This paper presents a Probabilistic Neural Network (PNN) approach for identifying and classifying faults on power transmission lines. The PNN is trained on voltage waveform data simulated using Electromagnetic Transient Program (EMTP) software for different fault types and locations on a 150km transmission line. Only two sets of simulated data are used to train the PNN, requiring less computation than other methods that preprocess data. The trained PNN is able to accurately identify and classify fault types based on the voltage waveform, which helps ensure reliable power transmission by isolating only faulty lines or phases.
FUZZY LOGIC APPROACH FOR FAULT DIAGNOSIS OF THREE PHASE TRANSMISSION LINEJournal For Research
This document summarizes a journal article that proposes using fuzzy logic to diagnose faults on three-phase transmission lines. It begins with an abstract of the journal article, which describes using fuzzy logic as an intelligent technique to quickly and accurately identify the type of fault that occurs on a transmission system. It then provides background on transmission line faults, fault types, and challenges with transmission line protection. The document outlines the proposed fuzzy logic approach, including defining fault types as fuzzy sets and developing if-then rules to relate transmission line voltages and currents to faults. Simulation results are presented showing the fuzzy logic approach can identify different fault types based on the current responses. The conclusion is that the proposed fuzzy logic method allows for fast and reliable fault detection on transmission
Fault location in sec interconnected network based on synchronized phasor mea...Abhishek Kulshreshtha
This document discusses using synchronized phasor measurements from Phasor Measurement Units (PMUs) to locate faults in interconnected power networks. It addresses the challenge that it is not economical to install PMUs at all network buses. The paper proposes using the Tree Search Method (TSM) to determine a near-optimal placement of PMUs that allows fault location. It presents simulation results applying TSM to standard test systems and a real network, showing the ability to accurately locate different fault types. Mathematical formulations for calculating fault distances are also discussed.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
This document presents a novel methodology for fault section estimation in power systems. The methodology uses protective device settings and a search algorithm to identify isolated sections. It considers changes in network topology caused by protective devices operating. The methodology calculates a fault reference value for each section based on relay settings and topology. It then calculates the deviation between this reference and the actual weights based on operated protections to identify the most likely fault sections. The result provides operators a prioritized list of fault candidates to assist with decision making during disturbances. The methodology uses intrinsic system information rather than uncertainties used in other existing methods.
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.
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
Effective two terminal single line to ground fault location algorithmMuhd Hafizi Idris
This paper presents an effective algorithm to locate Single Line to Ground (SLG) fault at a transmission line. Post fault voltages and currents from both substation terminals were used as the input parameters to the algorithm. Discrete Fourier Transform (DFT) was used to extract the magnitudes and phase angles of three phase voltages and currents. The modeling of the transmission line along with the algorithm was performed using Matlab/Simulink package. The results of fault location for SLG faults along the transmission line demonstrated the validity of the algorithm used even for high resistance earth fault.
The document presents a new method for fault classification and direction discrimination in transmission lines using 1D convolutional neural networks (1D-CNNs). A 132kV transmission line model is simulated to generate training and testing data for the 1D-CNN algorithm. The proposed 1D-CNN approach directly uses the voltage and current signals from one end as input, merging feature extraction and classification into a single learning process. Testing shows the 1D-CNN method accurately classifies and discriminates fault direction with higher accuracy than conventional neural network and fuzzy neural network methods under different fault conditions.
FUZZY LOGIC APPROACH FOR FAULT DIAGNOSIS OF THREE PHASE TRANSMISSION LINEJournal For Research
This document summarizes a journal article that proposes using fuzzy logic to diagnose faults on three-phase transmission lines. It begins with an abstract of the journal article, which describes using fuzzy logic as an intelligent technique to quickly and accurately identify the type of fault that occurs on a transmission system. It then provides background on transmission line faults, fault types, and challenges with transmission line protection. The document outlines the proposed fuzzy logic approach, including defining fault types as fuzzy sets and developing if-then rules to relate transmission line voltages and currents to faults. Simulation results are presented showing the fuzzy logic approach can identify different fault types based on the current responses. The conclusion is that the proposed fuzzy logic method allows for fast and reliable fault detection on transmission
Fault location in sec interconnected network based on synchronized phasor mea...Abhishek Kulshreshtha
This document discusses using synchronized phasor measurements from Phasor Measurement Units (PMUs) to locate faults in interconnected power networks. It addresses the challenge that it is not economical to install PMUs at all network buses. The paper proposes using the Tree Search Method (TSM) to determine a near-optimal placement of PMUs that allows fault location. It presents simulation results applying TSM to standard test systems and a real network, showing the ability to accurately locate different fault types. Mathematical formulations for calculating fault distances are also discussed.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
This document presents a novel methodology for fault section estimation in power systems. The methodology uses protective device settings and a search algorithm to identify isolated sections. It considers changes in network topology caused by protective devices operating. The methodology calculates a fault reference value for each section based on relay settings and topology. It then calculates the deviation between this reference and the actual weights based on operated protections to identify the most likely fault sections. The result provides operators a prioritized list of fault candidates to assist with decision making during disturbances. The methodology uses intrinsic system information rather than uncertainties used in other existing methods.
