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In this paper, a series of laboratory experiments are conducted to investigate the effect of momentary small variations in the transformer operating temperature on the dissolved gas analysis (DGA) measurement. With the increased penetration level of renewable energy sources of intermittent characteristics into electricity grids, operating power transformers are expected to experience frequent temperature variations. Sampling transformer oil during such temperature variation leads to inaccurate diagnosis. Experimental results reveal that gas evolution in transformer oil is greatly affected by the small variations in the operating temperature. Such small variation can be a result of the intermittent generation characteristics of renewable energy sources. Hence, false analysis may be reported if oil is sampled during generation or load fluctuation events. Experimental results are explained through chemical equilibrium constant theory, which indicates that dissolved gases reflect the change in aging rate of the transformer oil-paper insulation system. These results suggest a new paradigm for DGA process through correlating measurements with the transformer operating temperature through the generation and load profiles at the instant of oil sampling.

Health index has been widely accepted as a powerful tool for monitoring the condition of power transformer insulation system based on various diagnostic parameters. While this approach has been extensively discussed in the literature, not much attention was given to provide effective solutions to the uncertainty in the used data. According to CIGRE 761, data quality issues may arise due to measurement accuracy as well as incompleteness and unavailability of the required data. Therefore, this article presents the implementation and evaluation of a certainty level model for transformer insulation system health index to deal with data uncertainty. The impact of data unavailability on the health index results is also investigated. Certainty level of the health index is determined by the criticality level of available data, and is reported along with the health index result. A method to handle unavailable data by predicting the oil interfacial tension (IFT) using Random Forest approach is also presented. The proposed certainty level model is designed to accommodate the predicted value of missed data into the health index model while considering its prediction accuracy. The robustness of the developed model is validated through its application in assessing the health condition of six in-service power transformers. The results indicate that by including the proposed certainty level and the prediction approach to eliminate the issue of uncertain and missed diagnostic data, an asset management decision can be taken on operating power transformer fleets with high level of confidence.

Frequency Response Analysis (FRA) is the most reliable technique currently used to evaluate the mechanical integrity of power transformers. While the measurement devices have been well developed over the past two decades, interpretation of the FRA signatures is still challenging regardless of the several papers published in this regard. This paper adds an attempt to understand the power transformer FRA signatures through experimental and simulation analyses. In this context, experimental FRA measurements are conducted on a 33/11 kV, 30 MVA transformer under various faults, including winding deformation, the short circuit turns, loss of clamping, and bushing fault. At the same time, the high-frequency transformer model that comprises series capacitance, self-inductance, series resistance, and mutual inductance is simulated using MATLAB / Simulink to compare simulation and experimental results. The correlation between physical circuit parameters and various faults facilitates a better understanding of each fault’s effect on the FRA signature. To quantify the impact of such faults, correlation coefficient, the absolute sum of logarithmic error, standard deviation, and sum square error are calculated with respect to the healthy signature at three frequency regions. Results show that using statistical coefficients over three frequency ranges of the FRA signature facilitates better fault identification and quantification.

The low efficiency of the electromagnetic forming is one of the obstacles for the advancement of this technology. Aiming at solving this issue, this paper presents a double tube-task electromagnetic forming technology method by employing a single driving coil to achieve tube compression and expansion at the same time. On the basis of electromagnetic forming principles and the law of energy conversion, this paper presents a new electromagnetic forming model for double tubes. The magnetic flux density distribution, electromagnetic force distribution and forming efficiency of the tube are analyzed and the effect of the equivalent pulse width of the driving coil current on the forming efficiency is investigated. Results show that in order to maintain the same plastic strain energy of the tube, the initial discharge energy required by the proposed double tube model is 25 kJ which is smaller than that required by a traditional single tube model (33.58kJ) and the efficiency can be improved by about 34.3%. The double tube model attains the maximum forming efficiency near the optimal current pulse width and can improve the low efficiency of traditional electromagnetic forming to a certain extent and provide more possibilities for this technological process.

