In recent years, the golden codes have proven to exhibit a superior performance in a wireless
MIMO (Multiple Input Multiple Output) scenario than any other code. However, a serious
limitation associated with it is its increased decoding complexity. This paper attempts to resolve
this challenge through suitable modification of golden code such that a less complex sphere
decoder could be used without much compromising the error rates. In this paper, a minimum
polynomial equation is introduced to obtain a reduced golden ratio (RGR) number for golden
code which demands only for a low complexity decoding procedure. One of the attractive
approaches used in this paper is that the effective channel matrix has been exploited to perform
a single symbol wise decoding instead of grouped symbols using a sphere decoder with tree
search algorithm. It has been observed that the low decoding complexity of O (q1.5) is obtained
against conventional method of O (q2.5). Simulation analysis envisages that in addition to
reduced decoding, improved error rates is also obtained.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using A...ijtsrd
This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Application of particle swarm optimization with ANFIS model for double scroll...IJECEIAES
The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if neuro-fuzzy such as adaptive neural fuzzy inference system (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.
Optimization of Number of Neurons in the Hidden Layer in Feed Forward Neural ...IJERA Editor
The architectures of Artificial Neural Networks (ANN) are based on the problem domain and it is applied during
the „training phase‟ of sample data and used to infer results for the remaining data in the testing phase.
Normally, the architecture consist of three layers as input, hidden, output layers with the number of nodes in the
input layer as number of known values on hand and the number of nodes as result to be computed out of the
values of input nodes and hidden nodes as the output layer. The number of nodes in the hidden layer is
heuristically decided so that the optimum value is obtained with reasonable number of iterations with other
parameters with its default values. This study mainly focuses on Cascade-Correlation Neural Networks (CCNN)
using Back-Propagation (BP) algorithm which finds the number of neurons during the training phase itself by
appending one from the previous iteration satisfying the error condition gives a promising result on the optimum
number of neurons in the hidden layer
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN NEURAL NETWORK BASED IDENT...ijcsit
This paper explores the application of artificial neural networks for online identification of a multimachine power system. A recurrent neural network has been proposed as the identifier of the two area, four machine system which is a benchmark system for studying electromechanical oscillations in multimachine power systems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of the paper is on investigating the performance of the variants of the Backpropagation algorithm in training the neural identifier. The paper also compares the performances of the neural identifiers trained using variants of the Backpropagation algorithm over a wide range of operating conditions. The simulation results establish a satisfactory performance of the trained neural identifiers in identification of the test power system.
Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using A...ijtsrd
This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Application of particle swarm optimization with ANFIS model for double scroll...IJECEIAES
The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if neuro-fuzzy such as adaptive neural fuzzy inference system (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.
Optimization of Number of Neurons in the Hidden Layer in Feed Forward Neural ...IJERA Editor
The architectures of Artificial Neural Networks (ANN) are based on the problem domain and it is applied during
the „training phase‟ of sample data and used to infer results for the remaining data in the testing phase.
Normally, the architecture consist of three layers as input, hidden, output layers with the number of nodes in the
input layer as number of known values on hand and the number of nodes as result to be computed out of the
values of input nodes and hidden nodes as the output layer. The number of nodes in the hidden layer is
heuristically decided so that the optimum value is obtained with reasonable number of iterations with other
parameters with its default values. This study mainly focuses on Cascade-Correlation Neural Networks (CCNN)
using Back-Propagation (BP) algorithm which finds the number of neurons during the training phase itself by
appending one from the previous iteration satisfying the error condition gives a promising result on the optimum
number of neurons in the hidden layer
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
Study Of The Fault Diagnosis Based On Wavelet And Fuzzy Neural Network For Th...IJRES Journal
In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous motor, which adopts the technology of wavelet packet analysis. It also composes the characteristics of the vector as input of fuzzy RBF neural network, used to diagnose the induction motor bearing failures. The method overcomes the slow convergence, a long training time, local minimum problems when using BP neural network. Experimental results shows that using fuzzy RBF neural network can improve the accuracy of the motor fault diagnosis.
Optimal Location of Multi-types of FACTS Devices using Genetic Algorithm IJORCS
The problem of improving the voltage profile and reducing power loss in electrical networks is a task that must be solved in an optimal manner. Therefore, placement of FACTS devices in suitable location can lead to control in-line flow and maintain bus voltages in desired level and reducing losses is required. This paper presents one of the heuristic methods i.e. a Genetic Algorithm to seek the optimal location of FACTS devices in a power system. Proposed algorithm is tested on IEEE 30 bus power system for optimal location of multi-type FACTS devices and results are presented.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Two Bit Arithmetic Logic Unit (ALU) in QCAidescitation
Quantum cellular automata (QCA) is a new
technology in nanometre scale (<18nm) to support nano
technology. QCA is very effective in terms of high space density
and power dissipation and will be playing a major role in the
development of the Quantum computer with low power
consumption and high speed. This paper describes the design
and layout of a 2-bit ALU based on quantum-dot cellular
automata (QCA) using the QCADesigner design tool. The
ALU design is based on combinational circuits which reduces
the required hard-ware complexity and allows for reasonable
simulation times. The paper aims to provide evidence that
QCA has potential applications in future Quantum computers,
provided that the underlying technology is made feasible.
Design has been made using certain combinational circuits
by using Majority gate, AND, OR, NOT, X-OR in QCA. 2 bit
ALU needs the design of Logical Extender, Arithmetic
Extender and the Full adder circuits using QCA. The QCA is
a novel tool to realize Nano level digital devices and study and
analyze their various parameters.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
Short Term Load Forecasting: One Week (With & Without Weekend) Using Artifici...IJLT EMAS
This paper present for analysis of short term load forecasting: one week (with & without weekend) using ANN techniques for SLDC of Gujarat. In this paper short term electric load forecasting using neural network; based on historical load demand, The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back propagation algorithm for the Multilayer Feed Forward ANN model using MATLAB.12 ANN tool box. Design a model for one week (with & w/o weekend) load pattern for STLF using the neural network have been input variables are (Min., Avg., & Max. load demands for previous week, Min., Avg., & Max. temperature for previous week & Min., Avg., & Max. humidity for previous week). And Nov-12 to Apr-13 (6 Months) historical load data from the SLDC, Gujarat are used for training, testing and showing the good performance. Using this ANN model computing the mean absolute error between the exact and predicted values, we were able to obtain an absolute mean error within specified limit and regression value close to one. This represents a high degree of accuracy.
Study Of The Fault Diagnosis Based On Wavelet And Fuzzy Neural Network For Th...IJRES Journal
In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous motor, which adopts the technology of wavelet packet analysis. It also composes the characteristics of the vector as input of fuzzy RBF neural network, used to diagnose the induction motor bearing failures. The method overcomes the slow convergence, a long training time, local minimum problems when using BP neural network. Experimental results shows that using fuzzy RBF neural network can improve the accuracy of the motor fault diagnosis.
Optimal Location of Multi-types of FACTS Devices using Genetic Algorithm IJORCS
The problem of improving the voltage profile and reducing power loss in electrical networks is a task that must be solved in an optimal manner. Therefore, placement of FACTS devices in suitable location can lead to control in-line flow and maintain bus voltages in desired level and reducing losses is required. This paper presents one of the heuristic methods i.e. a Genetic Algorithm to seek the optimal location of FACTS devices in a power system. Proposed algorithm is tested on IEEE 30 bus power system for optimal location of multi-type FACTS devices and results are presented.
Performance assessment of an optimization strategy proposed for power systemsTELKOMNIKA JOURNAL
In the present article, the selection process of the topology of an artificial neural network (ANN) as well as its configuration are exposed. The ANN was adapted to work with the Newton Raphson (NR) method for the calculation of power flow and voltage optimization in the PQ nodes of a 10-node power system represented by the IEEE 1250 standard system. The purpose is to assess and compare its results with the ones obtained by implementing ant colony and genetic algorithms in the optimization of the same system. As a result, it is stated that the voltages in all system nodes surpass 0,99 p.u., thus representing a 20% increase in the optimal scenario, where the algorithm took 30 seconds, of which 9 seconds were used in the training and validation processes of the ANN.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Two Bit Arithmetic Logic Unit (ALU) in QCAidescitation
Quantum cellular automata (QCA) is a new
technology in nanometre scale (<18nm) to support nano
technology. QCA is very effective in terms of high space density
and power dissipation and will be playing a major role in the
development of the Quantum computer with low power
consumption and high speed. This paper describes the design
and layout of a 2-bit ALU based on quantum-dot cellular
automata (QCA) using the QCADesigner design tool. The
ALU design is based on combinational circuits which reduces
the required hard-ware complexity and allows for reasonable
simulation times. The paper aims to provide evidence that
QCA has potential applications in future Quantum computers,
provided that the underlying technology is made feasible.
