This paper summarizes diverse concepts for the next generation of power distribution system. The objective is to bring distribution engineering more closely aligned to smart grid philosophy. Issues of design, operation, and control are discussed with regard to new system theoretic as well as component/materials advances. In particular, two transmission engineering techniques are modified for use in distribution engineering: state estimation, and locational marginal pricing. The impact of electronic control in distribution systems is discussed. Because education and training have a great impact on distribution engineering, these topics are discussed as well.

The Next Generation of Power Distribution Systems

A high impedance fault (HIF) is characterized by a small, nonlinear, random, unstable, and widely varying fault current in a power distribution system. HIFs draw very low fault currents, and hence are not always effectively cleared by conventional overcurrent relays. Various schemes are proposed to detect such faults. This paper presents a method to detect HIFs using a tool based on mathematical morphology (MM). The method is implemented alongside the conventional overcurrent relay at the substation to improve the performance of this relay in detecting HIFs. It is rigorously tested on standard test systems using PSCAD/EMTDC® to generate test waveforms, and Matlab® to implement the method. Simulation results show that the proposed method is fast, secure, and dependable.

Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology

The measured values of current harmonics at a staged high-impedance ground fault on sandy soil are presented. The measured low-frequency spectrum is compared with current harmonics recorded continuously for one week at the substation. This comparison was carried out to determine to what extent 120 Hz and 180 Hz components can be used to help detect a high-impedance fault. The field measurements are supported by a simple theoretical model and laboratory measurements. It is concluded that, for the studied feeder, detection of high-impedance arcing faults may be possible by monitoring of the second-harmonic current. >

High impedance fault arcing on sandy soil in 15 kV distribution feeders: contributions to the evaluation of the low frequency spectrum

This paper presents a high impedance fault (HIF) detection method based on decision trees (DTs). The features of HIF, which are the inputs of DTs, are those well-known ones, including current [in root mean square (rms)], magnitudes of the second, third, and fifth harmonics, and the phase of the third harmonics. The only measurements needed in the proposed method are the current signals sampled at 1920 Hz. It will reduce the cost of hardware compared with methods that use high sampling rates. A new HIF model is also used. The data of current signals are from the simulation of Electromagnetic Transients Program (EMTP). The DT algorithm trained can successfully distinguish the HIFs from most normal operations on simulation data, including switching loads, switching shunt capacitors, and load transformer inrush currents. Testing on experimental data is recommended for future work.

Decision tree-based methodology for high impedance fault detection

A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.

High impedance fault detection based on wavelet transform and statistical pattern recognition

Signal processing hardware and software that can be used to improve the detection of certain power system faults using computer relays are discussed. Integrated systems and architectures for monitoring several fault-sensitive parameters have been investigated. A suggested architecture utilizing several processors is presented. Several fault-sensitive parameters for the detection of arcing faults are presented. A detection methodology based on these parameters is described, and a partial solution to the problem of directionality is discussed. The use of a knowledge-based environment to modify protection criteria is suggested. >

A digital signal processing algorithm for detecting arcing faults on power distribution feeders

Phase currents and voltages in a distribution power system change with a certain degree of chaos when high impedance faults (HIFs) occur. This paper describes application of the concepts of fractal geometry to analyze chaotic properties of high impedance faults. Root-mean-square rather that instantaneous values of currents are used for characterization of temporal system behavior; this results in relatively short time-series available for analysis. An algorithm is presented for pattern recognition and detection of HIFs; it is based on techniques suited for analysis of relatively small data sets. Examples are given to illustrate the ability of this approach to discriminate between faults and other transients in a power system.

Analysis of high impedance faults using fractal techniques

Traditional extremely low frequency (ELF) magnetic field computation techniques assume that the current carrying power line conductors are straight horizontal wires. This assumption results in a model whose magnetic fields are distorted from those produced in reality. An exact solution and an approximation are proposed for modeling magnetic fields produced by the sagged conductors of power lines, by taking advantage of the fact that the equation of the catenary exactly describes the line sag. The proposed approaches differ in the required computational burden and in the precision of the results. A field mapping measurement example illustrates the applicability and the need for this analysis. The relative importance of the catenary effect is discussed.

Effects of conductor sag on spatial distribution of power line magnetic field

Several low frequencies of the current waveform in distribution feeders exhibit modified behavior under fault conditions. Two frequencies, 180 Hz and 210 Hz, were selected for study owing to the strong magnitude variations associated with arcing faults at these frequencies. A hierarchical algorithm with adaptive characteristics is presented along with the performance results for its application at these low frequencies. The various parameters that affect the sensitivity of the algorithm are discussed. The results of tests using recorded field data are given, and the effects of using a digital filter front end for the algorithm are also discussed. >

An arcing fault detection technique using low frequency current components-performance evaluation using recorded field data

Distribution feeder faults modulate primary current and generate noise through arcing phenomena. The variation and behaviors selected low frequencies during fault conditions are presented. These are contrasted with normal events such as feeder-switching and capacitor-bank operations. Recorded field data have been analyzed, and the statistical results are presented. Specific behavior characteristics such as arc duration, arc repetition rate, and magnitude of low-frequency spectra are presented. Comparisons are made for different soil types and conditions. >

B.D. Russell

Papers

A digital signal processing algorithm for detecting arcing faults on power distribution feeders

Analysis of high impedance faults using fractal techniques

Effects of conductor sag on spatial distribution of power line magnetic field

An arcing fault detection technique using low frequency current components-performance evaluation using recorded field data

Behaviour of low frequency spectra during arcing fault and switching events