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.
Wavelet energy moment and neural networks based particle swarm optimisation f...journalBEEI
In this study, a combined approach of discrete wavelet transform analysis and a feed forward neural networks algorithm to detect and classify transmission line faults. The proposed algorithm uses a multi -resolution analysis decoposition of three-phasecurrents only to calculate the wavelet energy moment of detailed coefficients. In comparison with the energy spectrum, the energy moment could reveal the energy distribution features better, which is beneficial when extracting signal features. Theapproach use particle swarm optimization algorithm to train a feed forward neural network. The goal is the enhancement of the convergence rate, learning process and fill up the gap of local minimum point.The purposed scheme consists of two FNNs, one for detecting and another for classifying all the ten types of faults using Matlab/Simulink. The proposed algorithm have been extensively tested on a system 400 kV, 3 phases, 100 km line consideringvarious fault parameter variations.
Effective two terminal single line to ground fault location algorithmMuhd Hafizi Idris
This paper presents an effective algorithm to locate Single Line to Ground (SLG) fault at a transmission line. Post fault voltages and currents from both substation terminals were used as the input parameters to the algorithm. Discrete Fourier Transform (DFT) was used to extract the magnitudes and phase angles of three phase voltages and currents. The modeling of the transmission line along with the algorithm was performed using Matlab/Simulink package. The results of fault location for SLG faults along the transmission line demonstrated the validity of the algorithm used even for high resistance earth fault.
The document presents a new method for fault classification and direction discrimination in transmission lines using 1D convolutional neural networks (1D-CNNs). A 132kV transmission line model is simulated to generate training and testing data for the 1D-CNN algorithm. The proposed 1D-CNN approach directly uses the voltage and current signals from one end as input, merging feature extraction and classification into a single learning process. Testing shows the 1D-CNN method accurately classifies and discriminates fault direction with higher accuracy than conventional neural network and fuzzy neural network methods under different fault conditions.
This document describes a special project on using an artificial neural network (ANN) for load flow studies of the MSU-IIT electrical system. The objectives are to model the power system as a 5-bus system, evaluate bus voltages using a power flow program under different loads, train an ANN using the power flow results, and validate the ANN's accuracy by comparing its results to the power flow program. The document reviews literature on load flow studies, numerical methods, ANNs, and discusses how ANNs could provide faster and more accurate solutions to complex load flow problems compared to numerical methods.
"Use of PMU data for locating faults and mitigating cascading outage"Power System Operation
This document summarizes two methods presented in the paper: 1) A fault location method that uses sparse PMU data and electromechanical wave propagation to detect faults on transmission lines. It introduces a decision tree classifier to analyze voltage measurements and locate faults with high accuracy. 2) A controlled islanding scheme to predict and mitigate cascading outages. It uses spectral clustering to partition the system and suggest switching actions to create stable islands with minimum load shedding. The methods were tested on simulated systems and show potential to improve grid monitoring, fault response and prevention of blackouts.
Backpropagation Neural Network Modeling for Fault Location in Transmission Li...ijeei-iaes
In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
The power supply system is completely hooked into three major parts. First one is generation, second one is
transmission and the last one is distribution of electricity supply at the range of 415V to 400V approx. But while
the fault occurs it affects other lines additionally, and this causes difficulties for local people and additionally
perturb the flow of current in different areas. This eccentric and perturbed supply of nuisance is very
hazardous as it cannot be ceased when it comes to equal distribution of electricity. The area suffering from
faults and the other both get affected. So to stop all these we have implemented this project of Coordination of
over current relay utilising optimisation technique. We have utilised crow search algorithms with Kennedy as
swarm perspicacity algorithms which are very auxiliary in storing excess electricity supply and can be used
when needed. With the avail of this we can renovate the potency supply and this will conclusively implement
our main objective of this project.