Design has been made using certain combinational circuits
by using Majority gate, AND, OR, NOT, X-OR in QCA. 2 bit
ALU needs the design of Logical Extender, Arithmetic
Extender and the Full adder circuits using QCA. The QCA is
a novel tool to realize Nano level digital devices and study and
analyze their various parameters.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
High - Performance using Neural Networks in Direct Torque Control for Asynchr...IJECEIAES
This article investigates solution for the biggest problem of the Direct Torque Control on the asynchronous machine to have the high dynamic performance with very simple hysteresis control scheme. The Conventional Direct Torque Control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, as well as flux control at very low speed. In this paper, we propose an intelligent approach to improve the direct torque control of induction machine which is an artificial neural networks control. The principle, the numerical procedure and the performances of this method are presented. Simulations results show that the proposed ANN-DTC strategy effectively reduces the torque and flux ripples at low switching frequency, compared with Fuzzy Logic DTC and The Conventional DTC.
Artificial Neural Network Based Fault Classifier for Transmission Line Protec...IJERD Editor
This paper presents a method for classification of transmission line faults based on Artificial Neural Network (ANN).Samples of prefault and postfault three phase currents taken at one end of transmission line are used as ANN inputs. Simulation studies have been carried out extensively on two power system models: one in which the transmission line is fed from one end and another, in which the transmission line is fed from two ends. Different types of faults at different operating conditions have been considered for carrying out simulation studies. The simulation results confirm the feasibility of the proposed approach.
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.
Determination of Fault Location and Type in Distribution Systems using Clark ...IJAPEJOURNAL
In this paper, an accurate method for determination of fault location and fault type in power distribution systems by neural network is proposed. This method uses neural network to classify and locate normal and composite types of faults as phase to earth, two phases to earth, phase to phase. Also this method can distinguish three phase short circuit from normal network position. In the presented method, neural network is trained by αβ space vector parameters. These parameters are obtained using clarke transformation. Simulation results are presented in the MATLAB software. Two neural networks (MLP and RBF) are investigated and their results are compared with each other. The accuracy and benefit of the proposed method for determination of fault type and location in distribution power systems has been shown in simulation results.
Implementation of Feed Forward Neural Network for Classification by Education...ijsrd.com
in the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. Digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL).General Terms-Network.
Available transfer capability computations in the indian southern e.h.v power...eSAT Journals
Abstract This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow, Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
Park’s Vector Approach to detect an inter turn stator fault in a doubly fed i...cscpconf
An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach , using a neural network s.
Transient Stability Assessment and Enhancement in Power SystemIJMER
Power system is subjected to sudden changes in load levels. Stability is an important concept
which determines the stable operation of power system. For the improvement of transient stability the
general methods adopted are fast acting exciters, circuit breakers and reduction in system transfer
reactance. The modern trend is to employ FACTS devices in the existing system for effective utilization
of existing transmission resources. The critical clearing time is a measure to assess transient instability.
Using PSAT, the critical clearing time (CCT) corresponding to various faults are calculated. The most
critical faults were identified using this calculation. The CCT for the critical faults were found to change
with change in operating point. The CCT values are predicted using Artificial Neural Network (ANN) to
study the training effects of ANN. TCSC is selected as the FACTS device for transient stability
enhancement. Particle Swarm Optimization method is used to find the optimal position of TCSC using
the objective function real power loss minimization. The result shows that the technique effectively
increases the transient stability of the system
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
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2. 330 Computer Science & Information Technology (CS & IT)
tance in power system operation and control. The capability of the ANNs to approximate non
linear functions sufficiently accurately makes them a suitable choice for use in identification of
non linear systems. Multilayer feedforward artificial neural networks using Backpropagation al-
gorithm for training have been proposed for successful online model identification of synchron-
ous generator [18] and a UPFC equipped single machine infinite bus system [19]. A neural net-
work based estimation unit has been proposed to estimate in real time, the parameters for an inter-
facing scheme for grid-connected inverters and simultaneously estimating the grid voltage [20].
The authors have employed neural network for system identification for predictive control of a
multimachine power system operating under widely varying operating conditions and subjected
to transient conditions [21].
The work undertaken proposes to use a recurrent neural network for online identification of a
multimachine power system. This work aims to investigate the performance of such a neural net-
work in online identification of a multimachine power system. The testing performances of the
trained neural identifier are also investigated to establish the accuracy of the trained identifier.
