Application of the LM-HLP Neural Network to Automatic Smartphone Test System

2017 ◽  
pp. 217-225 ◽  
Author(s):  
Wei-Ting Hsu ◽  
Chia-Chi Lu ◽  
Jih-Gau Juang
Keyword(s):  
Neural Network ◽  
Test System

scholarly journals Introduction and Analysis of a Method for the Investigation of QCD-Like Tree Data

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Marko Jercic ◽  
Ivan Jercic ◽  
Nikola Poljak
Keyword(s):  
Neural Network ◽  
Iterative Method ◽  
Probability Distributions ◽  
Test System ◽  
Jet Formation ◽  
Decay Probability ◽  
Decay Channels ◽  
System Of Particles ◽  
Tree Data ◽  
True System

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.

Artificial Neural Network Application in Optimal Coordination of Directional Overcurrent Protective Relays in Electrical Mesh Distribution Network

2015 ◽  
Vol 785 ◽  
pp. 48-52 ◽  
Author(s):  
Osaji Emmanuel ◽  
Mohammad Lutfi Othman ◽  
Hashim Hizam ◽  
Muhammad Murtadha Othman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Time ◽  
Short Circuit ◽  
Distribution Networks ◽  
Test System ◽  
Wrong Response ◽  
Optimal Coordination ◽  
Artificial Neural ◽  
Short Circuit Fault

Directional Overcurrent relays (DOCR) applications in meshed distribution networks (MDN), eliminate short circuit fault current due to the topographical nature of the system. Effective and reliable coordination’s between primary and secondary relay pairs ensures effective coordination achievement. Otherwise, the risk of safety of lives and installations may be compromised alongside with system instability. This paper proposes an Artificial Neural Network (ANN) approach of optimizing the system operation response time of all DOCR within the network to address miscoordination problem due to wrong response time among adjacent DOCRs to the same fault. A modelled series of DOCRs in a simulated IEEE 8-bus test system in DigSilent Power Factory with extracted data from three phase short circuit fault analysis adapted in training a custom ANN. Hence, an improved optimized time is produced from the network output to eliminate miscoordination among the DOCRs.

A neural network based diagnostic test system for armored vehicle shock absorbers

1996 ◽  
Vol 11 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Patrick Sincebaugh ◽  
William Green ◽  
Gerard Rinkus
Keyword(s):  
Neural Network ◽  
Diagnostic Test ◽  
Test System ◽  
Shock Absorbers ◽  
Armored Vehicle

Based on RBF Neural Network Modeling of Well Test System

2014 ◽  
Vol 535 ◽  
pp. 606-609
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Effective Means ◽  
Test System ◽  
Neural Network Modeling ◽  
Well Test ◽  
Fast Learning ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Toolbox

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.

scholarly journals Radial Basis Function Neural Network Application to Measurement and Control of Shunt Reactor Overvoltages Based on Analytical Rules

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Early Stage ◽  
Rbf Neural Network ◽  
Test System ◽  
Worst Case ◽  
Shunt Reactor ◽  
Radial Basis

This paper presents an artificial intelligence application to measure switching overvoltages caused by shunt reactor energization by applying analytical rules. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a reactor with residual flux. A radial basis function (RBF) neural network has been used to estimate the overvoltages due to reactor energization. Equivalent circuit parameters of network have been used as artificial neural network (ANN) inputs; thus, RBF neural network is applicable to every studied system. The developed ANN is trained with the worst case of the switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can measure the peak values and duration of switching overvoltages with good accuracy.

scholarly journals Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

2019 ◽  
Vol 11 (13) ◽  
pp. 3586 ◽  
Author(s):  
Oyeniyi Akeem Alimi ◽  
Khmaies Ouahada ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Power System ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Hybrid Algorithm ◽  
Test System ◽  
Support Vector ◽  
Detection Accuracy ◽  
Bus System

In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

scholarly journals On-line determination of transient stability status using multilayer perceptron neural network

2018 ◽  
Vol 69 (1) ◽  
pp. 58-64 ◽  
Author(s):  
Emmanuel Asuming Frimpong ◽  
Philip Yaw Okyere ◽  
Johnson Asumadu
Keyword(s):  
Neural Network ◽  
New England ◽  
Multilayer Perceptron ◽  
Transient Stability ◽  
Test System ◽  
Frequency Deviation ◽  
Maximum Frequency ◽  
Two Samples ◽  
Using Data ◽  
The Stability

Abstract A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

scholarly journals Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems

2021 ◽  
Author(s):  
Nathan Elias Maruch Barreto ◽  
Ciro Monteiro Baer ◽  
Mateus Jaensen Daros ◽  
Marlon Alexsandro Fritzen ◽  
Guilherme Schneider de Oliveira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Systems ◽  
Power System ◽  
Test System ◽  
Measurement Unit ◽  
Two Phase ◽  
Advantages And Disadvantages ◽  
Phasor Measurement ◽  
Artificial Neural

This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.

scholarly journals Radial Basis Function Neural Network Application to Power System Restoration Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Test System ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
Radial Basis ◽  
System Restoration

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.

Sign in / Sign up

Export Citation Format

Share Document