Intelligent Control For Locating Fault in Transmission Lines

This paper presents a new approach for locating fault in transmission line using intelligent control relaying. Fault must be detected at its inception by issuing an output signal indicating this condition. Neural network approach for locating fault can be posed as a pattern-recognition to recognize pure sinusoidal signals as indicators of a normal system condition; abrupt changes of amplitude, phase, or the presence of transient components as indicators of fault. This method uses the fundamental frequency components of voltage and current basically current at pre-fault and post fault condition, measured at each phase from any one end of the selected power system. In this approach the data sets were trained using the available data from the system which comprises of different fault types data, and fault inception angles. This approach of locating fault using intelligent control can be used for supporting a new generation of very high speed protective relaying system

Enhancing reliability in passive anti-islanding protection schemes for distribution systems with distributed generation

This thesis introduces a new approach to enhance the reliability of conventional passive anti-islanding protection scheme in distribution systems embedding distributed generation. This approach uses an Islanding-Dedicated System (IDS) per phase which will be logically combined with the conventional scheme, either in blocking or permissive modes. Each phase IDS is designed based on data mining techniques. The use of Artificial Neural Networks (ANNs) enables to reach higher accuracy and speed among other data mining techniques. The proposed scheme is trained and tested on a practical radial distribution system with six-1.67 MW Doubly-Fed Induction Generators (DFIG-DGs) wind turbines. Various scenarios of DFIG-DG operating conditions with different types of disturbances for critical breakers are simulated. Conventional passive anti-islanding relays incorrectly detected 67.3% of non-islanding scenarios. In other words, the security is as low as 32.3%. The obtained results indicate that the proposed approach can be used to theoretically increase the security to 100%. Therefore, the overall reliability of the system is substantially increased

Simulation Analysis of a Power System Protection using Artificial Neural Network

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The aim of this Paper is to develop a software module acting as a protective relay using neural network techniques. The Artificial Neural Network (ANN) software developed module employs the back-propagation method to recognize the waveform patterns of impedance in a transmission line. The input waveforms are generated using PSCAD. The generated waveforms then are used as training and testing data for the ANN software. The ANN software is simulated using the Neural Network Toolbox. The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms.DOI:http://dx.doi.org/10.11591/ijece.v3i1.193

Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems

Conventional automatic reclosures blindly operate for permanent, semi-permanent or transient faults on an overhead line without any discrimination after allowing some estimated time delay. Reclosing onto a line with uncleared fault often results in, not only loss of stability and synchronism but also damage to system equipments, as a consequence. The thesis focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinctiontime in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. To get data for training the neural networks, a range of faults are simulated on two case studies -single machine -infinite bus model (connected via EHVtransmission line) and a benchmark IEEE 9-bus electric system. The spectra of the fault voltage data are analyzed using Fast Fourier Transform, and it has been found out that the DC, the fundamental and the first four harmonic components can sufficiently and uniquely represent the condition of each fault. In each case study, the neural network is fed with the normalized energies of the DC, the fundamental and the first four harmonics of the faulted voltages, effectively trained with a set of training data, and verified with a dedicated testing data obtained from fault voltage signals generated on IEEE 14-bus electric system model. The results show the efficacy of the developed adaptive automatic reclosing scheme. This effectively means it is possible to avoid reclosing before any fault on a transmission line (be it temporary or permanent) is totally cleared

Application of ANFIS for Distance Relay Protection in Transmission Line

The techniques hybrid intelligent was introduced in transmission protection that usage in electric power systems. There was applied ANFIS for distance relay protection particularly for transmission line. If a fault occurs during the transmission line identification caused by unwanted fault thus the power delivery to the consumer becomes not going well. Therefore, it would need to provide an alternative solution to fix this problem. The objective of this paper uses impedance transmission line to determine how long the channel spacing will be protected by distance relay. It has been distance relays when fault occurs in transmission line with the application Sugeno ANFIS. The simulation shows it excellent testing results can be contributed to an alternate algorithm that it has good performance to protecting system in transmission line. This application used by using software Matlab

Neural Systems for solving the inverse problem of recovering the Primary Signal Waveform in potential transformers

The inverse problem of recovering the potential transformer primary signal waveform using secondary signal waveform and information about the secondary load is solved here via two inverse neural network models. The first model uses two recurrent neural networks trained in an off-line mode. The second model is designed with the use a Dynamic Evolving Neural-Fuzzy Interface System (DENFIS) and suited for on-line application and integration into existing protection algorithms as a parallel module. It has the ability of learning and adjusting its structure in an on-line mode to reflect changes in the environment. The model is suited for real time applications and improvement of protection relay operation. The two models perform better than any existing and published models so far and are useful not only for the reconstruction of the primary signal, but for predicting the signal waveform for some time steps ahead and thus for estimating the drifts in the incoming signals and events

Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section

Design and implementation of ANN based phase comparators applied to transmission line protection

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The reliability and efficiency of ANN based distance relays is improving with the developing digital technologies. There are, however, some inherent deficiencies that still exist in the way these relays are designed. This research addresses some of these issues and proposes an improved protective relaying scheme. The traditional ANN distance relay designs use parameter estimation algorithms to determine the phasors of currents and voltages. These phasors are used as inputs to determine the distance of a fault from relay location. The relays are trained and tested on this criterion; however, no specific relay characteristic has been defined. There is a need for development of a new methodology that will enable designing of an ANN that works as a generic distance relay with clearly defined operating boundary. This research work presents a modified distance relaying algorithm that has been combined with a neural network approach to eliminate the use of phasors. The neural network is trained to recognize faults on basis of a specific relay characteristic. The algorithm is flexible and has been extended for the design of other relays. The neural network has been trained using pure sinusoidal values and has been tested on a 17-bus power system simulated in PSCAD. The training and testing of the neural network on different systems ensures that the relay is generic in nature. The proposed relay can be used on any transmission line without re-training the neural network. The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries