolutional neural networks for image data. The document concludes by highlighting the wide range of commercial applications for neural networks in areas like data analysis, forecasting, and military operations.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Neural Networks for Pattern RecognitionVipra Singh
- Neural networks are computing systems inspired by biological neural networks in the brain that can be used for pattern recognition. An artificial neuron receives multiple inputs and produces a single output. Neural networks are trained to recognize complex patterns and identify categories.
- An important application of neural networks is pattern recognition, where a network is trained to associate input patterns with output categories. Recent advances include using neural networks for tasks like predicting student performance, medical diagnosis, and analyzing customer interactions. Neural networks are also being used increasingly in business for applications like predictive analytics and artificial intelligence.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
The document discusses artificial neural networks (ANNs). It defines ANNs as computational models inspired by biological neural networks. The basic structure and types of ANNs are explained, including feed forward and feedback networks. The document also covers ANN learning methods like supervised, unsupervised, and reinforcement learning. Applications of ANNs span various domains like aerospace, automotive, military, electronics, and more. While ANNs can perform complex tasks, they require extensive training and processing power for large networks.
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.
Artificial intelligence in power systems seminar presentationMATHEW JOSEPH
The document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides examples of how each technique can help with tasks like fault detection, improving transmission line performance by adjusting parameters, and automated decision making. Current applications of AI in power systems include operations, planning, control, market strategies, and automation of various functions to improve reliability and reduce costs. Further research is still needed to fully leverage AI across all aspects of modern power systems.
Early detection of adult valve disease mitral stenosisIAEME Publication
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. The ENN is trained on M-mode echocardiography images labeled as mild, moderate, or severe stenosis. Kernel principal component analysis is used to extract features from the images by reducing the pixel dimensions. The ENN architecture includes input, hidden, connecting, and output layers to classify new images based on the training. The algorithm involves calculating network outputs and errors to update weights between layers and improve classification accuracy. In summary, the ENN is designed and tested to automatically detect and diagnose the severity of mitral valve stenosis from ultrasound images.
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. The ENN is trained on M-mode echocardiography images labeled as mild, moderate, or severe stenosis. Kernel principal component analysis is used to extract features from the images by reducing the pixel dimensions. The ENN architecture includes input, hidden, connecting, and output layers to classify new images based on the training. The algorithm updates the weights between layers using error backpropagation. The goal is an automated system that can diagnose mitral valve stenosis from echocardiograms.
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 document discusses the use of artificial neural networks (ANNs) for process control applications. It covers several key topics:
1) ANNs can model nonlinear systems through parallel processing and learning algorithms like backpropagation. Multi-layer neural networks are commonly used for pattern recognition and control.
2) Various ANN-based control configurations are described, including direct inverse control, direct adaptive control, and internal model control.
3) Learning algorithms like backpropagation and applications like system identification, modeling, fault detection, and temperature control are discussed.
4) The document concludes that multi-layer neural networks trained with backpropagation are well-suited for process identification and control, as they can handle nonlinearity using
This document provides an overview of various deep learning techniques including recurrent neural networks, convolutional neural networks, the universal approximation theorem, and generative adversarial networks. It describes what each technique is used for as well as key aspects of how they work, such as RNNs using sequential data and CNNs being applied to visual imagery. The document also discusses regularization techniques used in CNNs and implications of the universal approximation theorem.
This document discusses biological neurons, artificial neurons, and cellular neural networks (CNNs). It provides an overview of CNNs, including their history, architecture, applications, advantages, and future scope. CNNs were proposed to reduce the number of interconnections between neurons in neural networks by only connecting neurons within a local neighborhood. A CNN is an array of dynamical systems with local connections only. Each cell in the CNN interacts with neighboring cells.
Neural networks are computational models inspired by the human brain. They are constructed of interconnected nodes that mimic biological neurons. Neural networks are trained on large amounts of data to detect patterns and predict outcomes. There are several types of neural networks, including feedforward, convolutional, and recurrent networks. Neural networks have various applications in bioinformatics such as gene identification, protein structure prediction, and sequence analysis. Their advantages include the ability to model complex nonlinear relationships and generalize to new data.
1) A wearable brain cap is presented that can measure EEG signals without requiring electrical contact with the head using integrated contactless electrodes.
2) The cap is made of flexible polymeric material and the contactless electrodes may be obtained using a new electroactive gel that can read the EEG signals.
3) This cap aims to overcome the discomfort of typical EEG caps and electrodes that require electrolytic gel and time-consuming attachment by providing a fully wearable and portable system for brain-computer interface applications.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
IRJET- The Essentials of Neural Networks and their ApplicationsIRJET Journal
This document discusses neural networks and their applications. It begins by explaining the limitations of traditional computers in complex tasks and the need for a more human-like approach using neural networks. The basics of neural network architecture are then described, including the key components of neurons and synapses that operate in parallel like the human brain. Different types of neural networks are classified, including convolutional neural networks for image data. The document concludes by highlighting the wide range of commercial applications for neural networks in areas like data analysis, forecasting, and military operations.
Analysis of Neocognitron of Neural Network Method in the String RecognitionIDES Editor
This paper aims that analysing neural network method
in pattern recognition. A neural network is a processing device,
whose design was inspired by the design and functioning of
human brain and their components. The proposed solutions
focus on applying Neocognitron Algorithm model for pattern
recognition. The primary function of which is to retrieve in a
pattern stored in memory, when an incomplete or noisy version
of that pattern is presented. An associative memory is a
storehouse of associated patterns that are encoded in some
form. In auto-association, an input pattern is associated with
itself and the states of input and output units coincide. When
the storehouse is incited with a given distorted or partial
pattern, the associated pattern pair stored in its perfect form
is recalled. Pattern recognition techniques are associated a
symbolic identity with the image of the pattern. This problem
of replication of patterns by machines (computers) involves
the machine printed patterns. There is no idle memory
containing data and programmed, but each neuron is
programmed and continuously active.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
This document contains 40 questions about soft computing concepts including neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems. The questions cover topics such as the differences between hard and soft computing, components of expert systems, applications of artificial neural networks, types of learning in neural networks, perceptrons, adaptive linear neurons, backpropagation networks, and training algorithms for various neural network architectures.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Neural Networks for Pattern RecognitionVipra Singh
- Neural networks are computing systems inspired by biological neural networks in the brain that can be used for pattern recognition. An artificial neuron receives multiple inputs and produces a single output. Neural networks are trained to recognize complex patterns and identify categories.
- An important application of neural networks is pattern recognition, where a network is trained to associate input patterns with output categories. Recent advances include using neural networks for tasks like predicting student performance, medical diagnosis, and analyzing customer interactions. Neural networks are also being used increasingly in business for applications like predictive analytics and artificial intelligence.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
The document discusses artificial neural networks (ANNs). It defines ANNs as computational models inspired by biological neural networks. The basic structure and types of ANNs are explained, including feed forward and feedback networks. The document also covers ANN learning methods like supervised, unsupervised, and reinforcement learning. Applications of ANNs span various domains like aerospace, automotive, military, electronics, and more. While ANNs can perform complex tasks, they require extensive training and processing power for large networks.
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.
Artificial intelligence in power systems seminar presentationMATHEW JOSEPH
The document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides examples of how each technique can help with tasks like fault detection, improving transmission line performance by adjusting parameters, and automated decision making. Current applications of AI in power systems include operations, planning, control, market strategies, and automation of various functions to improve reliability and reduce costs. Further research is still needed to fully leverage AI across all aspects of modern power systems.
Early detection of adult valve disease mitral stenosisIAEME Publication
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. The ENN is trained on M-mode echocardiography images labeled as mild, moderate, or severe stenosis. Kernel principal component analysis is used to extract features from the images by reducing the pixel dimensions. The ENN architecture includes input, hidden, connecting, and output layers to classify new images based on the training. The algorithm involves calculating network outputs and errors to update weights between layers and improve classification accuracy. In summary, the ENN is designed and tested to automatically detect and diagnose the severity of mitral valve stenosis from ultrasound images.
The document discusses using an Elman artificial neural network (ENN) to classify the degree of mitral valve stenosis in ultrasound images. The ENN is trained on M-mode echocardiography images labeled as mild, moderate, or severe stenosis. Kernel principal component analysis is used to extract features from the images by reducing the pixel dimensions. The ENN architecture includes input, hidden, connecting, and output layers to classify new images based on the training. The algorithm updates the weights between layers using error backpropagation. The goal is an automated system that can diagnose mitral valve stenosis from echocardiograms.
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 document discusses the use of artificial neural networks (ANNs) for process control applications. It covers several key topics:
1) ANNs can model nonlinear systems through parallel processing and learning algorithms like backpropagation. Multi-layer neural networks are commonly used for pattern recognition and control.
2) Various ANN-based control configurations are described, including direct inverse control, direct adaptive control, and internal model control.
3) Learning algorithms like backpropagation and applications like system identification, modeling, fault detection, and temperature control are discussed.
4) The document concludes that multi-layer neural networks trained with backpropagation are well-suited for process identification and control, as they can handle nonlinearity using
IRJET- Three Phase Line Fault Detection using Artificial Neural NetworkIRJET Journal
This document describes a study that uses an artificial neural network to detect and classify faults on electric power transmission lines. The researchers modeled a three-phase transmission line system in MATLAB/Simulink and simulated different types of faults at various locations and resistances. Voltage and current data from the simulations were extracted and preprocessed as inputs to train an artificial neural network. The trained network was then able to detect and classify faults with 95.7% accuracy, demonstrating its effectiveness. Previous methods had issues with stability and slow dynamic response, but the artificial neural network approach provided improved fault detection performance.
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
Artificial neural network are the mathematical inventions motivated by observation made in study of biological system, through loosely founded on the actual biology. An artificial neural network can be defined as mapping an input space to output space. This concept is analogous to that of mathematical function. The purpose of neural network is to map an input into desired output. Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted that means that every input value is multiplied with individual weight.
Artificial neural networks are a form of artificial intelligence inspired by biological neural networks. They are composed of interconnected processing units that can learn patterns from data through training. Neural networks are well-suited for tasks like pattern recognition, classification, and prediction. They learn by example without being explicitly programmed, similarly to how the human brain learns.
This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.
This document introduces artificial neural networks and their relationship to biological neural networks. It discusses the basic components and functioning of artificial neural networks, including nodes, links, weights, and learning. Different network architectures are described, including single layer feedforward networks and multilayer feedforward networks. Supervised, unsupervised, and reinforced learning methods are also summarized. Applications of artificial neural networks include areas like airline security, investment management, and sales forecasting.
This document provides an overview of artificial neural networks. It describes the biological neuron model that inspired artificial networks, with dendrites receiving inputs, the soma processing them, the axon transmitting outputs, and synapses connecting neurons. An artificial neuron model is presented that uses weighted inputs, a summation function, and an activation function to generate outputs. The document discusses unsupervised and supervised learning methods, and lists applications such as character recognition, stock prediction, and medicine. Advantages include human-like thinking and handling noisy data, while disadvantages include the need for training and high processing times.
The document discusses artificial neural networks and electronic noses. It describes how electronic noses use sensor arrays and neural networks to identify chemicals and odors. A prototype electronic nose is presented that uses 9 tin oxide sensors and neural networks to identify common household chemicals. Applications discussed include using electronic noses for medical diagnosis by analyzing odors from the body.
Neural networks are algorithms that mimic the human brain in recognizing patterns in vast amounts of data. They can adapt to new inputs without redesign. Neural networks can be biological, composed of real neurons, or artificial, for solving AI problems. Artificial neural networks consist of processing units like neurons that learn from inputs to produce outputs. They are used for applications like classification, pattern recognition, optimization, and more.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
This document discusses the application of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides an overview of power systems and artificial intelligence. It then discusses the need for AI in power systems due to complex data handling. The major AI techniques considered for power system protection are expert systems, artificial neural networks, and fuzzy logic systems. Case studies on fault detection in transmission lines using fuzzy systems and improving line performance using expert systems and neural networks are also presented. The conclusion states that AI can help improve power system efficiency, analysis, control, and use of renewable resources.
An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available.
This document discusses the use of artificial intelligence techniques in electrical engineering, specifically in power systems. It introduces artificial intelligence and describes power systems. It explains the need for AI in electrical engineering due to complex systems and large amounts of data. The main AI techniques discussed are artificial neural networks, fuzzy logic systems, and expert systems. It provides details on each technique including advantages and disadvantages. It then discusses practical applications of these AI techniques in transmission lines and power system protection.
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Auto Configuring Artificial Neural Paper Presentation
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AUTOCONFIGURING ARTIFICIAL NEURAL
NETWORK APPLIED TO FAULT DIAGNOSIS
IN POWER SYSTEMS
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INTRODUCTION:
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The fault diagnosis of a power system provides an effective means to get
information about system restoration and maintenance of the power system.
Artificial intelligence has been successfully implemented on fault diagnosis
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and system monitoring. Expert systems are used by defining rules, for a fault
diagnosis. In the present work particularly a new method of “AI” namely
“Artificial Neural Network” is used as diagnosing to power system faults.
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A study has been made by taking a sample models of power systems. The all
possible faults of the system were diagnosed and predicted with the help of
“Auto-Configuring Artificial Neural Network” namely “Radial Basis Function
Network” and the comprehensive study reveals that the proposed method is
more efficient, faster and reliable than any other method used for fault
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diagnosis of power systems.
DEFINITION:
Artificial intelligence (AI) is simply the way of making the computer think
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intelligently. It there by provides a simple, structured approach to designing
complex decision-making programs. While designing an AI system, the goal
of the system must be kept in mind. There exists a more sophisticated system;
which guides the selection of a proper response to a specific situation. This
process is known as “Pruning”, as its name suggests eliminates path way of
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thoughts that are not relevant to the immediate objective of reaching a goal.
AI has made a significant impact on power system research. Power system
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engineers have applied successfully AI methods to power system research
problems like energy control, alarm processing, fault diagnosis, system
restoration, voltage/var control, etc. for the last couple of years a new AI
method namely Artificial Neural Network (ANN) has been used extensively in
power system research. In comparison to the AI method, which tries to mimic
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mental process that takes place in human reasoning, ANN on the other hand
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tries to stimulate the neural activity that takes place in the human brain. ANN
has been successfully applied to economic load dispatch, shot term load
forecasting, security analysis, alarm processing, capacitor installation and
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EMTP problems. An attempt has been made here to solve the fault diagnosis
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problem in power systems using ANN.
The principal functions of these diagnosis systems are:
1) Detection of fault occurrence
2) Identification of faulted sections
3) Classification of faults into types:
HIFs (high impedance faults) or LIFs(low impedance faults)
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This has been achieved through a cascade, multilayered ANN structure. Using
these FDS accurately identifies HIFs, which are relatively difficult to identify
in the other methods.
FAULT ANALYSIS AND PROTECTIVE SYSTEM
A fault in electrical equipment is defined as a defect in its electrical circuit due
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to which the current is diverted from the intended path. Breaking of
conductors or failure generally causes fault. The other causes of fault include
mechanical failure, accidents, excessive internal and external stress the faults
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can be minimized by inputting the system, design, quality of equipment and
maintenance Voltage and current unbalanced, Over voltage, Under frequency,
Reversal of power, Power swings, Instability. However the faults can be
eliminated completely.
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For the purpose of analysis the faults can be classified as
1) Single line to ground fault
2) Line to line fault
3) Double line ground fault
4) Simultaneous fault
5) Three phase fault
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6) Open circuit fault etc
Some of the abnormal conditions are not serious enough to call for tripping of
the circuit breaker. In such cases the protection relaying is arranged for giving
an alarm where as in other cases it is harmful in such cases the fault should be
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disconnected immediately without any delay. This function is performed by
protective relaying and switch gear.
FAULT CALCULATION:
The knowledge of the fault current is necessary for selecting the circuit
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breakers of adequate rating, designing the sub –station equipment, determining
the relay setting, etc. The fault calculation provides the information about the
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fault currents and the voltages at various points of the power system under
different fault conditions. The per. Unit (p.u) system normally used for fault
calculations
The symmetrical faults such as three phase faults are analyzed on per phase
basis the unsymmetrical fault is calculated by the method of symmetrical
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components
Network analyzer and digital computers used for fault calculation for large
systems
ARTIFICIAL INTELLIGENCE APPLIED TO FAULT DIAGONISIS
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AND POWER SYSTEM RESTORATIONS:
AI is simply a way of making a computer think intelligently this
accomplished by studying how people think when they are trying to make
decisions and solve problems, breaking these thought processes down into
basic steps and designing a computer program that solves problems using
those some steps .AI thereby provides a simple , structured approach to design
complex decision making programs, human intelligence is of complex
function that scientists have only began to understand, but enough is known
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for us to make certain assumptions about how we think and apply these
assumptions in designing AI problems.
SUPERFAST AUTOCONFIGURING ARTIFICIAL NEURAL
NETWORK:
The reasons for adapting ANNs are as follows:
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• Massive parallelism
• Distributive representation and computation
• Learning ability
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• Adaptivity
• Inherent contextual information processing
• Fault tolerance
• Low energy consumption
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BIOLOGICAL NEURON:
The concept of neuron in ANN structure is divided from biological neurons.
A neuron is special biological structure that process information. The output
area of the neuron is called axon through which an impulse triggered by the
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cell can be sent. The input area of the nerve cell is a branching fiber is called
dendrites. When a series of impulses is received at the dendrites area of the
neuron the result is usually an increase probability that the target nearer will
fire an impulse down its action.
ANN ARCHITECTURE:
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ANNs can be categorized into two groups:
• Feed forward networks
• Recurrent networks
Feed forward networks are static; they produce one set of output values rather
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a sequence of values from a given input. These networks are memory less in
the sense their response to an input is independent of the previous network
states. On the other hand recurrent network systems are dynamic systems
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when a new input pattern is presented the neuron outputs are computed,
because of the feedback paths. The inputs to each neuron are then modified,
which leads the network to enter a new state.
In a most common family of feed forward networks is called “multilayer
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perception”, neurons are organized into layers that have unidirectional
connections between them. The bottom layer of units is the input layer, the
only units in a network that receives external inputs. The layer above is the
hidden layer in which the PUs is interconnected to layers above and below.
The top layer is the output layer .the layers are fully interconnected to each PU
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is connected to every unit in the layer above and below it; units are not
connected to other units in the same layer.
A THREE LAYER NEURAL NETWORK
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OUTPUT PATTERN
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OUTPUT LAYER
PATTERN
HIDDEN LAYER
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WEIGHT CONNECTED
BETWEEN NEURON
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INPUT LAYER
INPUT PATTERN tyo
LEARNING:
The ability to learn is a fundamental trait of intelligence. A learning process
in the ANN context can be viewed as the problem of updating network
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architecture and connection weights, so that a network can efficiently perform
a specific task. There are three main learning paradigms:
• Supervised
• Unsupervised
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• Hybrid
In supervised learning the network is provided without a correct answer for
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every input pattern weights are determined to allow the network to produce
answers as close as possible to the known correct answers.
In unsupervised learning doesn’t require correct answers associated with each
input pattern in the training dataset. It explores the underlined structure in a
data, or corrections between patterns in the data and organizes patterns into
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categories from these correlations.
Hybrid learning combines both the supervised and unsupervised learning’s.
TRAINING OF ANN:
There are several training methods used for training of ANN:
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• Back propagation network(BPN)
• Radial basis function network(RBF)
• Levenberg-Marquardt network(LMN)
• Hopfied network
SYSTEM UNDER STUDY:
Here a sample power system is selected to test the neural network model.
POWER SYSTEM MODEL-I:
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The below power system-I consists of bus bars, transformers, transmission
lines, CBs and protective relays with their back-ups. The input pattern consists
of status (on or off) of the protective relays and the circuits breakers of the
power system. The output pattern for the training cases consists of the
corresponding faults of the system
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This power has 10 circuit breakers (CBs), 5 transmission lines (Ls), 2d buses
(Bs), 2 transformers (Ts) and 9 protective relays (Rs). It is assumed that each
protective relay for main and back-up protection and each line has two
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protective relays.
LINE1 LINE2
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BUS 1
C.B 3 C.B 5
T1
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C.B 5 C.B 6
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BUS 2
C.B 7 C.B 10
C.B 9
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LINE3 LINE4 LINE5
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POWER SYSTEM-I FOR FAULT DIAGNOSIS.
