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Regulating the output voltage of distributed generation units while maintaining proper power sharing among them is a challenging control issue in dc microgrids. This paper presents a new technique to regulate the voltage levels across the dc microgrid loads and achieve proper load current sharing among the distributed generation units without directly regulating their output voltages. To implement the proposed technique, a hybrid communication scheme comprising a centralized scheme for load current sharing and a decentralized scheme for load voltage regulation is proposed. The dynamic performance of the investigated dc microgrid is examined under different operating conditions through extensive digital computer simulation analyses in MATLAB. The simulation results reveal the effectiveness of the proposed technique under different operating and loading conditions including constant impedance, power, and current loads, communication link failure, and plug-and-play feature of the distributed generation units.

N. Tesla, the prospector of the power delivery in free space, suggested a huge amount of power can be transferred through the earth while G. Marconi concentrated on inventing wireless communication system over the ocean a hundred years ago. Whereas the telecommunication technology has been developed successfully, the development of wireless power transfer (WPT) system had been stagnant over decades as large devices were required to create the resonant oscillation in a safety manner and the system indicated low efficiency. Due to the development of power devices and the day-by-day increase of mobile products, electric vehicles and wireless sensors in smart grids, WPT technology is expected to undergo substantial advancement and significant power systems applications in the near future. This paper is aimed at introducing a state-of-the-art review for existing WPT technologies with detailed comparison. The paper also presents the limitation of inductive power transfer system through simulation and practical analyses. Finally, recommendations and future perspective to overcome these limitations are provided with four main themes: design, efficiency, stability, and safety.

While frequency response analysis (FRA) technique has been successfully used to assess the mechanical integrity of active parts within power transformers, it still exhibits some drawbacks including its inability to detect incipient and minor winding deformations and the requirement for an expert to analyze the results. Although several papers have investigated the impact of various faults on the transformer FRA signature, no attempt was made to automate and improve the fault detection accuracy of the current technique. The main contribution of this paper is the presentation of a new approach for the FRA technique through incorporating the magnitude and phase angle plots that can be measured using any commercial frequency response analyzer into one polar plot. Contrary to the current industry practice that only relies on the magnitude of the measured FRA signature for fault identification and quantification, the proposed polar plot that comprises more features than the magnitude plot will facilitate the use of digital image processing (DIP) techniques to improve the detection accuracy, standardize, and automate the FRA interpretation process. In this regard, three-dimensional models for two three-phase power transformers of different ratings, sizes, and windings structures are modeled using a finite element analysis technique to simulate various levels of axial displacement (AD) and disk space variation (DSV) at different locations of the transformer windings. Impact of minor fault levels on the proposed polar plot signature is investigated through the application of various DIP techniques. Simulation results are validated through practical measurements on a scaled-down transformer. Results show that the proposed polar plot along with the DIP technique is able to detect minor fault levels of AD and DSV with high accuracy.

Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost-effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high-resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels.

This paper investigates the feasibility of a cost effective method to prevent internal resonance in power transformers connected to gas insulated substation (GIS) due to very fast transient overvoltage (VFTO) within the GIS. Power transformer internal resonance takes place if one of the VFTO frequencies matches with the transformer natural frequency. If the system is not equipped with adequate damping scheme for such transients, transformer catastrophic failure may take place causing harmful impacts on the environment including fire and oil spillage. In this paper, the feasibility of utilizing RC surge suppressor to avoid power transformer internal resonance is investigated through the simulation of a real 400kV gas insulated substation located in the north of Iran and connected to a 250MVA, three phase power transformer. Several RC design topologies are analysed and the most effective topology to suppress high risk frequencies of VFTO is highlighted. In contrary to the existing resonance suppressing approaches that call for modifications in the GIS infrastructure, the proposed method in this paper is a cost effective technique that can be implemented within existing gas insulated substations without any change in its structure.

This paper investigates the performance of a recently proposed online transformer internal fault detection technique and examines the impact of harmonics through detailed nonlinear simulation of a transformer using 3-D finite-element modelling. The proposed online technique is based on considering the correlation between the instantaneous input and output voltage difference $(\Delta {\bf V})$ and the input current of a particular phase as a fingerprint of the transformer that could be measured every cycle to identify any incipient mechanical deformation within power transformers. To precisely emulate real transformer operation under various winding mechanical deformations, a detailed 3-D finite-element model is developed. Detailed simulations with (non)sinusoidal excitation are performed and analyzed to demonstrate the unique impact of each fault on the $\Delta {\bf V}\hbox{-}{\bf I}$ locus. Impacts on harmonic order, magnitude, and phase angle are also investigated. Furthermore, practical measurements have been performed to validate the effect of a winding short-circuit fault on the proposed $\Delta {\bf V}\hbox{-}{\bf I}$ locus without and with the impact of system harmonics.

Frequency Response Analysis (FRA) technique is commonly used to assess the mechanical integrity of power transformer core and windings. A few papers investigating the ability of FRA technique to detect bushing faults and transformer oil degradation can be found in the literatures. However, in all of these papers, only magnitude of the FRA signature along with visual analysis was used for fault identification and quantification which may lead to inconsistent conclusions for the same FRA signature when interpreted by various personnel. As such, there is an essential need to standardize and automate the FRA interpretation process to improve its reliability and accuracy. This paper is taking a step forward toward this goal through presenting a new FRA interpretation approach by incorporating the magnitude and phase angle plots of the measured FRA signature into one polar plot that comprises more features than the conventional magnitude plot currently used for fault diagnosis. Moreover, the proposed polar plot facilitates the application of digital image processing techniques (DIP) to automate and standardize the whole process. The new proposed technique is assessed through its application to detect minor transformer bushing faults and insulating oil degradations. In this regard, two transformers of different ratings, winding structure and physical dimensions are simulated using 3D finite element analysis to implement various levels of transformer bushing faults and oil degradation. The obtained FRA polar plots of the two investigated transformers under various bushings and oil health conditions are manipulated using the developed DIP codes to investigate the impact of each fault type / level on the proposed polar plot signature. Also, Practical measurements are performed to validate simulation results and evaluate the feasibility of the proposed technique. Results show the ability of the proposed technique to automate the detection of minor levels of bushing faults and insulating oil degradation with a high degree of accuracy.

Although a frequency response analysis (FRA) technique has been extensively used to detect mechanical deformation within power transformers, interpretation of FRA signature still needs a high level of expertise to identify and quantify faults as there is no FRA interpretation code widely accepted yet. All commercial frequency response analyzers can measure the magnitude and phase angle of the impedance, admittance, or transfer function of each phase in a wide frequency range; however, only magnitude is currently used for fault identification and quantification. This paper presents a novel approach for FRA signature interpretation by incorporating the FRA magnitude and phase plots into one polar plot that captures more features of the measured signal than the magnitude plot. Digital image processing-based techniques are employed to automate the fault identification and quantification process. To investigate the impact of transformer rating and size on the proposed technique, two transformers of different ratings and physical geometrical dimensions are simulated using 3-D finite-element analysis to emulate transformer real operation. Short-circuit of various fault levels is simulated at various locations within the high-voltage and low-voltage windings of the two transformer models and the obtained FRA polar plot signature for each case study is analyzed and compared with the healthy signature. Also, practical measurement is conducted to validate the simulation results. Results show that fault level along with fault location can be easily identified using the proposed polar plot signature along with the developed digital image processing technique.

The quality of insulation system within power transformer reflects the overall health condition of the asset. A timely and reliable maintenance decision along with the remaining operational life estimation of the asset can be identified through the measurements of some parameters reflecting the degradation rate of the transformer dielectric insulation. However, the process is not always straightforward due to the complexity of the insulation structure and degradation process. This paper proposes a new fuzzy logic approach to provide a proper asset management decision and predict the remaining operational life of a power transformer based on some insulating oil tests. The developed fuzzy logic model is validated through field data collected for various transformers of preknown health condition and life span. Results show that the proposed model is reliable and can be facilitated to provide a timely asset management decision with less reliance on expert personnel.

