Arcing High Impedance Fault Detection Using Real Coded Genetic Algorithm

Safety and reliability are two of the most important aspects of electric power supply systems. Sensitivity and robustness to detect and isolate faults can influence the safety and reliability of such systems. Overcurrent relays are generally used to protect the high voltage feeders in distribution systems. Downed conductors, tree branches touching conductors, and failing insulators often cause high-impedance faults in overhead distribution systems. The levels of currents of these faults are often much smaller than detection thresholds of traditional ground fault detection devices, thus reliable detection of these high impedance faults is a real challenge. With modern signal processing techniques, special hardware and software can be used to significantly improve the reliability of detection of certain types of faults. This paper presents a new method for detecting High Impedance Faults (HIF) in distribution systems using real coded genetic algorithm (RCGA) to analyse the harmonics and phase angles of the fault current signals. The method is used to discriminate HIFs by identifying specific events that happen when a HIF occurs

Modelling arcing high impedances faults in relation to the physical processes in the electric arc

There is an increasing demand for more detailed and accurate modelling techniques for predicting transient response of power systems caused in particular by high impedance arcing faults (HIF). This is particularly so in relation to the design and development of improved equipment and new protection techniques. Accurate prediction of fault transients requires detailed and comprehensive representation of all components in a system, while the transient studies need to be conducted into the frequency range well above the normal power frequency. The HIF is a very complex phenomenon and exhibits very high nonlinear behaviour. The most distinctive characteristics are nonlinearity and asymmetry. The nonlinearity arises from the fact that the voltage-current characteristic curve of the HIF is itself nonlinear. It is observed that the fault current has different waveforms for positive and negative half cycles which is called asymmetry. The nonlinearity and asymmetry exist in every cycle after HIF. In order to obtain a model for a HIF, it is necessary to develop a model that gives the above mentioned characteristics, as well as the harmonic content of the HIF

The evolution of high impedance fault modeling

There is an increasing demand for more detailed and accurate modelling techniques for predicting transient response of power systems caused in particular by high impedance arcing faults (HIF). This is particularly so in relation to the design and development of improved equipment and new protection techniques. Accurate prediction of fault transients requires detailed and comprehensive representation of all components in a system, while the transient studies need to be conducted into the frequency range well above the normal power frequency. The HIF is a very complex phenomenon and exhibits highly nonlinear behaviour. The most distinctive characteristics are nonlinearity and asymmetry. The nonlinearity arises from the fact that the voltage-current characteristic curve of the HIF is itself nonlinear. It is also observed that the fault current has different waveforms for positive and negative half cycles, which is called asymmetry. The nonlinearity and asymmetry exist in every cycle after the HIF. In order to obtain a good representation of a HIF, it is necessary to develop a model that gives the above mentioned characteristics, as well as the harmonic content of the HIF. This paper introduces a new HIF model, it also reviews and summarizes some of the methods for modelling high impedance faults and the developments which led to them

A Digital Technique for Online Identification and Tracking of Power System Harmonics Based on Real Coded Genetic Algorithm

Current and voltage waveforms of a distribution or a transmission system are not pure sinusoids. There are distortions in these waveforms that consist of a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents an enhanced measurement scheme for identification and tracking of harmonics in power system. The proposed technique is not limited to stationary waveforms, but can also estimate harmonics in waveforms with time-varying amplitudes. This paper presents a new method based on Real Coded Genetic Algorithm, which is a technique for optimization inspired by genetics and natural evolution. The algorithm was tested using simulated data, and effects of sampling rate studied. Results are reported and discussed