High impedance fault detection in distribution feeders using extended kalman filter and support vector machine

2010 ◽  
Vol 20 (3) ◽  
pp. 382-393 ◽  
Author(s):  
S. R. Samantaray ◽  
P. K. Dash
Keyword(s):  
Support Vector Machine ◽  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Support Vector ◽  
High Impedance ◽  
High Impedance Fault

scholarly journals Autonomous Monitoring of Line-to-Line Faults in Photovoltaic Systems by Feature Selection and Parameter Optimization of Support Vector Machine Using Genetic Algorithms

2020 ◽  
Vol 10 (16) ◽  
pp. 5527 ◽  
Author(s):  
Aref Eskandari ◽  
Jafar Milimonfared ◽  
Mohammadreza Aghaei ◽  
Angèle H.M.E. Reinders
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Parameter Optimization ◽  
Kernel Functions ◽  
Normal Operation ◽  
Support Vector ◽  
Svm Classifier ◽  
Pv Systems ◽  
High Impedance ◽  
High Impedance Fault

Photovoltaic (PV) monitoring and fault detection are very crucial to enhance the service life and reliability of PV systems. It is difficult to detect and classify the faults at the Direct Current (DC) side of PV arrays by common protection devices, especially Line-to-Line (LL) faults, because such faults are not detectable under high impedance fault and low mismatch conditions. If these faults are not diagnosed, they may significantly reduce the output power of PV systems and even cause fire catastrophe. Recently, many efforts have been devoted to detecting and classifying LL faults. However, these methods could not efficiently detect and classify the LL faults under high impedance and low mismatch. This paper proposes a novel fault diagnostic scheme in accordance with the two main stages. First, the key features are extracted via analyzing Current–Voltage (I–V) characteristics under various LL fault events and normal operation. Second, a genetic algorithm (GA) is used for parameter optimization of the kernel functions used in the Support Vector Machine (SVM) classifier and feature selection in order to obtain higher performance in diagnosing the faults in PV systems. In contrast to previous studies, this method requires only a small dataset for the learning process and it has a higher accuracy in detecting and classifying the LL fault events under high impedance and low mismatch levels. The simulation results verify the validity and effectiveness of the proposed method in detecting and classifying of LL faults in PV arrays even under complex conditions. The proposed method detects and classifies the LL faults under any condition with an average accuracy of 96% and 97.5%, respectively.

scholarly journals Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882509 ◽  
Author(s):  
Zaopeng Dong ◽  
Xin Yang ◽  
Mao Zheng ◽  
Lifei Song ◽  
Yunsheng Mao
Keyword(s):  
Support Vector Machine ◽  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
State Vector ◽  
Support Vector ◽  
Response Model ◽  
Model Based ◽  
Discrete Response ◽  
Marine Vehicle

To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm.

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