While electromagnetic forming of aluminum alloy sheet has gained several advantages, the uneven radial deformation of the sheet is still one of the main drawback of this technology that calls for reliable and cost-effective solution. Aiming at this point, this paper presents an electromagnetic bulging system equipped with a new magnetic field shaper placed between the driving coil and the object sheet to modulate the distribution of the induced eddy current in the sheet, and hence regulating the distribution of the electromagnetic force to achieve more uniform deformation. A finite element electromagnetic-structure 2D coupling model simulating this process is developed and the effects of workpiece geometry and deformation speed on the circuit, magnetic field and structure field are considered. The coupling between the electric circuit and magnetic field; magnetic field and workpiece deformation is realized to simulate the actual system of thin plate electromagnetic bulging. The performance of the proposed system is compared with that of the traditional system through investigating the profiles of the induced eddy current, electromagnetic force and forming effect. Simulation results show that the uniformity of sheet metal forming is improved by using the proposed magnetic field shaper, and the maximum uniform deformation area is increasing from 32.1 mm to 73.8 mm, which stimulates more industrial applications of sheet metal electromagnetic forming.

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.

This paper investigates the dissolved gases evolution in transformer oil under combined DC/AC electrical-thermal stress. Dissolved gases detected in transformer aged insulation oil reveal that oil under AC electric field combined with thermal stress can produce more dissolved gases than oil under DC electric field with thermal stress but less than the gases produced in oil under distorted AC or combined AC/DC voltage stress. This is attributed to the divergent migration properties of the charged components under different types of electric fields. To further understand this behavior, carrier recombination coefficient is proposed to explain the oil DGA results under various aging stresses. Results show that diagnostic parameters such as breakdown voltage, oil interfacial tension, and moisture content in pressboard should be used along with DGA results in order to accurately diagnose the insulation condition within converter transformers that impose a combined AC/DC voltage stress on the insulation system.

Frequency response analysis (FRA) has become a widely accepted technique by worldwide utilities to detect winding and core deformations within power transformers. The main drawback of this technique is its reliance on the personnel level of expertise more than standard or automated codes. To establish reliable FRA interpretation codes, accurate high frequency transformer model that can emulate the frequency characteristics of real transformers in a wide frequency range is essential. The model can be used to investigate the impact of various winding and core deformations on the transformer FRA signature. The transformer equivalent high frequency electric circuit parameters can be calculated based on design data, which are rarely available, especially for old transformers. As such, this paper presents an artificial intelligence technique to estimate these parameters from the transformer FRA signature. The robustness of the proposed technique is assessed through its application on three, 3-phase power transformers of different ratings, sizes, and winding structures to estimate their high frequency electric circuit parameters during normal and fault conditions. Results show that the proposed technique can estimate equivalent circuit parameters with high accuracy and helps interpret the FRA signature based on the numerical changes of these parameters. The main advantage of this approach is the physical meaning of the model parameters facilitates reliable identification of various faults and hence aids in establishing reliable interpretation codes for transformer FRA signatures.

Winding deformation is a common problem that takes place within operating power transformers due to various internal and external conditions. Due to their progressive nature, winding deformations should be detected and rectified as soon as they emerge. Unfortunately, the only reliable technique to detect such faults at this stage is the frequency response analysis which is conducted offline. This has motivated researchers to propose alternative techniques to detect power transformer incipient winding and core deformations in real time. One of the promising online techniques proposed in the literature is the V – I Lissajous pattern method. While this technique was validated through simulation analysis, its feasibility for practical applications has not been explored yet. This paper takes a further step toward the field implementation of this technique through practical measurements on a custom-made transformer to assess its sensitivity, repeatability, and antinoise capability. Also, a new compensation approach is proposed to eliminate the effect of the transformer loading variation on the V – I Lissajous pattern. For the practical application of this technique, a hardware setup has been developed and installed to monitor the V – I characteristics of an operating power transformer within a 220-kV substation in real time.

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

Wind energy is one of the premier renewable energy sources that have gained popularity during the last decade. Among the currently available wind energy conversion systems (WECS), doubly fed induction generator-based technology has been widely employed due to its superior advantageous. The key features of the DFIG-based WECS include its ability to capture more wind energy and support the grid with large reactive power during short disturbance events. On the other hand, DFIG is very sensitive to grid faults which affect its fault ride through (FRT) capability. Furthermore, extreme wind gust even for short durations may lead to the violation of the low voltage ride through (LVRT) threshold limits set by worldwide transmission line operators. This situation cannot be mitigated by the turbine blades pitch controller since the response of the pitch mechanical control is much slower than the rapid dynamic change in the wind speed during short duration of wind gust events. While DFIG FRT capability has been discussed in several papers in the literature, not much attention was given to the mitigation of the effects of extreme wind gust of short duration on the DFIG performance. In this paper, the effect of various levels of wind gust on the performance of a DFIG-based wind energy conversion grid-connected system is investigated and mitigated using a new controller for superconducting magnetic energy storage (SMES) unit. A combination of hysteresis current and fuzzy logic controllers is employed to control the voltage source converter and the DC-DC chopper interfacing the SMES coil with the investigated system. Simulation results reveal the effectiveness of the proposed SMES controller that can be easily implemented within existing as well as new WECS installations.

Non-uniform axial deformation and tube wall thinning are the main issues restricting the further development of the tube electromagnetic bulging technology. Aiming at overcoming these issues, a tube electromagnetic bulging method based on convex driving coil is proposed in this paper. An electromagnetic-structure coupling model of the tube electromagnetic expansion process is established and the influence of detailed convex coil parameters on radial and axial Lorentz forces are analyzed. Results show that with the new proposed method, the homogeneous length of the tube axial deformation is 4.7 times that of the expansion loading with conventional driving coil. Meanwhile, the issue of tube wall thinning in the conventional electromagnetic bulging method is restrained due to the produced axial electromagnetic force using the proposed convex coil. The proposed tube electromagnetic bulging based on convex coil can effectively overcome the issues currently exist in the conventional tube electromagnetic bulging and promote wide industrial application of the electromagnetic forming technology.

Due to the fact that geometrical structure of tube electromagnetic bulging process with field shaper does not conform to the axisymmetric property, three-dimensional (3D) model is usually used to analyze the process which is complicated and time consuming. Therefore, on the basis of understanding the working principle of the field shaper, a two-dimensional (2D) model with current constraint is proposed in this paper to analyze the electromagnetic bulging process of a tube fitted with a field shaper. COMSOL finite element analysis software is used to develop three models for performance analysis and comparison, these are: conventional 3D model and the proposed 2D model with and without current constraint. Simulation results show that the induced eddy current density, magnetic flux density and electromagnetic body density of the two-dimensional model with current constraint are in good agreement with the results of the three-dimensional model. This provides an alternative and effective way to analyze the performance of the tube electromagnetic bulging with field shaper using a simple but yet reliable 2D model.

Uneven axial deformation is the current main issue of the tubes electromagnetic bulging technology which calls for innovative and cost-effective solutions. This paper presents a new method to overcome the above-mentioned issue based on employing a magnetic field shaper. The proposed magnetic field shaper is employed in the middle of the pipe and the driving coil. Induced eddy current control is realized by changing the structural parameters of the magnetic field shaper; thereby regulating the electromagnetic force distribution. The effectiveness of the proposed method is investigated through numerically comparing the electromagnetic performance of two EMF setups; traditional coil-based loading model and magnetic field shaper driving coil-based loading model proposed in this paper. The influence of the magnetic field shaper structural parameters on the tube electromagnetic forming performance is examined for optimum structure design. Results reveal that the concave profile of the radial electromagnetic force in the traditional loading method can be effectively improved using the proposed loading in this paper. The introduction of a magnetic field shaper not only changes the induced eddy current distribution, but also regulates the electromagnetic force profile in the axial-direction of the pipe which effectively enhances the uniform range of the pipe electromagnetic bulging, the maximum uniform deformation area is increased from 9mm to 35mm.

While electromagnetic forming is widely used for aluminum alloy materials, the non-uniformity of plate radial deformation is still a key issue that restricts the wide development of this technology. This article presents a new electromagnetic-forming topology. The proposed topology comprises outer ring double-coil plates along with an inner loop driving coil of a small number of turns whose geometrical parameters are optimized to adjust the induced eddy current of the plate. Numerical simulation results show that the presence of an inner ring driving coil shapes the magnetic field in a manner that the radial electromagnetic force is concentrated close to the edge of the die which affects the center of the plate. With optimum design for the geometrical parameters of the proposed inner ring driving coil, the electromagnetic force distribution can be adjusted over the plate, resulting in an improved plate deformation with an increase in the maximum uniform deformation area from 24mm to 65mm. Results also show that the proposed inner and outer ring double coil loading method possesses superior advantages over the traditional flat spiral coil loading method.

Frequency response analysis (FRA) plays an important role in the assessment of the mechanical integrity and hence, reliability of high-voltage power transformers. Wireless power transfer (WPT) system introduced by Tesla a 100 years ago based on Faraday’s law of electromagnetic induction is similar to a conventional power transformer. Therefore, the reliability of a large WPT system can also be evaluated using FRA measurement. When the WPT system is constructed, the electrical components and the desired transfer distance need to be ascertained accurately. Furthermore, the structure of the winding and the electrical characteristic of the components in WPT systems may vary due to aging or fault events. FRA measurement can examine the discrepancy between the WPT system parameters and the reference fingerprint data to provide cost-effective condition monitoring-based maintenance scheme for the WPT systems. This paper presents a detailed circuit analysis of a two-coil inductively coupled WPT model in different compensating topologies to accomplish efficient energy transfer. Then, FRA measurement is used to detect the variation in the amplitude and the phase angle of the developed circuit input impedance in a wide frequency range. In addition, displacements due to the placement of ferrite material and the variation of the transfer distance between the transmitter and receiver units are investigated through practical measurements.

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.

Power transformers are subject to winding and core deformations due to short circuit faults and other environmental conditions. Majority of these faults are of progressive nature and should be rectified as soon as they emerge. Frequency response analysis (FRA) has been widely accepted as a reliable diagnostic tool to detect such faults. As reliable interpretation codes for FRA signatures have not been fully developed and accepted yet, researchers have put much effort to investigate the impact of various mechanical deformations on the transformer FRA signature. Because of the intrusive nature of such faults when staged on a real transformer, most of the studies in the literatures were conducted on a transformer high frequency electric circuit model. Simplifications assumed in these models by ignoring the turn-to-turn capacitance, not taking into account the detailed winding structure and not considering all mutual inductances between coils in various phases have reduced the accuracy of the obtained results. To establish reliable FRA interpretation codes, it is essential to develop electric equivalent circuit models that can yield FRA signature as close as possible to the FRA signature trend of the real transformer. This paper proposes a detailed transformer high frequency electric circuit model which considers the winding structure, inter-turn capacitance and all mutual inductances. Calculation details of the model’s parameters are presented. The accuracy of the proposed model is assessed by comparing its FRA signature with that of an equivalent transformer hardware model during healthy, short circuit disks, radial deformation and axial disks buckling fault.

With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

Power transformers are prone to short circuit faults and external harsh environmental conditions that may result in various internal winding and core deformations. Frequency response analysis (FRA) is the current technique widely used to detect such kind of faults. The technique relies on scanning each phase in a wide frequency range to detect any discrepancies in the winding FRA signature. Current interpretation process for FRA signatures relies on graphical analysis of the magnitude plot without paying much attention to the phase angle plot. This may result in inconsistent analysis for the FRA signature as so far, there is no standardized and automated technique for FRA signature classification. To overcome this limitation, a new polar plot signature based on the integration of the FRA magnitude and phase angle plots has been proposed in the literature. While this new approach has been validated through simulation analysis, its performance on real transformers has not been assessed yet. This paper is aimed at investigating the practical feasibility of replacing the conventional FRA signature with the polar plot and automate the fault diagnosis process using digital image processing techniques. In this context, various winding faults including short-circuit disks, disk space variation, and axial disk buckling have been implemented on a custom-made, Y/Y three phase, 50 Hz, 400 kVA power transformer. The low voltage winding of the investigated transformer comprises 12-turn helical type while the high voltage winding is made of composite structure comprising 10 disks of interleaved winding on the top, 10 disks winding in the middle and 10 disks of continuous winding in the bottom. Impact of various levels and locations of these faults on the FRA polar plot is investigated through extracting digital image texture analysis features. To ease the classification process, a unique classification metric namely image sum square max-min ratio error is calculated based on the extracted features.

With the growing global interest in developing future smart grids, nonconventional instrument transformers have been given much attention. In contrary with the classic magnetic core current transformer, electronic current transformer (ECT) does not exhibit saturation, is of small volume, and can generate a digital signal complying with the recent digital communication protocols such as the IEC61850. However, the measurement accuracy of the ECT is influenced by the mechanical vibration caused by the adjacent circuit breaker (CB) operation and the bus bar electromagnetic forces generated due to short-circuit currents. According to the GB/T 20840.8 and the IEC 60044-8 standards, the root mean square of the ECT output current should not exceed 3% of the rated secondary current after 5-ms duration following a CB operation or a large current of a short duration. Current ECT vibration compliance test does not correlate the ECT output current with the CB or the large current signal to correctly identify their ending moment. As such, the current testing technique may reveal inaccurate outcomes. This paper proposes a new vibration testing platform for ECT in which the status of the vibration source is synchronously correlated with the ECT output signal. A status monitoring circuit is proposed and implemented to achieve synchronous acquisition and comparison of the CB status and the ECT output current. In addition, a primary current sensor is designed to accurately realize the end time of any short-time large current. A software platform is developed to correctly identify the CB breaking moment and the end of the large current. The robustness and superiority of the proposed testing platform are validated through extensive simulation and practical analyses.

Electronic instrument transformers are expected to play a significant protection and control role in the future smart grids. This paper aims at improving the performance and accuracy of electronic instrument transformers that are replacing the conventional current and potential transformers in the future digital substations. One of the main issues associated with the electronic instrument transformer is its poor thermal stability that affects the accuracy of the measurements. In this paper, to overcome this issue, a new wireless temperature measurement and compensation technique is proposed. In this technique, the operating temperature of the electronic transformer is continuously monitored and its impact on the measurements is compensated in real-time. The experimental results reveal that the proposed technique can effectively improve the thermal stability and measurement accuracy of the electronic instrument transformers.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

This paper presents a new cost-effective technique to improve the performance of doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS) during wind gust and fault events through the integration of high temperature superconductor (SC) within the DC link of the rotor side and grid side converters that interface the DFIG rotor with the grid. Fractional order proportional integral (FOPI) controller is employed to control the energy exchange between the SC and the system through controlling the duty cycle of the DC chopper interfacing the SC with the DC link. Rating of the SC and parameters of the proposed FOPI controller are precisely calculated using harmony search optimization technique in order to regulate the DFIG generated power and improve its fault ride through capability during disturbance events. To validate the superiority of the proposed FOPI controller, the performance of the studied system is also investigated when a conventional PI controller is employed. Results reveal the ability of the proposed topology and controller to improve the performance of the DFIG-based WECS during wind gust and short circuit faults at the DFIG terminals.