Wide Area Fault Location for Power Transmission Network using Reactance Based...Muhd Hafizi Idris
Download here: https://www.researchgate.net/publication/332441499_Wide_Area_Fault_Location_for_Power_Transmission_Network_using_Reactance_Based_Method?_sg=Tkk3ur2Kc3XGh3JHwtJdPM3IdJJx_K42N3Zu9kX_ECutHW5j91ExIMtrJFOui4E-RikSYmuYR0uZWEEVHoSaDTPZuRvC29V6GzZ5g9BS.GnmzKNF1XN22czjk5npta57bMn8D2KxxwQsAMEPlK7abE5qGykkxj8CgUcnYHlzpKEZST1ujqv7avTquOi7Aug
With the advancements in smart grid, communication technology, intelligent electronic device and substation automation, wide area applications for monitoring, protection, control and fault location becoming focused nowadays and improved from time to time. This research focuses on using wide area synchrophasor measurements for fault location in transmission network which acts as a backup to conventional fault location method. Simple reactance based methods together with a developed rules system are used to locate the possible affected line and its fault location. Using the developed rules and algorithm, fault location impedance will be compared at each synchrophasor bus connected lines for different fault types, then between connected lines and finally between synchrophasors buses. Faults at various locations with different fault resistances have been simulated and the results prove that the developed method can be used to locate the fault point and can be used as a backup to main fault location method. Future works also discussed how the method can be improved to get the best and accurate fault location results.
Ijeee 28-32-accurate fault location estimation in transmission linesKumar Goud
Accurate Fault Location Estimation in Transmission Lines
B. Narsimha Reddy Dr. P. Chandra Sekar
Sr. Assistant Professor, Dept. of EEE Associate Professor, Dept. of EEE
Mahatma Gandhi Institute of Technology Mahatma Gandhi Institute of Technology
Hyderabad, TS, India Hyderabad, TS, India
babubnr@gmail.com Pcs_76@rediffmail.com
Abstract: In trendy power transmission systems, the double-circuit line structure is increasingly adopted. However, owing to the mutual coupling between the parallel lines it is quite difficult to style correct fault location algorithms. Moreover, the widely used series compensator and its protecting device introduce harmonics and non-linearity’s to the transmission lines, that create fault location a lot of difficult. To tackle these issues, this thesis is committed to developing advanced fault location strategies for double-circuit and series-compensated transmission lines. Algorithms utilizing thin measurements for pinpointing the situation of short-circuit faults on double-circuit lines square measure planned. By moldering the initial net-work into 3 sequence networks, the bus ohmic resistance matrix for every network with the addition of the citations fault bus may be developed. It’s a perform of the unknown fault location. With the increased bus ohmic resistance matrices the sequence voltage amendment throughout the fault at any bus may be expressed in terms of the corresponding sequence fault current and also the transfer ohmic resistance between the fault bus and the measured bus. Resorting to tape machine the superimposed sequence current at any branch may be expressed with relevancy the pertaining sequence fault current and transfer ohmic resistance terms. Obeying boundary conditions of different fault sorts, four different categories of fault location algorithms utilizing either voltage phasors, or phase voltage magnitudes, or current phasors or section current magnitudes square measure derived. The distinguishing characteristic of the planned methodology is that the information measurements need not stem from the faulted section itself. Quite satisfactory results are obtained victimisation EMTP simulation studies. A fault location rule for series-compensated transmission lines that employs two-terminal asynchronous voltage and current measurements has been implemented. For the distinct cases that the fault happens either on the left or on the right aspect of the series compensator, 2 subroutines square measure developed. In addition, the procedure to spot the proper fault location estimate is represented during this work. Simulation studies disbursed with Matlab Sim Power Systems show that the fault location results square measure terribly correct.
Keywords: Ohmic Resistance, Transmission Lines, PMU, DFR, VCR, EMTP, MOV.
Recent Trends InDigital Differential Protection of Power Transformerijiert bestjournal
Digital protection has several advantages over conventional protection scheme. For protecting
costliest and vital equipment such as transformer, digital schemes have been proposed by several authors in recent
past. This paper throws light on all such efforts and it will help researchers to focus on integrated efforts to protect
transformer in a better and efficient way. Artificial intelligence along with signature and pattern recognition
techniques give much more useful information about happenings in and outside of transformer. Efforts are put by
all concerned with fast, accurate, flexible, reliable and easy to understand scheme of protection. With the advent of
soft computing methods condition monitoring with protection has become on line objective. Keeping all these
state of art techniques of protection, this paper will be a useful resource. Discrimination of several faults external
and internal needs digital signal processing and feature extraction as well. Many algorithms are proposed as
summarized in paper.
