Abstract
Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. A number of abnormal events can cause unsafe operation, including a high impedance fault (HIF), a partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization. These abnormal events are not detectable by conventional protection schemes. In this paper, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates.
Highlights
The deterioration of medium voltage distribution feeder equipment develops over time until a major outage takes place
The supervisory control and data acquisition (SCADA) information is applied with the decision tree (DT) general classification to precisely identify the type of the event from the above classes
Testing for all events at a single moment in time, The same values for the thresholds are used for both test feeders, Changing high impedance fault (HIF) currents, from 15 to 75 A [3], Testing single and multi-phase events, Fault types, balanced and unbalanced, grounded and ungrounded, Fault distance, location and inception angle (0◦ –360◦ )
Summary
The deterioration of medium voltage distribution feeder equipment develops over time until a major outage takes place. A recent technique that can detect both HIFs and IFs but not the CB restrikes is presented in [19] This method, uses both the voltage and current measurements to estimate the arc parameters associated with such faults. Based on the characteristics and signatures of permanent faults, HIFs, IFs, un-cleared and self-cleared CB restrikes due to capacitor bank de-energization and normal operations (capacitor energization and load switching), a classification method is applied using DT. The proposed algorithm improves both the detection speed and the classification time, both of which are necessary for fast action in the case of HIFs. The algorithm is able to detect very high impedance IFs, unlike [10,11,12,13,14].
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