Owing to its modular construction, ability for bi-directional power flow and suitability for AC/DC grids, solid-state transformer (SST) is expected to be the backbone of the future smart grids. One of the main drawbacks of SST is the generation of negative-sequence current component at its input stage under unbalanced distribution system which causes adverse impacts on the power quality of the electricity grids. This paper is aimed at proposing a novel unbalance compensation method based on reduced order generalized integrator to suppress the negative-sequence current. Unlike the conventional sequence compensation method that is based on dual synchronous reference frames, the new proposed method does not involve complex calculation of the command current and sequence decomposition. As such, the response speed of the compensation controller is significantly improved. Additionally, the proposed method is easy to implement when compared with the current conventional compensation technique as there is no need to inject sequence components into the grid. A simulation model of three-module cascaded SST with three-phase star connection is established in Matlab/Simulink. Several case studies are carried out under different operating conditions. Simulation results validate the feasibility of the proposed method.

Power transformers are located in the electrical transmission and distribution networks where different voltage levels are needed. The turn ratio of the low voltage and high voltage windings is mechanically controlled by an on-load tap changer or de-energized tap changer. As the tap changer is the transformer’s only moving part, it is highly susceptible to mechanical failure and aging degradation. While some diagnostic tools have been used to determine the mechanical condition of tap changer contacts, not much attention was given to use the frequency response analysis to diagnose the transformer’s tap changers’ mechanical integrity. This paper is taking one step forward into maturing the application of the frequency response analysis (FRA) technique to detect transformer tap changer faults. In this regard, two common tap changer faults are created, and experimental testing for four FRA test configurations is conducted. For a better understanding of the tap changer fault mechanism, an electrical equivalent circuit model is proposed and designed using Simulink. The simulation and implementation of the equivalent circuits using MATLAB\R2018a.

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.

To minimum the capacity of rotor-side SMES and reduce the cost, the capacity ratio between energy storage converter (ESC) and RSC are redesigned. Dual control strategy is utilized into ESC controller to eliminated the influence of negative sequence component of rotor current during voltage unbalance. Two kinds of control target are analyzed in detail and the control diagram in ESC has been designed. Simulation has been done to validate the effectiveness of proposed method. Simulation results express that the controller can precisely carry out the two control targets.

The electronic voltage transformer (EVT) has received much attention with the recent global trend to establish smart grids and digital substations. One of the main issues of the EVT is the deterioration of its performance with long-term operation which affects the control and protection systems it is employed for and hence the overall reliability of the power grids. This calls for the essential need for a reliable technique to regularly assess the accuracy of operating EVT in real-time. Unfortunately, traditional calibration methods cannot detect the incipient EVT performance change in real-time. As such, this paper presents a new online method to evaluate the accuracy of the EVT. In this regard, the Q-statistic is calculated based on the recursive principal components analysis (RPCA) using the output data of EVT to map up the changes of metering error on the electric–physics relationship. By employing the output data of the EVT along with the power grid characteristics, the performance of the EVT is evaluated without the need for a standard transformer, as per the current industry practice. Results show that the proposed method can assess the EVT with a 0.2 accuracy class.

Wind energy has been identified as the second dominating source in the world renewable energy generation after hydropower. Conversion and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system (WECS) including multilevel inverters (MLIs). The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link capacitor. This paper proposes a simplified proportional integral (PI)-based space vector pulse width modulation (SVPWM) to minimize the output waveform ripples, resolve the voltage balancing issue and produce better-quality output waveforms. WECS experiences various types of faults particularly in the DC-link capacitor and switching devices of the power converter. These faults, if not detected and rectified at an early stage, may lead to catastrophic failures to the WECS and continuity of the power supply. This paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real time. Since most wind power plants are located in remote areas or offshore, WECS condition monitoring needs to be developed over the internet of things (IoT) to ensure system reliability. In this paper, an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