2. SYSTEM DESCRIPTION
The system under consideration for the undertaken work is a two area system with active power
flowing from Area 1 to Area 2 [22]. In spite of the small size of the system, its behavior mimics
the behavior of a large power system in actual operation. Each area comprises of two 900 MVA
machines and the two areas are connected by a 220 kV double circuit line of length 220 km. The
load voltage profile is improved by installing additional 187 MVAr capacitors in each area. The
system under study is equipped with PSS and has a UPFC installed between bus 11 and bus 12
with bus 11 common to the shunt and series converters and the other side of the series converter
connected to bus 12 as shown in Figure 1.
The effective utilization of the UPFC in the system requires implementation of various control
schemes, many of which require the system to be identified. In this work, the active power at bus
12, 12P corresponding to a specific value of the quadrature component, qV of the series voltage
injected by the UPFC is to be predicted under various operating conditions. This next step value
of 12P is predicted using the values of qV and 12
P at some preceding instants.
Fig.1. 2-Area system equipped with UPFC
3. Computer Science & Information Technology (CS & IT) 331
3. DESIGN OF THE NEURAL IDENTIFIER
Recurrent neural networks have the capability to predict the future values based on the values at
the preceding instants. The nonlinear autoregressive network with exogenous (independent) in-
puts i.e. NARX, is a recurrent dynamic network defined by
( ) ( ) ( ) ( ) ( )( )uy ntutuntytyfty −−−−= ,,1,,,1 KK (1)
Where ( )ky and ( )ku are the outputs and inputs at the kth instant and yn , un are the number of
time steps for which the current output is regressed on the output and input respectively. A dia-
gram showing the implementation of the NARX model using a feedforward neural network to
approximate the function f in (1) is given in Figure 2.
Fig.2. Implementation of NARX model
A neural identifier that predicts the next step value of 12P on the basis of the values of qV and 12
P at
four preceding instants is proposed. As the objective clearly requires that the output of the neural
network should depend on the current input as well as on current and/or previous inputs and out-
puts, the NARX model is used. A two layer neural network with sigmoidal hidden layer neurons
and linear output layer neurons can identify any system with any degree of accuracy, subject to
the availability of sufficient number of hidden neurons [23]. Therefore, the NARX model is im-
plemented using the two layer feedforward neural network. Since the true value of the output
12
P for the preceding instants are available, the series parallel architecture is used. As the values
of q
V and 12
P at four preceding instants are to be used, the total number of inputs to the neural
network is eight as shown in Figure 3. The number of sigmoidal neurons in the hidden layer has
been fixed at thirteen using trial and error approach and the output layer has one linear neuron.
Fig.3. Architecture of the proposed neural identifier
4. 332 Computer Science & Information Technology (CS & IT)
The system under consideration is modeled and simulated using MATLAB / SIMULINK to gen-
erate data for training and testing the proposed neural identifier. The operation of the system is
simulated by applying qV restricted within the range +0.1 pu and -0.1 pu (restricting the quadra-
ture component of the series injected voltage to 10% of the nominal line-to-ground voltage) and
sampling the input, q
V and output, 12
P at the rate of 32 samples per second. The neural network
is presented with the following inputs
( ) ( ) ( ) ( ) T
qqqq tVtVtVtV ]321[ −−−=qV (2)
And
( ) ( ) ( ) ( ) T
tPtPtPtP ]4321[ 12121212
−−−−=12
P (3)
for predicting ( )tP12
. For linear input neurons, the output of the input neurons is same as the input
given by
T
][ 12q
0
18
PVa =×
(4)
The output of the hidden layer (layer 1), consisting of 13 sigmoidal neurons is given by
( )1
113
0
18
1
813
1
113
baWa ××××
+= sigtan (5)
where, 1
813×W is the weight matrix between input and hidden layers and 1
113×b is the bias to the hid-
den layer neurons. Similarly, the output of the output layer (layer 2), comprising of one linear
neuron is given by
( )21
113
2
13112
ˆ bpurelinP += ×× aW (6)
where, 2
131×W is the weight matrix between hidden and output and layers and
2
b is the bias to the
output layer neurons.
4. TRAINING OF THE NEURAL IDENTIFIER
Identification requires setting up a suitably parameterized identification model and adjustment of
these parameters of the model to optimize a performance function based on the error between the
outputs from the plant and the identification model [24]. It is assumed that the weight matrices of
the neural network proposed as the identifier exists, for which, both plant and the identifier have
the same output for any specified inputs, for the same initial conditions [24].
The system under consideration is simulated for different operating conditions ranging over a
wide range of steady state active power flow level between the two areas to generate data for
training. The training data set consists of 1288 data points spread over a wide range of operation.