The document summarizes research on using an artificial neural network (ANN) approach for fault detection in power transmission lines. It describes training an ANN to recognize normal system conditions from fault conditions based on changes in current and impedance signals. The ANN was trained using the backpropagation algorithm on over 11,000 data points of faults at different locations and inception angles on a simulated 100km transmission line. The trained ANN was able to detect faults with a final error rate of 0.1%, demonstrating the potential for ANNs to enable fast and accurate fault detection compared to conventional relaying techniques.
This paper presents a methodology using
Gravitational Search Algorithm for optimal placement of Phasor
Measurement Units (PMUs) in order to achieve complete
observability of the power system. The objective of proposed
algorithm is to minimize the total number of PMUs at the power
system buses, which in turn minimize installation cost of the PMUs.
In this algorithm, the searcher agents are collection of masses which
interact with each other using Newton’s laws of gravity and motion.
This new Gravitational Search Algorithm based method has been
applied to the IEEE 14-bus, IEEE 30-bus and IEEE 118-bus test
systems. Case studies reveal optimal number of PMUs with better
observability by proposed method.
HEURISTIC BASED OPTIMAL PMU ROUTING IN KPTCL POWER GRIDIAEME Publication
Power system monitoring is an important process in an efficient smart grid. The control centers used in smart grid requires restructuring. State measurements rather than state estimationare pre-requisite for the modern control center. The Phasor Measurement Unit (PMU) measures the synchronized voltage and current parameters. Placement of minimum number of PMUs in a bus system such that the wholes system becomes observable is considered as Optimal PMU Placement (OPP) problem. In this paper, Hybrid Distance Optimization (HDO) algorithm is proposed to reduce the number of PMUs for complete observability along with the minimum length of fiber optic cable required for interconnecting the PMU nodes
Performance Analysis Of PV Interfaced Neural Network Based Hybrid Active Powe...IJERA Editor
This paper presents a comparative analysis of neural network controlled PV interfaced hybrid active power filter designed for harmonic compensation for nonlinear load.The neural network has been chosen for reference current generation because of its fast adaptiveness, simple calculation and high accuracy to eliminate harmonics.This paper shows a novel approach to interface PV array to hybrid active power filter to keep the capacitor voltage stable. To obtain efficient output from PV Array Maximum power point tracking (MPPT) is employed in it. MPPT is able to extract maximum possible power from PV Array of change in atmospheric condition. Simulation and analysis of hybrid active power filter and PV Array is done under nonlinear load, sudden change in load and unbalanced load conditions. The detailed simulation results have been presented to validate the proposed methodology.
Pmu's Placement in power System using AI algorithmsAjay Singh
Abstract:
In today's era, Wide-area monitoring plays a major role in modern power system (smart grid). To monitor this, we need to place Phasor Measurement Units (PMUs) in the system in such a way that the complete observability of the system is achieved. PMUs have the capability that they can provide synchronized measurements of both voltage and current. In this paper, a Minimum Connectivity Based Reduction (MCBR) technique is suggested to place PMUs optimally for complete observability of the system. The proposed MCBR Technique is explained with the help of IEEE bench mark systems. Finally, its performance is compared with existing methodologies.
ANN based fault diagnostic scheme for power transformerMohammad Sohaib
1) Artificial neural networks can be used for fault diagnosis of power transformers based on dissolved gas analysis. Different ANN models are trained to classify fault types based on inputs of different dissolved gas ratios from techniques like Rogers ratio and Duval triangle.
2) A smart fault diagnostic approach uses the outputs of each ANN model to make a normalized decision on fault type. This includes classifications like no fault, thermal fault, arcing, partial discharge, and undetermined fault.
3) The smart fault diagnostic approach can be enhanced by adding another ANN that is trained directly on raw gas concentration data, and integrating its output with the existing approach to improve accuracy of fault classification.
Enhanced Protection Modeling Approach for Power System Transient Stability St...Power System Operation
Accurate protection modelling in power system transient stability studies is required to ensure that reliable conclusions are drawn from such analyses. Typically, protection models available in transient stability programs use only positive sequence quantities such as the positive sequence voltages, currents, etc. to trigger any preventive/corrective actions such as tripping of generators, load-shedding, etc. However, with the increasing penetration of inverter-based resources, these models could prove to be inadequate in some scenarios. The work reported in this paper uses improved modelling practices for protection elements in transient stability studies using sequence/individual phase quantities. This approach does not necessarily require additional data from users and incurs only minimal incremental computational costs. In addition to using the sequence voltages/currents or individual phase voltages/currents for more accurate representation of protection systems, simply monitoring these quantities can also provide useful additional information about the system. Additionally, having access to these quantities could be useful in more accurate modelling of inverter-based resources such as the ability to model converter controls’ protective functions, controls that actively suppress the negative sequence current produced by the inverter, and other such controls that use or control the negative sequence or zero sequence current injections.