Frequency response analysis (FRA) is considered as the most popular and reliable method to detect mechanical deformations within power transformers. Despite this popularity, interpretation of FRA signatures has not yet been standardized worldwide. Correct interpretation of FRA signatures results in reliable diagnosis of the transformer mechanical integrity which facilitates timely and proper remedial action. As a further step towards the full understanding of the analysis of FRA signatures, this paper presents a k-means method to cluster power transformers under different fault types. In this regard, series of FRA measurements has been conducted on various transformer models under different fault types. Then, a feature based on interval maximum to global maximum (IMGM) is extracted from the obtained FRA measurements to facilitate data clustering. Using grasshopper optimization algorithm (GOA), the centers of the clusters are determined and by applying the data obtained from operating transformers, the performance of the proposed method is evaluated and compared under different cases.

This paper presents a novel three-phase DC-link multilevel inverter topology with reduced number of input DC power supplies. The proposed inverter consists of series-connected half-bridge modules to generate the multilevel waveform and a simple H-bridge module, acting as a polarity generator. The inverter output voltage is transferred to the load through a three-phase transformer, which facilitates a galvanic isolation between the inverter and the load. The proposed topology features many advantages when compared with the conventional multilevel inverters proposed in the literatures. These features include scalability, simple control, reduced number of DC voltage sources, and less devices count. A simple sinusoidal pulse-width modulation technique is employed to control the proposed inverter. The performance of the inverter is evaluated under different loading conditions and a comparison with some existing topologies is also presented. The feasibility and effectiveness of the proposed inverter are confirmed through simulation and experimental studies using a scaled down low-voltage laboratory prototype.

Digital Communication systems have matured to a point of acceptance in the last decade enormously based on IEC 61850 guidelines from Substation Automation System (SAS) perspective. However, these networks have issues related to latency, data clogging delays, errors etc. in the digital protection system. Measuring propagation delays and assessing the performance of IED’s and other peripherals in an Ethernet topology play a critical role in relay coordination setting. This paper discusses issues associated with a digital network and addresses the problems that could mitigate spurious tripping or compromise the protection of the assets in the feeders leading to a reliable operation of the protection system. The discussion in this paper is carried out based on a case study related to a digital star topology using Optimized Network Engineering Tool (OPNET) software of a 132/22-kV zone substation. We report the practical case of measuring delay time during frame exchanges of Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV) messages amongst the IED’ and peripherals.

One of the key advantages of cascaded multilevel inverters (CMLI) is their ability to generate medium voltage output by using low voltage rated circuit components. For this reason, CMLI has been given much attention in renewable and industrial applications. However, in spite CMLI advantages, balanced input dc voltage management at the cascaded cells is still considered one of the main drawbacks, which limits its straightforward applications. Moreover, galvanic isolation between the input dc supply and the inverter output voltage is essential for grid-connected application. In such case, a step-up transformer is utilized between the inverter output terminals and the grid. This solution incurs additional cost, increases implementation size, weight and maintenance. In this paper, a CMLI is proposed for medium voltage applications by utilizing high frequency magnetic link to ensure galvanic isolation without the need to a conventional step-up transformer as per the current practice. 3rd harmonic-injected sine pulse width modulation strategy is adopted as a switching controller for the proposed cascaded inverter that is implemented and tested. Experimental results attest the simulation results and confirm the feasibility of the proposed inverter.

One of the main drawbacks of the frequency response analysis (FRA) technique that is widely accepted as the most reliable tool to detect transformer internal mechanical deformations is the inconsistent interpretation of the measured signature because of its reliance on personal expertise more than standard codes. Moreover, conventional FRA signature has a very low accuracy in detecting incipient and low mechanical fault levels. In order to avoid inconsistent interpretation for the transformer FRA signatures and improve its accuracy to detect minor fault levels, a reliable automated technique has become essential. This study investigates the feasibility of utilising FRA polar plot to detect minor radial deformation levels within two, three-phase power transformers of different ratings and winding configurations simulated using three-dimensional finite-element analysis software. Simulation results are validated through experimental measurements. Results of this study are also compared with the results obtained for other types of transformer winding deformations that are published in the literature in order to identify unique impact for each fault type on the proposed method. Findings reveal the superiority of the proposed approach over existing conventional technique in terms of accurate identification and quantification for minor transformer winding deformations.