APPLICATION OF RADIAL BASIS FUNCTION NETWORK TO THE
PROBLEM:
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As mentioned earlier the radial basis function network model is adopted for
solving the system under study problems. The network can be represented by a
number of inputs, hidden layer and outputs are calculated and subsequently,
radial basis algorithm is applied to determine the weight element changes. The
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more efficient batching operation is applying Q input vector simultaneously
and get the network response to each of them. The inputs and outputs can be
represented by matrices called P and T, which can be written in the following
form:
The network also produces the output in matrix form.
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P= T=
P(1,1) P(1,2)……P(1,q) T(1,1) T(1,2)…………T(1,t)
P(2,1) P(2,2)……P(2,q) T(2,1) T(2,2)………….T(2,t)
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………………………. …………………………..
………………………. …………………………..
P(x,1) P(x,2)……P(x,q) T(s,1) T(s,2)……….....T(s,q)
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PERCEPTRON:
The perceptron is the simplest form of the neural network used for
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classification. It consists of single layer with adjustable synaptic weights and a
threshold. A single layer perceptron is limited to performing pattern
classification with only two separate classes.
• The model of each neuron in the network includes a non linear element
at the output end. tyo
• The network contains one or more layers of hidden neurons that are not
of a part of the input or output of the network. The hidden neurons
enable the network to learn complex tasks by extracting progressively
more meaningful features from the input patterns..
The simulation of perceptron consists of two phases
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• Initialization
• Training
INITIALIZATION: The MATLAB function for the initialization is rad. This
function is used to initialize the weights and bias elements to small positive
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and negative values.
TRAINING:
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The major steps in the training phase can be summarized as follows:
i. The presentation phase: presented the inputs and calculate the network
outputs.
ii. Checking phase: check to see if each output vector is equal to the
target vector associated with the given input.
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iii. Training algorithm: training is done by orthogonal least square
algorithm for radial basis function network.
iv. Learning phase: adjust weight and bias accordingly using perceptron
learning rule.
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FOR POWER SYSTEM 1:
The input layer of the neural network contains informations about the above
mentioned 10 circuit breakers and 9 protective relays.
The input layers are (from the left):
CB1, CB2, CB3, CB4, CB5, CB6, CB7, CB8, CB9,
CB10,LIM,LIB,L2M,L2B,L3M,L3B,L4M,L4B,L5M,L5B,T1M,T2M,X1B,
X2B
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Where:
CB*=circuit breaker
L*M=main relay associated with line
L*B=back up relay associated with line
T*M=main relay associated with transformer
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X*=main relay associated with bus
The possible faults associated with the given power system are transmission
line faults, transformer faults and bus bar faults
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Therefore the variables of the output layer of the neural network1(from the
left)
B1, B2, L1, L2, L3, L4, L5, T1, T2
Where:
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B*=fault of bus bar*
L*=fault of line
T*=fault of transformer
The on/off status of the circuit breakers and the relays are represented by 1s
and 2s as defined in the below table.
DEFINITION OF THE STATUS OF THE NEURON
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NEURON STATUS
1 2
Relay Not operated Operated
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Circuit breaker Not tripped Tripped
Fault components No fault fault
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The typical input patterns and the corresponding output pattern that can be
used to train the neural network are given below:
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TRAINING PATTERNS:
PATTERN-1:
INPUT PATTERN:
112211111111111111111121
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OUTPUT PATTERN:
211111111
PATTERN-2:
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Failure of line L1, due to over current
Relay operated: L1M
Circuit breaker operated: CB1
INPUT PATTERN:
211111111121111111111111
OUTPUT PATTERN:
112111111
TEST PATTERN:
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Failure of main line L1 relay
Relay operated : L1B
Circuit breaker operated: CB1
In this way the patters were computed assuming that only one single fault
occurs at any time. The total number of pattern chosen for the training sets
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were equal to 9,in addition to this 5 patters were selected for the testing of
neural network. This test pattern consists of one piece of equipment
malfunction for the single fault(failure of main relay). Refer the below given
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tables:
PATTERN GENERATION FOR POWER SYSTEM-1:
TRAINING PATTERN FOR SIMULATION
INPUT (9 patterns,24 inputs)
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INPUT/
1 2 3 4 5 6 7 8 9
PATTERN
1 CB1 1 1 2 1 1 1 1 1 2
2 B2 1 1 1 2 1 1 1 1 2
3
4
5
CB3
CB4
CB5
2
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
2
1
tyo OUTPUT (9 pattern,9 output)
OUTPUT/
1 2 3 4 5 6 7 8 9
PATTERN
6 CB6 1 1 1 1 1 1 1 1 2 1 B1 2 1 1 1 1 1 1 1 1
7 CB7 1 2 1 1 2 1 1 1 1 2 B2 1 2 1 1 1 1 1 1 1
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8 CB8 1 2 1 1 1 1 1 1 1 3 L1 1 1 2 1 1 1 1 1 1
9 CB9 1 1 1 1 1 2 1 1 1 4 L2 1 1 1 2 1 1 1 1 1
10 CB10 1 1 1 1 1 1 2 1 1 5 L3 1 1 1 1 2 1 1 1 1
11 L1M 1 1 2 1 1 1 1 1 1 6 L4 1 1 1 1 1 2 1 1 1
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12 L1B 1 1 1 1 1 1 1 1 1 7 L5 1 1 1 1 1 1 2 1 1
13 L2M 1 1 1 2 1 1 1 1 1 8 T1 1 1 1 1 1 1 1 2 1
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14 L2B 1 1 1 1 1 1 1 1 1 9 T2 1 1 1 1 1 1 1 1 2
15 L3M 1 1 1 1 2 1 1 1 1
16 L3B 1 1 1 1 1 1 1 1 1
17 L4M 1 1 1 1 1 2 1 1 1
18 L4B 1 1 1 1 1 1 1 1 1
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19 L5M 1 1 1 1 1 1 2 1 1
20 L5B 1 1 1 1 1 1 1 1 1
21 T1M 1 1 1 1 1 1 1 2 1
22 T2M 1 1 1 1 1 1 1 1 2
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23 X1M 2 1 1 1 1 1 1 1 1
24 X2B 1 2 1 1 1 1 1 1 1
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INPUT/PATTERN 1 2 3 4 5
1 CB1 2 1 1 1 1
2 CB2 1 2 1 1 1
3 CB3 1 1 1 1 1
4 CB4 1 1 1 1 1 OUTPUT(TESTING PATTERN)
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5 CB5 1 1 1 1 1 (5 patterns 9 outputs)
6 CB6 1 1 1 1 1
7 CB7 1 1 1 1 1 OUTPUT/
1 2 3 4 5
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8 CB8 1 1 1 1 1 PATTERN
1 B1 1 1 1 1 1
9 CB9 1 1 1 2 1
2 B2 1 1 1 1 1
10 CB10 1 1 1 1 2
3 L1 2 1 1 1 1
11 L1M 1 1 1 1 1
4 L2 1 2 1 1 1
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12 L1B 2 1 1 1 1
13 L2M 1 1 1 1 1 5 L3 1 1 2 1 1
14 L2B 1 2 1 1 1 6 L4 1 1 1 2 1
15 L3M 1 1 1 1 1 7 L5 1 1 1 1 2
16 L3B 1 1 2 1 1 8 T1 1 1 1 1 1
17
18
19
L4M
L4B
L5M
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
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20 L5B 1 1 1 1 2
21 T1M 1 1 1 1 1
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22 T2M 1 1 1 1 1
23 X1M 1 1 1 1 1
24 X2B 1 1 1 1 1
INPUT( TESTING PATTERN)5 pattern, 24
inputs)
CONCLUSION:
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The salient features of the RBF networks are:
a) They are extremely fast due to the hybrid two stage training scheme
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employed.
b) They have only a single hidden layer with growing number of neurons
during learning to achieve an optimal configuration.
c) Only a single network parameter called spread factor (SF) is varied.
LIMITATIONS:
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• Requires more training data for more accurate results.
• Unworthiness to detect multiple faults due to more piece of
equipment malfunctioning.
REFRENCES:
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1) Sharestani.S.A, Silartis.J.Y.P “Application of pattern recognition to
identification of power faults, electric power system research”.
2) Fausett.L “ Fundamentals of neural networks”, PHI, 1994 and several other
technical periodicals.
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