This document presents an unsynchronized fault location technique for multisection compound transmission lines. The objective is to identify fault locations on such lines using unsynchronized measurements from both ends of the line, without requiring knowledge of the line parameters. It formalizes the technique in several steps, including manipulating voltage and current measurements based on fault distance and line impedance. The technique then calculates the fault distance over a range of synchronization angles and selects the minimum value. The technique is able to locate faults on both overhead lines and underground cables. It provides fault distances for various single-phase and three-phase faults.
This document provides an overview of artificial neural networks. It defines ANNs as highly interconnected networks of neurons inspired by the human brain. The document then discusses key aspects of ANNs like biological neurons, network architecture, learning rules, activation functions, and specific ANN models including perceptrons, backpropagation networks, associative memories, and Hopfield networks. It provides details on the basic building blocks and functioning of various ANN concepts.
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
A Fault Detection and Classification Method for SC Transmission Line Using Ph...paperpublications3
Abstract: In this paper, fault detection and classification for Series Compensated Line (SCL) using phasor measurement unit is presented. The algorithm presented in this paper uses the PMU synchronized measurements and not depends on the data to be provided by the electricity utility. The compensated line parameters and Thevenin’s equivalent (TE) of the system at SCL terminals are calculated online, using three independent sets of pre-fault phasor measurements. The accuracy of fault location is performed with respect to fault location/position, types of fault, fault angle. The accuracy of the algorithm is simulated in MATLAB for 9-bus transmission system.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...ijfls
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And
Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed.
The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail
Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A
Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection,
Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception
Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The
Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is
Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
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.
Intelligent Fault Identification System for Transmission Lines Using Artifici...IOSR Journals
Transmission and distribution lines are vital links between generating units and consumers. They are
exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be
immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults
on electric power transmission lines using artificial neural networks. A feed forward neural network is
employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number
of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks
in each step. The developed neural network is capable of detecting single line to ground and double line to
ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial
neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory
performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system.
Hardware implementation of neural network is done on TMS320C6713.
Transient Stability Assessment and Enhancement in Power SystemIJMER
This document discusses transient stability assessment and enhancement in power systems. It first introduces transient stability and its importance. It then describes using PSAT software to analyze the IEEE 39-bus test system and calculate critical clearing times (CCTs) for different faults to assess stability. An artificial neural network is trained to predict CCTs at different operating points. Finally, particle swarm optimization is used to find the optimal placement of a thyristor controlled series capacitor to enhance stability by minimizing real power losses, increasing several CCTs above 0.1 seconds.
This document describes a special project on using an artificial neural network (ANN) for load flow studies of the MSU-IIT electrical system. The objectives are to model the power system as a 5-bus system, evaluate bus voltages using a power flow program under different loads, train an ANN using the power flow results, and validate the ANN's accuracy by comparing its results to the power flow program. The document reviews literature on load flow studies, numerical methods, ANNs, and discusses how ANNs could provide faster and more accurate solutions to complex load flow problems compared to numerical methods.
"Use of PMU data for locating faults and mitigating cascading outage"Power System Operation
This document summarizes two methods presented in the paper: 1) A fault location method that uses sparse PMU data and electromechanical wave propagation to detect faults on transmission lines. It introduces a decision tree classifier to analyze voltage measurements and locate faults with high accuracy. 2) A controlled islanding scheme to predict and mitigate cascading outages. It uses spectral clustering to partition the system and suggest switching actions to create stable islands with minimum load shedding. The methods were tested on simulated systems and show potential to improve grid monitoring, fault response and prevention of blackouts.
Backpropagation Neural Network Modeling for Fault Location in Transmission Li...ijeei-iaes
In this topic research was provided about the backpropagation neural network to detect fault location in transmission line 150 kV between substation to substation. The distance relay is one of the good protective device and safety devices that often used on transmission line 150 kV. The disturbances in power system are used distance relay protection equipment in the transmission line. However, it needs more increasing large load and network systems are increasing complex. The protection system use the digital control, in order to avoid the error calculation of the distance relay impedance settings and spent time will be more efficient. Then backpropagation neural network is a computational model that uses the training process that can be used to solve the problem of work limitations of distance protection relays. The backpropagation neural network does not have limitations cause of the impedance range setting. If the output gives the wrong result, so the correct of the weights can be minimized and also the response of galat, the backpropagation neural network is expected to be closer to the correct value. In the end, backpropagation neural network modeling is expected to detect the fault location and identify operational output current circuit breaker was tripped it. The tests are performance with interconnected system 150 kV of Riau Region.
