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This paper proposes a new algorithm for localizing phase to ground faults in an electric power distribution system. Fault-originated traveling waves propagate along the distribution paths in both directions away from the fault point and are reflected at line terminations, junctions between feeders, laterals, and the fault location. Depending on the paths which traveling waves reciprocate through, the transient signal at each node contains certain path characteristic frequencies (PCFs). Energy spectrum of the transient signal has high density around the path characteristic frequencies. On this basis, the transient voltage is decomposed by a wavelet filter and its energy spectrum is decomposed into different levels. Depending on the bandwidth of the wavelet filter and the path characteristic frequencies, the decomposed signal in each level contains a certain percentage of energy. Then, a neural network-based methodology is proposed as a power distribution line fault locator. Energy percentage in each level is the candidate feature for training the artificial neural network. Simulations and the training process for the neural network are performed respectively using ATP/EMTP and MATLAB. It is shown that the results of the proposed algorithm are quite satisfactory. Index Terms-Artificial neural network, fault location, path characteristic frequency, wavelet transform.

The Internet of Things (IoT) is a crucial component of Industry 4.0. Due to growing demands of customers, the current IoT architecture will not be reliable and responsive for next generation IoT applications and upcoming services. In this paper, the next generation IoT architecture based on new technologies is proposed in which the requirements of future applications, services, and generated data are addressed.

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