This training dataset is employed in training the proposed neural identifier offline through simula-
tion to make it learn the forward dynamics of the plant. During training the weights and biases of
the network are iteratively adjusted to minimize the network performance function. The perfor-
mance function used for the neural identifier under consideration is the mean square error, mse,
given by
( ) ( )
2
1
2
1
1212
1ˆ1
∑=∑ −=
==
N
q
q
N
q
e
N
PP
N
V qq
(7)
5. Computer Science & Information Technology (CS & IT) 333
where, N is the size of training dataset, 12P and 12
ˆP are the target and predicted value of the output
of the neural network when the th
q input is presented and qe is the error (difference between the
target and predicted value) for the th
q input. The performance index V in (7) is a function of
weights and biases, ][ 21 nxxx K=x and can be given by
( ) ( )∑=
=
N
q
q xe
N
xV
1
21
(8)
The performance of the neural network can be improved by modifying x till the desired level of
the performance index, ( )xV is achieved. This is achieved by minimizing ( )xV with respect to x
and the gradient required for this is given by
( ) ( ) ( )xexJV T
x =∇ (9)
where, ( )xJ is the Jacobian matrix given by
( )
( ) ( )
( ) ( )
∂
∂
∂
∂
∂
∂
∂
∂
=
n
NN
n
x
xe
x
xe
x
xe
x
xe
xJ
L
MOM
L
1
1
1
1
(10)
and ( )xe is the error for all the inputs.
The gradient in (9) is determined using backpropagation, which involves performing computa-
tions backward through the network. This gradient is then used by different algorithms to update
the weights of the network. These algorithms differ in the way they use the gradient to update the
weights of the network and are known as the variants of the Backpropagation algorithm. This
work compares the performance of the basic implementation of the Backpropagation algorithm
i.e. Gradient descent algorithm with the Levenberg-Marquardt algorithm. A brief overview of the
different algorithms considered in this work is given under:
4.1 Gradient Descent Algorithm
The network weights and biases, x is modified in a direction that reduces the performance func-
tion in (8) most rapidly i.e. the negative of the gradient of the performance function [25]. The
updated weights and biases in this algorithm are given by
kkkk
Vxx ∇−=+
α1
(11)
Where, k
x is the vector of the current weights and biases, k
V∇ is the current gradient of the per-
formance function and k
α is the learning rate.
4.2 Levenberg-Marquardt Algorithm
Since the performance index in (8) is sum of squares of non linear function, the numerical opti-
mization techniques for non linear least squares can be used to minimize this cost function. The
Levenberg-Marquardt algorithm (LM), which is an approximation to the Newton’s method is said
to be more efficient in comparison to other methods for convergence of the Backpropagation al-
gorithm for training a moderate-sized feedforward neural network [26]. As the cost function is a
6. 334 Computer Science & Information Technology (CS & IT)
sum of squares of non linear function, the Hessian matrix required for updating the weights and
biases need not be calculated and can be approximated as
( ) ( )xJxJH T
= (12)
The updated weights and biases are given by
( ) ( ) ( ) ( )xexJIxJxJxx TT
kk
1
1 ][ −
+ +−= µ (13)
where, µ is a scalar and I is the identity matrix.
This work compares the performance of the basic implementation of the Backpropagation algo-
rithm i.e. Gradient descent algorithm with the LM algorithm.
5. SIMULATION RESULTS AND DISCUSSIONS
5.1 Training
The neural network proposed in section 3 was trained using the training set and the training algo-
rithms described in section 4. A Pentium (R) Dual-Core CPU T4400 @2.20 GHz was used to
train the proposed neural identifier. The proposed neural identifier was trained 50 times each with
the two training algorithms, with random initial weights taken for each trial to rule out the weight
sensitivity of the performance of the two training algorithms. The network was trained in each
case till the value of the performance index in (8) was 0.0001 or less. The training trials estab-
lished the inability of the Gradient Descent algorithm to converge for the required value of the
performance index but the LM algorithm converges successfully during each of the 50 trials. The
average time (from the 50 trials) required for training the network using the Levenberg-
Marquardt algorithm using the entire training set consisting of 1288 data points is 5.8675
seconds. The minimum and maximum time required for training is 2.9393 and 9.4187 seconds
respectively.