In this study, a new circuit topology of a three-phase half-bridge multilevel inverter (MLI) is proposed. The proposed MLI that consists of a cascaded half-bridge structure along with a modified full-bridge structure requires less number of dc-power supplies and power semiconductor devices, e.g. insulated gate bipolar transistors and diodes when compared with the existing MLI topologies, which significantly reduces the size and cost of the inverter. Two different structures: isolated and non-isolated dc-power supply-based three-phase half-bridge MLIs are investigated. A number of generalised methods are proposed to determine the magnitude of the input dc-power supplies that has a great impact on the number of levels of the output voltage waveform. To verify the feasibility of the proposed MLI topology, a scaled down laboratory prototype three-phase half-bridge MLI is developed and the experimental results are analysed and compared with the simulation results. Experimental and simulation results reveal the feasibility and excellent features of the proposed inverter system.

Frequency response analysis (FRA) has been globally accepted as a reliable tool to detect mechanical deformation within power transformers. However, because of its reliance on graphical analysis, interpretation of FRA signature is still a challenging area that calls for skilled personnel, as so far, there is no widely accepted reliable standard code for FRA signature identification and quantification. While several papers investigating the impact of various mechanical winding deformations on the transformer FRA signature can be found in the literature, no attention was given to investigate the impact of various bushing faults and transformer oil degradation on the FRA signature. This paper introduces a detailed simulation and practical analyses to elaborate the impact of bushing faults as well as transformer oil degradation on the transformer FRA signature. In this regard, the physical geometrical dimension of a three phase power transformer is simulated using 3D finite element analysis to emulate the real transformer operation. Various bushing faults have been emulated on the studied model and oil degradation is implemented through changing oil permittivity. Practical FRA test is conducted on a three phase 132 kV, 35 MVA power transformer to validate the simulation results. Results show that bushing faults and oil degradation can be visibly detected through FRA signature.

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h.

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 current techniques rely on personnel experience more than analytical formulation. As a result, the current techniques do not necessarily lead to the same conclusion for the same oil sample. A significant number of DGA results fall outside the proposed codes of the ratio-based interpretation techniques and cannot be diagnosed using these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach that aids in standardizing DGA interpretation and identifies transformer critical ranking based on DGA data. The approach relies on incorporating all traditional DGA interpretation techniques (Roger, Doerenburg, IEC, key gas and Duval triangle methods) into one expert model. In this context, DGA results of 338 oil samples of pre-known fault conditions that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used first to analyze the DGA results to evaluate the consistency and accuracy of each method in identifying various faults. Results of this analysis were then used to develop the proposed fuzzy logic model. The model is validated using another set of DGA data that were collected form previously published papers.

Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Oil sampling for IFT measurement calls for extra precautions due to its high sensitivity to various oil parameters and environmental conditions. The current used technique to measure IFT of transformer oil is relatively expensive, requires an expert to conduct the test and it takes long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces a new technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, does not need an expert person to conduct the test and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Artificial neural network (ANN) approach is proposed to model this correlation.

Frequency response analysis (FRA) is proven to be a powerful tool to detect winding deformation within power transformers. Although the FRA test along with the equipment are well developed, interpretation of FRA signature is still a challenge and it needs skilled personnel to identify and quantify the fault type if exists as at this stage, there is no reliable standard code for FRA signature classification and quantification. As it is very hard to implement faults on physical transformer without damaging it, researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature by randomly changing the value of particular electrical parameters of the transformer equivalent electrical circuit. None of them however, precisely investigated the correlation between physical fault level and the percentage change in each parameter. In this paper, the physical geometrical dimension of a singlephase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical radial deformation of different fault levels is simulated on both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated and the correlation between the fault level and the percentage change in each parameter of the equivalent circuit is provided. This will facilitate precise fault simulation using transformer equivalent electrical circuit and ease the quantification analysis of FRA signature.

