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

No abstract

This paper introduces a novel method for single and simultaneous fault location in distribution networks by means of a sparse representation (SR) vector, Fuzzy-clustering, and machinelearning. The method requires few smart meters along the primary feeders to measure the pre-and during-fault voltages. The voltage sag values for the measured buses produce a vector whose dimension is less than the number of buses in the system. By concatenating the corresponding rows of the bus impedance matrix, an underdetermined set of equation is formed and is used to recover the fault current vector. Since the current vector ideally contains few nonzero values corresponding to fault currents at the faulted points, it is a sparse vector which can be determined by -norm minimization. Because the number of nonzero values in the estimated current vector often exceeds the number of fault points, we analyze the nonzero values by Fuzzy-c mean to estimate four possible faults. Furthermore, the nonzero values are processed by a new machine learning method based on the k-nearest neighborhood technique to estimate a single fault location. The performance of our algorithms is validated by their implementation on a real distribution network with noisy and noise-free measurement.Index Terms-Compressive sensing, distribution networks, fault location, Fuzzy-c mean, k-nearest neighborhood, and stable -norm minimization, smart meters.

In this study, seventeen samples were created for classifying internal, surface, and corona partial discharges (PDs) in a high voltage lab. Next, PDs were measured experimentally to provide a dictionary comprising the types. Due to the huge size of the recorded dataset, a new and straightforward preprocessing method based on signal norms was used to extract the appropriate features of various samples. The new sparse representation classifier (SRC) was computed using ℓ 1 and stable ℓ 1 -norm minimization by means of Primal-Dual Interior Point (PDIP) and Basis Pursuit De-noise (BPDN) algorithms, respectively. The pattern recognition was also performed with an artificial neural network (ANN) and compared with the sparse method. It is shown that both methods have comparable performance if training process, tuning options, and other tasks for finding the best result from ANN are not taken into account. Even with this assumption, it is shown that SRC still performs better than ANN in some cases. In addition, the SRC technique presented in this paper converges to a fixed result, while the results after training the ANN vary with every run due to random initial weights.Index Termssparse representation, compressive sensing, partial discharges, pattern recognition, ℓ 1 and stable ℓ 1 -norm minimization, ANN, signal norms. MehrdadMajidi received the B.Sc. and M.Sc. degrees in power electrical engineering from Power and Water University of Technology (PWUT), Tehran, Iran with honors in 2009 and 2011, respectively. He is currently a Graduate Research Assistant in the

No abstract