The power supply system is completely hooked into three major parts. First one is generation, second one is
transmission and the last one is distribution of electricity supply at the range of 415V to 400V approx. But while
the fault occurs it affects other lines additionally, and this causes difficulties for local people and additionally
perturb the flow of current in different areas. This eccentric and perturbed supply of nuisance is very
hazardous as it cannot be ceased when it comes to equal distribution of electricity. The area suffering from
faults and the other both get affected. So to stop all these we have implemented this project of Coordination of
over current relay utilising optimisation technique. We have utilised crow search algorithms with Kennedy as
swarm perspicacity algorithms which are very auxiliary in storing excess electricity supply and can be used
when needed. With the avail of this we can renovate the potency supply and this will conclusively implement
our main objective of this project.
Wide Area Fault Location for Power Transmission Network using Reactance Based...Muhd Hafizi Idris
Download here: https://www.researchgate.net/publication/332441499_Wide_Area_Fault_Location_for_Power_Transmission_Network_using_Reactance_Based_Method?_sg=Tkk3ur2Kc3XGh3JHwtJdPM3IdJJx_K42N3Zu9kX_ECutHW5j91ExIMtrJFOui4E-RikSYmuYR0uZWEEVHoSaDTPZuRvC29V6GzZ5g9BS.GnmzKNF1XN22czjk5npta57bMn8D2KxxwQsAMEPlK7abE5qGykkxj8CgUcnYHlzpKEZST1ujqv7avTquOi7Aug
With the advancements in smart grid, communication technology, intelligent electronic device and substation automation, wide area applications for monitoring, protection, control and fault location becoming focused nowadays and improved from time to time. This research focuses on using wide area synchrophasor measurements for fault location in transmission network which acts as a backup to conventional fault location method. Simple reactance based methods together with a developed rules system are used to locate the possible affected line and its fault location. Using the developed rules and algorithm, fault location impedance will be compared at each synchrophasor bus connected lines for different fault types, then between connected lines and finally between synchrophasors buses. Faults at various locations with different fault resistances have been simulated and the results prove that the developed method can be used to locate the fault point and can be used as a backup to main fault location method. Future works also discussed how the method can be improved to get the best and accurate fault location results.
Ijeee 28-32-accurate fault location estimation in transmission linesKumar Goud
Accurate Fault Location Estimation in Transmission Lines
B. Narsimha Reddy Dr. P. Chandra Sekar
Sr. Assistant Professor, Dept. of EEE Associate Professor, Dept. of EEE
Mahatma Gandhi Institute of Technology Mahatma Gandhi Institute of Technology
Hyderabad, TS, India Hyderabad, TS, India
babubnr@gmail.com Pcs_76@rediffmail.com
Abstract: In trendy power transmission systems, the double-circuit line structure is increasingly adopted. However, owing to the mutual coupling between the parallel lines it is quite difficult to style correct fault location algorithms. Moreover, the widely used series compensator and its protecting device introduce harmonics and non-linearity’s to the transmission lines, that create fault location a lot of difficult. To tackle these issues, this thesis is committed to developing advanced fault location strategies for double-circuit and series-compensated transmission lines. Algorithms utilizing thin measurements for pinpointing the situation of short-circuit faults on double-circuit lines square measure planned. By moldering the initial net-work into 3 sequence networks, the bus ohmic resistance matrix for every network with the addition of the citations fault bus may be developed. It’s a perform of the unknown fault location. With the increased bus ohmic resistance matrices the sequence voltage amendment throughout the fault at any bus may be expressed in terms of the corresponding sequence fault current and also the transfer ohmic resistance between the fault bus and the measured bus. Resorting to tape machine the superimposed sequence current at any branch may be expressed with relevancy the pertaining sequence fault current and transfer ohmic resistance terms. Obeying boundary conditions of different fault sorts, four different categories of fault location algorithms utilizing either voltage phasors, or phase voltage magnitudes, or current phasors or section current magnitudes square measure derived. The distinguishing characteristic of the planned methodology is that the information measurements need not stem from the faulted section itself. Quite satisfactory results are obtained victimisation EMTP simulation studies. A fault location rule for series-compensated transmission lines that employs two-terminal asynchronous voltage and current measurements has been implemented. For the distinct cases that the fault happens either on the left or on the right aspect of the series compensator, 2 subroutines square measure developed. In addition, the procedure to spot the proper fault location estimate is represented during this work. Simulation studies disbursed with Matlab Sim Power Systems show that the fault location results square measure terribly correct.