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.

A doubly-fed induction generator’s (DFIG’s) sensitivity to external faults has motivated researchers to investigate the impact of various grid disturbances, such as voltage sags and short circuit faults, on its low-voltage ride-through (LVRT) capability. However, no attention has been paid to the impact of internal faults in voltage source converters (VSCs), that interface a DFIG with the grid, on the dynamic performance of the machine. This paper investigates the impacts of various VSC faults, on the dynamic performance and LVRT capability of a DFIG. Faults such as open and short circuit within VSC switches and across the DC-link capacitor are considered in this paper. The impact of internal VSC faults when they occur within the grid side converter (GSC) and rotor side converter (RSC) is investigated. Compliance of the voltage profiles of a DFIG with the LVRTs specified in the recent grid codes of the USA, Spain, Mexico, Denmark, Ireland, Germany, Quebec and the UK are examined for the faults studied. The simulation results show that these faults should be considered in designs of protection systems aimed at avoiding any catastrophic failure of the converter switches or wind turbine.

Dissolved gas analysis (DGA) is the most effective method to detect incipient faults in power transformers. It uses the concentrations of various gases dissolved in the transformer oil due to decomposition of the oil and paper insulation. Gases such as hydrogen, methane, acetylene, ethylene, and ethane are generated as a results of oil decomposition, while carbon monoxide and carbon dioxide are generated due to the paper degradation. In the meantime, faults such as partial discharge, overheating, and arcing produce a range of gases. The concentrations of these gases can be used to identify faults and estimate their severity. Various techniques have been developed to interpret DGA results such the key gas method, Doernenburg, Rogers, IEC ratio-based methods, Duval triangles, and the latest Duval Pentagon methods. However, each of these techniques relies on the accumulated knowledge and experience of experts rather than quantitative scientific models, therefore different diagnoses may be reported for the same oil sample. Hence the accountability of the judgment is questionable if it only relies on one interpretation technique. This paper proposes the use of scoring point method to improve the accountability of DGA interpretation process. In this context, DGA results of several transformer oil samples are analyzed with four existing interpretation techniques; Doernenburg, Rogers, IEC, and Duval triangle methods from which a scoring index is developed for all fault types. Based on this analysis, each technique is assigned with specific weighting factor. The summation of scoring point times weightage represents the scoring index for particular faults. The proposed method is validated through DGA historical data of various transformers of pre-known health condition and life span. Results show that the proposed method can increase the accuracy of the DGA interpretation process.

Many new cascaded multilevel inverter (MLI) topologies have recently been proposed and published in the literature. All proposed topologies demand significant amount of semiconductor components and input dc supplies, which is considered the main drawback for the implementation of three phase cascaded MLIs. This paper proposes a new generalized concept that could be employed within any existing cascaded MLI topology in order to reduce its size in terms of device count including semiconductor switches, diodes, and dc power supplies. The new generalized concept involves two stages namely, cascaded stage (CS) and phase generator stage (PGS). The PGS stage is a combination of conventional three phase two level inverter (CTPTLI) and three bidirectional (BD) switches, while the cascaded stage can be modified using any existing cascaded topology. The proposed concept is validated through extensive simulation and experimental analyses. Results show the capability of the proposed technique in reducing device count of the existing topologies while maintaining its performance.

One of the most expensive and critical component in wind turbines is the gearbox. Gearbox failure may have significant impacts on the network in terms of continuity of power supply especially in islanded areas where the majority of power demand is supplied by wind farms. As such, an effective condition monitoring technique for wind turbine gearbox should be adopted to avoid any catastrophic failure to the gearbox and maintain system reliability. This paper presents a technique to monitor the gearbox condition through monitoring the shaft torque and the gearbox vibration signals. Practical measurement is conducted on a test rig that emulating real wind turbine operation. Model based control technique using voltage slider by injecting a current through the induction generator stator terminals is applied to emulate real fault conditions on the wind turbine blades such as icing accumulation, wind share and gust wind. Order analysis technique is used to analyse and compare the obtained signals during normal and abnormal operating conditions.

