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Reza Noroozian, Mehrdad Abedi and Gevorg Gharehpetian

Combined Operation of AC and DC Distribution System with Distributed Generation Units

This paper presents a DC distribution system which has been supplied by external AC systems as well as local DG units in order to demonstrate an overall solution to power quality issue. In this paper, the proposed operation method is demonstrated by simulation of power transfer between external AC systems, DG units, AC and DC loads. The power flow control in DC distribution system has been achieved by network converters and DG converters. Also, the mathematical model of the network, DG and load converters are obtained by using the average technique, which allows converter systems accurately simulated and control strategies for this converters is achieved. A suitable control strategy for network converters has been proposed that involves DC voltage droop regulator and novel instantaneous power regulation scheme. Also, a novel control technique has been proposed for DG converters. In this paper, a novel control system based on stationary and synchronously rotating reference frame has been proposed for load converters for supplying AC loads connected to the DC bus by balanced voltages. The several case studies have been studied based on proposed methods. The simulation results show that DC distribution systems including DG units can improve the power quality at the point of common coupling (PCC) in the power distribution system or industrial power system.

Open access

Hamed Nafisi, Mehrdad Abedi and Gevorg B. Gharehpetian

Abstract

In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transformer for simulation purposes. In present paper PD phenomenon is implemented in different points of transformer winding using threecapacitor model. Then impulse test is applied to transformer terminals in order to use produced current in neutral point for training and test of employed ANNs. In practice obtained current signals include noise components. Thus the performance of Fuzzy ARTmap and Bayesian networks for correct identification of PD location in a noisy condition for detected currents is also investigated. In this paper RBF learning procedure is used for Bayesian network, while Markov chain Monte Carlo (MCMC) method is employed for training of Fuzzy ARTmap network for locating PD in a power transformer winding and results are compared.