Locating high impedance fault (HIF) in a distribution network enhances the reliability of the system and improves the quality of the service for the customers. Due to low magnitudes of fault current and nonlinearity of fault impedance it is a very hard task and less considered by the distribution automation system. This paper introduces a novel method for estimating the location of high impedance arcing faults in radial distribution systems. The proposed method searches the feeders of the network at different locations, to find where the estimated parameters of the assumed HIF would result the most accurate estimation of the faulty phase voltage. The fault current and the arc parameters are estimated at each probable location employing an appropriate model to estimate the faulty phase voltage. As the proposed method uses the time domain analysis, it is applicable for nonhomogeneous distribution networks with unbalanced loading as well as symmetrical systems. The results of simulations of IEEE 34-node test feeder with various HIF parameters by EMTP-RV show that the proposed method is well capable of locating high impedance faults despite uncertainty in the load data.

A Searching Based Method for Locating High Impedance Arcing Fault in Distribution Networks

Incipient faults in power distribution systems potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves power supply reliability. Since the faults are infrequent and transient, few samples can be procured in real applications. In this paper, a detection method based on human-level concept learning (HLCL) is proposed to address this problem. The method contains two steps: human-level waveform decomposition (HLWD) and hierarchical probabilistic learning (HPL). HLWD, inspired by human perception, decomposes waveform into primitives: segments of general shape and residuals, which identify a waveform. HPL learns waveform through a generative process based on primitives, where the probability for an abnormal event to be an incipient fault can be hierarchically calculated. Experiments on simulation data and field data, which contain subcycle incipient faults, multicycle incipient faults, permanent faults and transient disturbances, indicate that the proposed method outperforms other three generally used classifiers.

Incipient Fault Identification in Power Distribution Systems via Human-Level Concept Learning

Incipient faults in underground cables are often resulted from electric stress and cable aging. If such faults occur as current spikes in short periods, permanent faults may appear. Moreover, incipient faults may disturb electricity transmissions because of detection delays and thus, precise well-timed protection decisions cannot be made. Therefore, one of the most important considerations of utilities in the monitoring process is to recognize this type of faults from other conditions as soon as possible. In this paper, a precise approach based on CUmulative SUM and ADAptive LInear NEuron has been proposed. In addition to its high-speed detection ability, the practical on-line implementation of the proposed approach is simple as well. The simulation results in EMTPWorks environment demonstrate that the proposed approach has a strong capability in distinguishing the cable incipient faults from other similar conditions in distribution systems.

Incipient Faults Monitoring in Underground Medium Voltage Cables of Distribution Systems Based on a Two-Step Strategy

Machine learning applications in power system fault diagnosis: Research advancements and perspectives

A new method is proposed to identify the location of transient events, including incipient faults, in power distribution systems, by using synchronized measurements from an emerging class of sensors, called waveform measurement units (WMUs). WMUs capture the voltage and current waveforms in time domain. The proposed method consists of three steps. The first step is to characterize the oscillatory modes of the transient components of all the captured synchronized voltage and current waveforms from all WMUs, by conducting a multi-signal modal analysis. The second step is to construct a circuit model for the underlying distribution feeder at the identified dominant mode(s) of the transient event. The final step is to identify the location of the transient event with the means of a method that involves certain forward and backward analyses of the constructed circuit model. The proposed method requires installing as few as only two WMUs. It can also utilize several synchronized waveform measurements when several WMUs are available. The performance of the proposed method is assessed on the IEEE 33-bus test system; for different cases of transient events, such as sub-cycle incipient faults, multi-cycle incipient faults, permanent faults, as well as benign yet informative events such as capacitor bank switching. Both the accuracy and the robustness of the proposed method are verified. The analysis and results in this paper provide new insights on possible applications of synchronized WMU measurements; while they also address a highly challenging problem in power distribution networks.

Synchronous Waveform Measurements to Locate Transient Events and Incipient Faults in Power Distribution Networks

Early and accurate detection of incipient cable faults is of a great interest to the utility industry. Such faults can be classified into subcycle and multicycle types. Although a lot of research has been conducted on subcycle types of faults, little work has been done for multicycle types of faults. This paper proposes a novel method for detecting and locating a multicycle incipient fault in a cable. The incipient fault is modeled as a self-clearing arcing fault. The distortion degree of calculated voltage is used to detect the occurrence of an incipient fault. The degree of match between the measured and calculated waveforms is used to guide the search for the fault distance. The accuracy is further improved by taking into account the incipient fault angle as seen in the voltage waveform and the power loss characteristics. The proposed method has been verified by PSCAD/EMTDC simulations. Lab experiments are also conducted to further validate the proposed detection method.

Kai Zhou

Papers

Multicycle Incipient Fault Detection and Location for Medium Voltage Underground Cable