Fault Location in Distribution Networks by Compressive Sensing

Abstract

This paper proposes a novel method for fault location in distribution networks using compressive sensing. During-and pre-fault voltages are measured by smart meters along the feeders. The voltage sag vector and impedance matrix produce a current vector that is sparse enough with one nonzero element. This element corresponds to the bus at which a fault occurs. Due to limited number of smart meters installed at primary feeders, our system equation is underdetermined. Therefore, the ℓ 1 -norm minimization method is used to calculate the current vector. Primal-Dual interior point (PDIP) and Log Barrier Algorithm (LBA) are utilized to solve the optimization problem with and without measurement noises, respectively. Our proposed method is implemented on a real 13.8 kV, 134-bus distribution network when single-phase, three-phase, double-phase, and double-phase to ground short circuits occur. Simulation results show the robustness of the proposed method in noisy environments and satisfactory performance for various faults with different resistances.Index Terms-fault location, distribution networks, smart meters, compressive sensing, ℓ 1 and stable ℓ 1 -norm minimization

Fault location in distribution systems with DG based on similarity of fault impedance

“…Smart measurement devices report outages and measured voltage sags in the fault conditions. PMUs are utilized to measure the magnitude and phase of deviation voltage along the feeders, whereas smart meters are used just for measuring the voltage magnitudes [11]. When a fault occurs in the feeder, each node has specific voltage sag with different magnitudes and phase angles [15].…”

“…In this paper, noise is considered as a normal distribution of zero mean, µ , and 1% of standard deviation, σ . Noise is considered by multiplication of both magnitude and phase of measurement in (n+1) where 'n' is the number that is generated by normal distribution [11]. Table 3 shows the robustness of obtained simulation results while each PMU has noise N (0, 1%).…”

“…Furthermore, for multiple fault sections, a ranking approach was utilized. In [11], based on comprehensive sensing, the fault current vector from a limited number of voltage sag meters was recovered. The performance of this algorithm was improved in [12].…”

Ubicación óptima de monitores de hundimientos de tensión en redes de distribución incorporando restricciones de localización de fallas de red

“…2) Se realiza un análisis de cortocircuito mediante simulación en todas las barras y locaciones de línea que se definan el sistema eléctrico. Para ello se recomienda establecer puntos de falla a intervalos de 100 metros en promedio, siendo una segmentación apropiada en este tipo de redes eléctricas [19]. El valor de la impedancia de falla es el mismo para todas las fallas realizadas, y de acuerdo a lo expresado previamente, se establecen tres valores distintos ( , , ).…”

Fault Location Using Wide-Area Measurements and Sparse Estimation

“…In fact this kind of techniques have been used by various researchers for purposes of detection and identification of faults. One recent work reported in [25] uses several compressive sensing algorithms to pinpoint the location of a faulted bus. In [26] the authors employ greedy orthogonal matching pursuit (OMP) method and the least-absolute shrinkage and selection operator (Lasso) to identify line outages at affordable complexity.…”