Keywords: Ohmic Resistance, Transmission Lines, PMU, DFR, VCR, EMTP, MOV.
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The document summarizes research on using an artificial neural network (ANN) approach for fault detection in power transmission lines. It describes training an ANN to recognize normal system conditions from fault conditions based on changes in current and impedance signals. The ANN was trained using the backpropagation algorithm on over 11,000 data points of faults at different locations and inception angles on a simulated 100km transmission line. The trained ANN was able to detect faults with a final error rate of 0.1%, demonstrating the potential for ANNs to enable fast and accurate fault detection compared to conventional relaying techniques.
This paper presents a methodology using
Gravitational Search Algorithm for optimal placement of Phasor
Measurement Units (PMUs) in order to achieve complete
observability of the power system. The objective of proposed
algorithm is to minimize the total number of PMUs at the power
system buses, which in turn minimize installation cost of the PMUs.
In this algorithm, the searcher agents are collection of masses which
interact with each other using Newton’s laws of gravity and motion.
This new Gravitational Search Algorithm based method has been
applied to the IEEE 14-bus, IEEE 30-bus and IEEE 118-bus test
systems. Case studies reveal optimal number of PMUs with better
observability by proposed method.
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Accurate protection modelling in power system transient stability studies is required to ensure that reliable conclusions are drawn from such analyses. Typically, protection models available in transient stability programs use only positive sequence quantities such as the positive sequence voltages, currents, etc. to trigger any preventive/corrective actions such as tripping of generators, load-shedding, etc. However, with the increasing penetration of inverter-based resources, these models could prove to be inadequate in some scenarios. The work reported in this paper uses improved modelling practices for protection elements in transient stability studies using sequence/individual phase quantities. This approach does not necessarily require additional data from users and incurs only minimal incremental computational costs. In addition to using the sequence voltages/currents or individual phase voltages/currents for more accurate representation of protection systems, simply monitoring these quantities can also provide useful additional information about the system. Additionally, having access to these quantities could be useful in more accurate modelling of inverter-based resources such as the ability to model converter controls’ protective functions, controls that actively suppress the negative sequence current produced by the inverter, and other such controls that use or control the negative sequence or zero sequence current injections.
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This document provides an overview of artificial neural networks. It defines ANNs as highly interconnected networks of neurons inspired by the human brain. The document then discusses key aspects of ANNs like biological neurons, network architecture, learning rules, activation functions, and specific ANN models including perceptrons, backpropagation networks, associative memories, and Hopfield networks. It provides details on the basic building blocks and functioning of various ANN concepts.
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This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
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A Fault Detection and Classification Method for SC Transmission Line Using Ph...paperpublications3
Abstract: In this paper, fault detection and classification for Series Compensated Line (SCL) using phasor measurement unit is presented. The algorithm presented in this paper uses the PMU synchronized measurements and not depends on the data to be provided by the electricity utility. The compensated line parameters and Thevenin’s equivalent (TE) of the system at SCL terminals are calculated online, using three independent sets of pre-fault phasor measurements. The accuracy of fault location is performed with respect to fault location/position, types of fault, fault angle. The accuracy of the algorithm is simulated in MATLAB for 9-bus transmission system.
WAVELET- FUZZY BASED MULTI TERMINAL TRANSMISSION SYSTEM PROTECTION SCHEME IN ...ijfls
In This Paper, A New Protection Scheme In The Areas Of Accurate Fault Detection, Classification And
Location Estimation For Multi Terminal Transmission System Compensated With Statcom Is Proposed.
The Fault Indices Of All The Phases At All The Terminals Are Obtained By Analyzing The Detail
Coefficients Of Current Signals Through Bior 1.5 Mother Wavelet. The Complete Digital Simulation Of A
Transmission System With Statcom Is Performed Using Matlab /Simulink For Fault Detection,
Classification, And Faulty Terminal Identification With Variations In Fault Distance And Fault Inception
Angle For All Types Of Faults And Fuzzy Inference System Is Used To Estimate The Fault Location. The
Protection Scheme Yielded Accurate Results Within Half Cycle And Show That The Above Scheme Is
Suitable For Multi Terminal Transmission System With And Without Statcom Compensation.