This paper proposes a simple and effective evolutionary computation-based technique to estimate the equivalent circuit parameters of a single-phase transformer from its nameplate data without the need to conduct any experimental measurements. Two techniques, namely: particle swarm optimization and genetic algorithm are employed to track nameplate data by minimizing certain objective functions. The effectiveness of the proposed technique is examined through its application for three single-phase transformers of different ratings. The results show that evolutionary computation techniques can precisely identify transformer equivalent circuit parameters. The proposed technique can be extended to estimate the parameters of a three-phase power transformer from its nameplate data without taking the transformer out of service to carry out any experimental testing.

Gearbox and the blades are classified as the most critical and expensive components of the wind turbine. Moreover, these parts are prone to high risk failure when compared to the rest of the wind turbine components. Due to the global significant increase in wind turbines, a reliable and cost effective condition monitoring technique is essential to maintain the availability and to improve the reliability of wind turbines. This paper aims to present a comprehensive review of the latest condition monitoring techniques for turbine gearbox and blades which are considered as the crux of any wind energy conversion system.

Various configurations can be considered for a dc Microgrid (MG) comprising distributed generation (DGs) units and loads. Renewable energy sources and energy storage systems of plug-and-play nature can change the configuration of the MG at any time. Each configuration has a distinct and unique mathematical representation that influences the operation of the MG. However, the voltage throughout the MG must be regulated within acceptable limit regardless of the configuration, load demand, and the utilized control principle. This paper presents various configurations of dc MGs and analyzes their characteristic with respect to voltage regulation, and circulating currents among DGs. A suitable voltage regulation technique is proposed which can be applied for various MG configurations. A data communication technology required for the employment of the proposed technique is discussed as well. The dynamic performance of various dc MG circuit configurations, operating with the proposed methods, is evaluated through simulation analyses, realized in MATLAB/Simulink®.

A microgrid (MG) is a cluster of loads and distributed generation units (DG) that are located in a close distance from each other. In a dc MG, the DGs generate a dc voltage and the loads consume power while supplied by a dc voltage. The power sharing amongst DGs is one of the main control issues in dc MGs. Power sharing is conducted in either form of load current or power sharing; however, many of existing techniques assume a single load in the MG. This paper proposes a different technique which can be applied for both current load and power sharing techniques, and in single or multi-loads. The performance of the proposed methods is evaluated through simulation analyses, realized in MATLAB/Simulink®, during various loading demands, DGs capacities and in single or multi loads.

Frequency Response Analysis (FRA) is the most reliable technique for detecting a mechanical failure within electrical power transformers. Associated literature reports the ability of the FRA technique to identify non-mechanical faults such as transformer bushing failures. However, the interpretation of the FRA signature is still a challenge because of its reliance on graphical analysis, which requires an expert person for the interpretation process. Although the impact of transformer failure on the signature of the FRA technique has been intensively investigated, no standard code has been widely adopted. Moreover, the FRA technique is frequently ineligible in detecting minor levels of power transformer fault that decreases the transformer's ability to withstand any further electrical, thermal and mechanical stresses. In order to apply the FRA technique properly, the minimum detection of transformer faults should be estimated. This paper aims to identify the impact of the minimum level of transformer bushing failure on the FRA signature that can be visually detected. In this regard, a 3-D geometric of a 3-phase power transformer is simulated (based on the Finite Element Analysis (FEA) technique) to imitate its real operation during both healthy and faulty conditions. The impact FRA signatures (based on FEA) for various bushing failure levels was obtained and then compared to the healthy signature, and variations are detected if they exist. Results show that the transformer bushing failure has a minimum level that can be visually detected using the FRA technique.

