The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The inrush current occurs when transformers are initially energized at no load, which lead to mal-operations of protective relays and issues of power quality. However, it is very necessary to discriminate between inrush condition and faulty condition for modern protection of power transformer. This paper presents an alternative approach of using artificial neural network for discrimination and detection...
This paper proposes a detection method for open-circuit faults in a 3-phase uncontrolled rectifier that forms the input stage of a single-phase current controlled power converter. The rectifier is driven by a 3-phase wind generator with variable amplitude and frequency. The algorithm is based on fault signatures embedded within the output voltage ripple of the rectifier. It is demonstrated that seven...
This paper addresses the problem of extracting effective features for the analysis of underlying causes of power quality (PQ) disturbances. For each underlying cause, we define and extract a set of features based on analysis of voltage/current waveforms or the combination of them. The proposed feature sets are then used for building a rule-based classification framework for automatic identification...
This paper presents a novel intelligent autoreclosure technique to discriminate temporary faults from permanent faults, and accurately determine fault extinction time. A variety of fault simulations are carried out on a specified transmission line on the standard IEEE 9-bus electric power system using MATLAB/SimPowerSytems. FFT and Prony analysis methods are employed to extract data features from...
This paper proposed a novel identification method for voltage sags (dips) based on Hilbert-Huang transform (HHT). Different voltage sag has distinct characters of fault time, three-phase amplitude and harmonics. And HHT is an excellent time-frequency representation and very suitable for feature extraction of voltage sags. The time-frequency absolute values of the instantaneous frequency differential...
This paper presents a method to discriminate a temporary fault from a permanent one in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training...
In this paper, a fault diagnostic system in a multilevel- inverter using a adaptive back-propagation neural network is developed. An adaptive back propagation neural network classification is applied to the fault diagnosis of a MLI system to avoid the difficulties in using mathematical models. A multilayer perceptron (MLP) network with 40 - 12 - 8 architecture is used to identify the type and location...
This paper presents a new algorithm for transformer differential protection, based on pattern recognition of the instantaneous differential currents. A decision logic by wavelet Transform has been devised using extracted feature from differential currents due to internal fault and inrush currents. In this logic, diagnosis criterion is based on time difference of amplitudes of wavelet coefficients...
This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard error back-propagation, Levenberg Marquardt algorithm and resilient back-propagation...
Modeling study provides a more effective and flexible way for research of high impedance fault (HIF) detection. An accurate model which can reveal most features of HIF is very important. In this paper, based on extensive investigation into documented test results, a set of most common and agreeable features of HIF has been summarized: the arc features, the non-linearity, the time-varied resistance...
A novel fault line detection method based on rough set neural network is proposed to avoid the longtime training and the complicated model structure for the neural network based fault line detection model. Through performing single-phase-to-earth fault experiments by means of the ATP-EMTP simulation program, the zero sequence currents of every line are obtained. The fault features are extracted from...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.