5.2 Testing
The trained neural network was tested on the same CPU. The test datasets consisted of data points
not included in the training set. The system under consideration was simulated at two such oper-
ating points for which no data point was included in the training set. The operation of the multi-
machine power system under consideration at these two operating points was simulated using
MATLAB/SIMULINK for a period of 13 seconds each. This period also included a 3-phase short
circuit fault at point A at t=10 s for a duration of 200 ms with the circuit breakers auto reclosing
after 12 cycles. The load on the system was then increased and the operation of the system under
the new load condition was then simulated for a period of 45 seconds with the same fault at t=30
s with auto reclosing circuit breakers. The data during these three simulations was sampled at the
rate of 32 samples per second to form three test sets: Test Set I and Test Set II, corresponding to
the two operating points at the initial loading condition and Test Set III, at the higher load operat-
ing point. As the Gradient Descent algorithm failed to converge for the desired value of the per-
formance index, the neural network that was trained using the LM algorithm was tested using
these three test sets.
Test Set I. Test Set I consists of 417 data points. The first four data points are used to predict the
output at the next instant. Therefore, the number of predicted outputs for this test set is 413. A
7. Computer Science & Information Technology (CS & IT) 335
three-phase short circuit fault is simulated at t=10 s which corresponds to the sample point num-
ber 321 in the test set. The actual output for the system after autoreclosure of the circuit breaker is
available in the sample point number 329 of the test set. The actual values of the output power
and the values predicted for the same using the neural identifier during a part of the steady state
and transient period are shown in Figure 4 and 5 respectively. The effect of the 3-phase short cir-
cuit fault on the system is captured in the sample point 322 of the actual output power as shown
in Figure 5. However, the circuit breakers operate and an improved system performance is re-
flected in subsequent samples. As the neural network has been trained to use the information at
four preceding instants to predict the next step output, the effect of decrease in the actual output
in sample 322 is reflected immediately in the values predicted by the neural identifier in sample
323. Figure 5 clearly shows that the values predicted by the neural identifier follow the actual
values closely in the transient period. The effect of autoreclosure of the circuit breakers on the
power level is visible in sample number 329 of the actual output power. The increased value of
the actual output power in sample 329 due to the autoreclosure of the circuit breakers is reflected
in the value predicted by the neural identifier in the subsequent instant. The average absolute er-
ror in the predicted values is determined as a measure to establish the predictive quality of the
neural identifier. The value of the average absolute error over the entire set is 0.0103.
Fig.4. Actual and predicted values of output power during steady state for test set I
Fig.5. Actual and predicted values of output power during transient period for test set I
Test Set II. Test Set II also consists of 417 data points and 413 predicted outputs. This test set is
generated by simulating the system at an operating point corresponding to a different tie line
power level and subjected to the same fault as in Test Set I. Figures 6 and 7 show the actual and
8. 336 Computer Science & Information Technology (CS & IT)
predicted output values for a section of steady state and transient period of the system respective-
ly, at the operating point under consideration. It is clear from these figures that the actual power
output values and the values predicted using the neural identifier in steady state and transient pe-
riod are in close proximity even at this operating point. The average absolute error over this test
set is 0.0078.
301 303 305 307 309 311 313 315 317318318
1
1.2
1.4
1.6
1.8
2
2.2
x 10
8
Sample
ActivePower,P12
(W)
Target
Predicted (LM)
Fig.6. Actual and predicted values of output power during steady state for test set II
320 322 324 326 328 330 332 334 336336
−2
−1
0
1
2
3
4
x 10
8
Sample
ActivePower,P12
(W)
Target
Predicted (LM)
Fig.7. Actual and predicted values of output power during transient period for test set II
Test Set III. Test Set III consists of 1441 data points. The actual and predicted output values for
a section of steady state and transient period of the system are shown in figures 8 and 9 respec-
tively. These figures demonstrate the same trend as in Test Set I and II. The value of average ab-
solute error for this test set is 0.0367.
Fig.8. Actual and predicted values of output power during steady state for test set III
9. Computer Science & Information Technology (CS & IT) 337
Fig.9. Actual and predicted values of output power during transient period for test set III
6. CONCLUSION
A neural network has been proposed to predict the next step value of the output power on the
basis of the values of the control input and output power at preceding time instants. The proposed
neural network is trained using the Levenberg-Marquardt algorithm. The trained neural identifier
is tested over a range of operating conditions and the test results establish a satisfactory perfor-
mance of the trained neural identifier over the entire range of testing conditions. The availability
of fast computing machines in current times and the accurate predictions reported in this work
clearly establish the scope for online application of neural networks for identification of multima-
chine power systems.
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