This paper presents analysis of a three phase hybrid multilevel inverter (MLI) by applying low frequency stair case control strategy. The proposed MLI contains three cascaded stages, auxiliary stage-1, auxiliary stage-2, and main stage. The main stage contains three half-bridge cells which are connected in parallel with a common DC power supply. Each of the stages in the auxiliary stages consists of three H-bridge where the H-bridge cells contain isolated DC power supplies. The proposed hybrid MLI is tested for symmetric and asymmetric configurations of input voltage sources of the main and auxiliary stages. A scaled down prototype of the proposed hybrid three-phase hybrid MLI is developed to verify the control concept and the real time performance of the inverter.

Although several papers investigating the transient performance of induction motors can be found in the literature, a little attention was given to investigate the electromechanical oscillations of a multi-motor set connected on a common mechanical shaft which is widely used in oil and mining industry. This paper investigates the torsional oscillations phenomena occurring in a cascaded set of induction motors driving inertia load through elastic shafts. The electrical and mechanical mathematical models of the investigated induction motors are developed in the ABC reference frame and the system model is implemented using Matlab/Simulink. The electrical and mechanical transients during startup are studied and attention is given to the transmitted torque on the mechanical shaft sections. Different case studies are presented with different loading conditions in order to accurately study the transient performance of the electrical and mechanical components of the system under study. Results indicate that severe torsional stresses will be induced on the mechanical shaft during the motor startup especially when the system starts against load. These high torsional stresses may lead to fatigue failure or fracture and affect the life time of the mechanical shaft if no adequate countermeasure is considered.

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 these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model.

A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders.

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.

The doubly fed induction generator (DFIG) is interfaced to the AC network through voltage source converters (VSCs) which are considered to be the core of the DFIG system. This paper investigates the impact of different intermittent VSC faults on the overall performance of a DFIG-based wind energy conversion system (WECS). The fault ride through capability of the DFIG under various VSC faults is also investigated. Faults such as open circuit and short circuit across the switches, when they occur within the grid side converter (GSC) and rotor side converter (RSC), are considered and compared in this paper. Short circuit and open circuit across the DC-link capacitor are also considered in this study as common VSC problems. Simulation results indicate that the short circuit faults have a severe impact on the overall performance of the DFIG, especially when they occur within the GSC. This is attributed to the fact that the GSC directly regulates the point of common coupling voltage. The open circuit faults hav...

A new Cascaded Multilevel inverter (CMLI) concept is presented in this paper, which is applicable to any existing CMLI topologies. The proposed new three phase MLI topology consists of level generator stage (LGS) and phase generator stage (PGS). While the PGS contains a specific structure of 12-semiconductor switches and 12-diodes, the LGS structure depends on the existing CMLI topologies. The main feature of the proposed new CMLI topology is to reduce the device counts of the existing CMLI topologies while they are implemented as dc/ac three phase power converters. The conventional cross-switched CMLI topology is considered as LGS in the experimental prototype. Simulation and experimental results are provided to verify the proposed new three phase CMLI concept.

Emerging smart grid technology is creating unique opportunity for wide applications of renewable distributed generation resources, smart appliances and plug-in electric vehicles (PEVs). This paper presents a combined online (real-time) and delayed (overnight and next day) PEV coordination scheme based on fuzzy reasoning and maximum sensitivity selections (MSS) considering wind and rooftop PV generations. The proposed combined online fuzzy-based and delayed MSS-based (OL-F/DL-MSS) algorithm aims at reducing energy generation cost by charging the electric vehicles during high renewable energy generation periods. To include vehicle owners’ satisfaction, they are classified into three groups; high priority consumers requiring expensive PEV charging as soon as arriving home during early evening hours, low priority consumers requesting cheap overnight PEV charging and medium priority consumers requiring inexpensive PEV charging within 24 hours. PEV coordination is performed to minimize the cost of generating energy and regulate node voltages while utilizing the available renewable energy resources for vehicle battery charging. Detailed simulations for uncoordinated and combined online/overnight coordinated PEV charging are presented for a 449 node smart grid test system consisting of the IEEE 31 node medium voltage distribution network, 3 wind turbines and 22 low voltage 19-node residential feeders with 88 rooftop PVs and 264 PEVs.

