New smart load management (SLM) approach for the coordination of multiple plug-in electric vehicle (PEV) chargers in distribution feeders is proposed. PEVs are growing in popularity as a low emission and efficient mode of transport against petroleum-based vehicles. PEV chargers represent sizeable and unpredictable loads, which can detrimentally impact the performance of distribution grids. Utilities are concerned about the potential overloads, stresses, voltage deviations and power losses that may occur in distribution systems from domestic PEV charging activity as well as from newly emerging charging stations. Therefore this study proposes a new SLM control strategy for coordinating PEV charging based on peak demand shaving, improving voltage profile and minimising power losses. Furthermore, the developed SLM approach takes into consideration the PEV owner preferred charging time zones based on a priority selection scheme. The impact of PEV charging stations and typical daily residential loading patterns are also considered. Simulation results are presented to demonstrate the significant performance improvement offered by SLM for a 1200 node test system topology consisting of several low-voltage residential networks populated with PEVs.

Dissolved gas analysis (DGA) is used to assess the condition of power transformers. It uses the concentrations of various gases dissolved in the transformer oil due to decomposition of the oil and paper insulation. DGA has gained worldwide acceptance as a method for the detection of incipient faults in transformers.

Frequency-response analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relies on graphical analysis, it calls for an expert to analyze the results. As so far, there is no standard code for FRA interpretation worldwide. In this paper, a novel online technique is introduced to detect the internal faults within a power transformer by constructing the voltage-current (V - I) locus diagram to provide a current state of the transformer. The technique does not call for any special equipment as it uses the existing metering devices attached to any power transformer to monitor the input voltage, output voltage, and the input current at the power frequency and, hence, online monitoring can be realized. Various types of faults have been simulated to assess its impact on the proposed locus. A Matlab code based on digital image processing is developed to calculate any deviation of the V - I locus with respect to the reference one and to identify the type of fault. The proposed technique is easy to be implemented and automated so that the requirement for expert personnel can be eliminated.

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation and significant number of DGA results fall outside the proposed codes of the current methods and cannot be diagnosed by these methods. To overcome these limitations, this paper introduces a novel approach using Gene Expression Programming (GEP) to help in standardizing DGA interpretation techniques, identify transformer critical ranking based on DGA results and propose a proper maintenance action. DGA has been performed on 338 oil samples that have been collected from different transformers of different rating and different life span. Traditional DGA interpretation techniques are used in analyzing the results to measure its consistency. These data are then used to develop the new GEP model. Results show that all current traditional techniques do not necessarily lead to the same conclusion for the same oil sample. The new approach using GEP is easy to implement and it does not call for any expert personnel to interpret the DGA results and to provide a proper asset management decision on the transformer based on DGA analysis.

This paper presents a comprehensive analysis of the effects of various faults on the FRA signatures of a transformer simulated by a high-frequency model. The faults were simulated through changes in the values of some of the electrical components in the model. It was found that radial displacement of a winding alters the FRA signature over the entire frequency range (10 Hz-1 MHz), whereas changes due to axial displacement occur only at frequencies above 200 kHz. A Table listing various transformer faults and the associated changes in the FRA signature was compiled and could be used in the formulation of standard codes for power transformer FRA signature interpretation.

Frequency response analysis (FRA) has become a widely accepted tool to detect power transformer winding deformation due to the development of FRA test equipment. Because FRA relies on graphical analysis, interpretation of its signature is a very specialized area that calls for skilled personnel, as so far, there is no reliable standard code for FRA signature identification and quantification. Many researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature using simulation analysis by altering particular electrical parameters of the transformer equivalent electrical circuit. None of them however, investigated the impact of various physical fault levels on the corresponding change in the equivalent circuit parameters. In this paper, the physical geometrical dimension of a single-phase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical axial displacement of different fault levels is simulated in both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated. A key contribution of this paper is the charts it introduces to correlate various axial displacement levels with the percentage change of all transformer equivalent circuit parameters due to the axial displacement fault. In contrary with other researchers who only considered mutual inductance between low voltage and high voltage windings, simulation results shown in this paper reveal that other circuit parameters should be changed by a particular percentage to accurately simulate particular fault level of transformer winding axial displacement. Results of this paper aid to precisely simulating winding axial displacement using transformer equivalent circuit that facilitates accurate qualitative and quantitative analysis of transformer FRA signatures.