IRJET- Three Phase Line Fault Detection using Artificial Neural NetworkIRJET Journal
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Intelligent Fault Identification System for Transmission Lines Using Artifici...IOSR Journals
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Transient Stability Assessment and Enhancement in Power SystemIJMER
This document discusses transient stability assessment and enhancement in power systems. It first introduces transient stability and its importance. It then describes using PSAT software to analyze the IEEE 39-bus test system and calculate critical clearing times (CCTs) for different faults to assess stability. An artificial neural network is trained to predict CCTs at different operating points. Finally, particle swarm optimization is used to find the optimal placement of a thyristor controlled series capacitor to enhance stability by minimizing real power losses, increasing several CCTs above 0.1 seconds.
Double Circuit Transmission Line Protection using Line Trap & Artificial Neur...IRJET Journal
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Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching A...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Differential equation fault location algorithm with harmonic effects in power...TELKOMNIKA JOURNAL
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Distance protection scheme for transmission line using back propagation neura...eSAT Publishing House
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
Wavelet based detection and location of faults in 400kv, 50km Underground Po...ijceronline
This document presents a method for detecting and locating faults in underground power cables using wavelet transforms. A 400kV, 50km underground cable system is modeled in MATLAB Simulink. Various single-phase, two-phase, and three-phase faults are simulated at distances of 25km and 50km from the measurement point. Voltage and current signals are analyzed using continuous wavelet transforms to detect and locate faults. Simulation results show the method can accurately estimate fault locations, with errors generally under 7%. The method is capable of determining fault type and location for both transmission and distribution cables.
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Fault analysis is the process of determining the magnitude of fault voltage and current during the occurrence of different types of fault in electrical power system. Transmission line fault analysis is usually done for both symmetrical and unsymmetrical faults. Symmetrical faults are called three-phase balance fault while unsymmetrical faults include: single line-to-ground, line-to-line, and double line-to-ground faults. In this research, bus impedance matrix method for fault analysis is presented. Bus impedance matrix approach has several advantages over Thevenin’s equivalent method and other conventional approaches. This is because the off-diagonal elements represent the transfer impedance of the power system network and helps in calculating the branch fault currents during a fault. Analytical and simulation approaches on a single line-to-ground fault on 3-bus power system network under bolted fault condition were used for the study. Both methods were compared and result showed negligible deviation of 0.02% on the average. The fault currents under bolted condition for the single line-to-ground fault were found to be 4. 7244p.u while the bus voltage is 0. 4095p.u for buses 1 and 2 respectively and 0. 00p.u for bus 3 since the fault occurred at this bus. Therefore, there is no need of burdensomely connecting the entire three sequence network during fault analysis in electrical power system.
ANN Approach for Fault Classification in Induction Motors using Current and V...IRJET Journal
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Reliability analysis of pmu using hidden markov modelamaresh1234
As modern electric power systems are transforming into smart grids, real time wide area monitoring system (WAMS) has become an essential tool for operation and control. With the increasing applications of WAMS for on-line stability analysis and control in smart grids, phasor measurement unit (PMU) is becoming a key element in wide area measurement system and the consequence of the failure of PMU is very severe and may cause a black out. Therefore reliable operation of PMU is very much essential for smooth functioning of the power system. This thesis is focused mainly on evaluating the reliability of PMU using hidden Markov model. Firstly, the probability of given observation sequence is obtained for the individual modules and PMU as a whole using forward and backward algorithm. Secondly, the optimal state sequence each module passes through is found. Thirdly, the parameters of the hidden Markov model are re-estimated using Baum-Welch algorithm.
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.
IRJET- Low Cost Harmonic Measurement using Arduino UNOIRJET Journal
This document describes a low-cost system for measuring harmonics using an Arduino Uno. Harmonic measurement is important for power quality monitoring and control. The system quantizes and samples the input signal to detect total harmonic distortion using Arduino's fast Fourier transform implementation. It uses a potential divider and Arduino to build a portable device for measuring power line frequency changes and quality interference in real-time. This allows immediate detection of the power system's operational state.
Fuzzy Logic-Based Fault Classification for Transmission Line AnalysisIRJET Journal
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This document proposes an adaptive scheme for measuring signal parameters for generator monitoring and protection using adaptive orthogonal filters. The algorithm adapts the filter data window length and coefficients according to a coarse estimation of the signal frequency, allowing accurate measurements over a wide frequency band including during generator start-up. Measured signals are also used to train artificial neural networks to classify generator operation modes and detect phenomena like pole slipping and out-of-step conditions. The document describes the adaptive measurement scheme, provides an example using signals from simulations, and discusses using a genetic algorithm to optimize the design of an artificial neural network-based out-of-step protection system.
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This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.