Frequency response analysis (FRA) has been widely utilized to detect winding and core deformations within power transformers. While the measurement technique has been well-developed and standardized, the interpretation of the FRA signature is still a challenging area that calls for more research, in order to establish standard and widely-accepted interpretation codes. To investigate the impact of various winding deformations on the transformer FRA signature, a reliable high frequency transformer model is required. This paper presents two high frequency transformer models based on the electrical distributed parameters circuit and numerical representation that are to be used for FRA studies. The electrical parameters used in both models are obtained through simulating a 10-kVA transformer using a three-dimensional finite element analysis, so as to mimic a transformer's real operation. The calculated parameters are then employed within the two proposed models to acquire FRA signatures which are then analyzed and compared. The results show that both methods are suitable to model power transformers for FRA studies.

Owing to the fact that the performance and control design of large scale induction machines depend on accurate knowledge of its equivalent electrical circuit parameters, precise identification of these parameters is essential. Current methods used to quantify induction machine parameters call for performing several experimental testing such as no-load, locked-rotor and DC tests which may not be available due to the lack of hardware, experience and time required to perform the tests. In this paper, two different evolutionary computational techniques namely; bacterial foraging and genetic algorithm, are employed to estimate these parameters from machine nameplate data without conducting any experimental measurements. The accuracy of the proposed techniques is assessed through their application on squirrel cage and wound rotor induction motors of different ratings. The motors performance computed using the proposed techniques is compared with that computed using classical practical measurements. The obtained results reveal the ability of evolutionary techniques to estimate the equivalent electrical circuit parameters of induction machines with a reasonable degree of accuracy. Results also show that bacterial foraging approach is more accurate than genetic algorithm in estimating induction machine parameters.

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.

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.

This paper investigates different approaches to improve the power transfer capability (PTC) of transmission lines. Study was performed on the Eastern Gold Fields (EGF) area of the Western Power network in Western Australia where power transfer to this area is currently enhanced using four saturable reactor types static var Compensators (SR SVCs) installed in this region. These SR SVCs have reached to the end of operational life and they are scheduled for replacement by different dynamic reactive power devices such as Static Synchronous Compensators (STATCOMs) or Thyristor Controlled SVCs (TSVCs). Another proposed option to enhance the PTC to this area is by upgrading the 220 kV transmission line to 275 kV transmission line and / or using series capacitor compensation. This paper introduces technical and economical study for the aforementioned options and suggests a reliable and cost effective approach to improve the PTC to EGF area.

This paper investigates the use of a superconducting magnetic energy storage (SMES) unit to improve the performance of an ac/dc power system. In this context, investigations have been conducted on a large turbine-generator unit connected to a high-voltage direct current (HVDC) system. The impact of HVDC converter station faults on the torsional torques induced in turbine-generator shafts with and without using an SMES unit is elaborated. Faults considered are fire-through, misfire, short circuit across the inverter station, flashover, and a three-phase short circuit in the ac system. These investigations are studied using an electromagnetic transient program power system simulation/electromagnetic transients including DC (PSCAD/EMTDC) and the results are presented in the form of typical time responses as well as harmonic analysis.

The number of doubly fed induction generators (DFIG) connected to the existing network has increased significantly worldwide during the last two decades. The stator in DFIG is directly connected to the grid whereas the rotor is interfaced to the grid through two voltage source converters; rotor side converter (RSC) and grid side converter (GSC), which are considered as the crux of the DFIG system. The converter stations determine the ability of wind turbine to operate optimally during wind speed fluctuation and it can provide reactive power support to the grid during grid disturbance events. This paper investigates the impact of fire-through and misfire faults in the converter station on the dynamic performance of the DFIG. Detailed simulation is carried out using MATLAB/SIMULINK software.

The majority of mechanical deformation within power transformers is due to short circuit faults. Although, frequency response analysis (FRA) has been recently recognized as the most reliable detection tool for mechanical deformation in transformers, in the event of minor fault, the current FRA interpretation approach may not able to detect any variations between healthy and faulty FRA signatures. This paper is aimed at identifying the minimum level of short circuit fault within power transformer that can be detected using FRA technique. In this regard, the physical geometrical dimension of a three-phase transformer has been simulated using three-dimensional finite element analysis to emulate the physical operating conditions of power transformers. Short-circuit faults within transformer winding are simulated at several fault levels and the FRA signature for each case study is obtained and compared with the healthy signature. Results show that there is a minimum detection level of short circuit faults that can be detected using FRA technique.

Doubly Fed Induction Generators (DFIGs) are currently extensively used in variable speed wind power plants due to their superior advantages that include reduced converter rating, low cost, reduced losses, easy implementation of power factor correction schemes, variable speed operation and four quadrant active and reactive power control capabilities. On the other hand, DFIG sensitivity to grid disturbances, especially for voltage sags represents the main disadvantage of the equipment. In this paper, a high temperature superconductor (HTSC) is proposed to be integrated within the DFIG converters to improve the overall performance of a DFIG-based wind energy conversion system (WECS). A fuzzy logic (FL) to control the charging and discharging of the high temperature superconductor is introduced. Simulation results reveal the effectiveness of the proposed HTSC in improving the dynamic performance of WECS under study.

Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Due to high sensitivity to various oil parameters and environmental conditions, oil sampling for IFT measurement requires extra precautions. The current technique used to measure IFT of transformer oil is relatively expensive and it takes a long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces an alternative technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Two artificial intelligence (AI) approaches; artificial neural network (ANN) and fuzzy logic are proposed to model this correlation.

The number of wind turbines connected to power grids have significantly increased during the last decade. This is mainly due to the convincing revolution in power electronic technology and the growing concern about greenhouse effect that is intensified due to the burning of fossil fuels. Variable-speed wind energy conversion systems (WECSs) such as doubly fed induction generators (DFIGs) are dominating the wind energy market due to their superior advantages over fixed-speed-based WECS which include more captured energy, less mechanical stress, and acoustical noise. DFIG is interfaced to the ac network through the grid-side voltage source converter (GSC) and rotor-side RSC to enable the variable-speed operation of the wind turbine and to provide reactive power support to the ac grid during disturbance events. Converter switching malfunction such as fire-through may influence the power dispatch capability of the DFIG. In this paper, a coil is proposed to be integrated within the DFIG converters to improve dynamic performance of DFIG-based WECS during internal converter switching malfunctions such as fire-through fault. Simulation results without and with the coil connected to the system are presented.

Frequency response analysis (FRA) has been employed as an effective tool for the detection of various mechanical winding and core deformations within electrical power transformer. Traditional FRA signature interpretation relies on the magnitude of the FRA plot although all practical frequency response analyzers are able to provide both magnitude and angle of the FRA signal in wide frequency range. Moreover, no attention has been given to the impact of power transformer insulating oil degradation on the FRA signature. This paper presents is aimed at introducing a new interpretation approach of the FRA signature using polar plot which is obtained by incorporating FRA signature magnitude and phase into one plot. The paper also investigates the impact of insulating mineral oil degradation on the proposed polar plot signature. Digital image processing (DIP) technique is developed to automate the interpretation process. In this regard, the physical geometrical dimension of a single-phase transformer filled with insulating mineral oil is simulated using three-dimensional finite element analysis to emulate real transformer operation. FRA polar plot signatures are measured and analyzed for various health conditions of the mineral oil. Results show that insulating mineral oil degradation has an impact on the transformer FRA polar plot signature. The proposed FRA polar plot technique is easy to implement within any frequency response analyzer.