Power transformer is a critical asset in electrical transmission and distribution networks that need to be carefully monitored during its entire operational life. Considering the fact that a significant number of worldwide in-service power transformers have approached the end of their expected operational life, utilities have adopted various transformer condition-based maintenance techniques to avoid any potential catastrophic failure to the equipment. The extent of transformer insulation system ageing can be quantified through measuring several diagnostic indicators such as interfacial tension number of the insulating oil which has a strong correlation with the number of transformer operating years. Moisture and furanic compounds generated due to paper insulation degradation are indicators for solid insulation ageing. This paper introduces a new adaptive neuro fuzzy logic model to estimate the life of mineral oil-filled power transformers based on the values of insulating oil interfacial tension number, furan content in oil and the moisture content within the cellulose insulation. Also, an integrated asset management decision model is proposed. Results of the proposed model are validated against practical data collected from utility and industry mineral oil-filled power transformers of different ratings, designs, operating conditions and lifespans.

Power transformers play a significant role in maintaining the reliability of electrical power transmission and distribution networks. The probability that a transformer will fail increases with the aging of its insulation system [1]. Core deformation and winding movement may be caused by severe electromagnetic forces generated during short circuit faults [2]–[5]. Although a transformer may still work normally with minor axial or radial winding deformations, accumulating effects of these deformations may lead to sudden catastrophic failure of a transformer [6]. According to an international CIGRE survey conducted on power transformers covering the period 1996 to 2010, 19.4% of transformer failures during this period were attributed to windings- related problems [7]. It is therefore essential to assess the mechanical integrity of transformers which have been exposed to severe short circuit faults or lightning, or relocated, and take appropriate remedial action.

Winding deformation is a critical power transformer issue that needs to be detected as soon as it emerges due to its progressive nature and severe consequences it may lead to. Frequency response analysis (FRA) is the only reliable technique currently used to detect such deformation. However, the technique is conducted offline that may cause interruption to the electricity grid when taking an operating transformer out of service for testing. Moreover, as the current FRA technique is only conducted on suspected units, a mechanical fault may emerge and progress before the utility becomes aware of. To overcome these limitations, this paper presents a practical feasibility study to detect transformer winding deformations online. The technique relies on utilizing transient overvoltage signals that a transformer is subjected to during its normal operation as a natural variable frequency excitation source. The feasibility of utilizing various transient overvoltage signals as excitation source for transformer FRA signature measurement is firstly investigated and optimum signal parameters are recommended based on signal voltage and energy spectrum analyses. Secondly, the proposed technique is verified through simulation analysis by comparing the transformer frequency response signatures when transient overvoltage and conventional sweep frequency voltage are used as excitation sources. Also, experimental testing is conducted to assess the feasibility of the proposed technique to identify various winding deformations such as radial buckling, telescoping and short circuit turns. Custom-made capacitive voltage divider is developed to accurately detect the response of transient overvoltage signals generated due to environmental conditions or power system switching operations. The proposed technique along with the developed capacitive voltage divider is implemented on a 3-phase Wye-Delta, 50 Hz, 31.5 MVA, 110/10.5 kV power transformer to measure the frequency response signatures of the high voltage winding.

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.

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.

Dissolved gas analysis (DGA) is widely accepted as an effective technique to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are generated as a result of insulation oil decomposition, while carbon monoxide and carbon dioxide are generated as a result of insulation paper decomposition. Laboratory-based gas chromatography (GC) in accordance to ASTM D3612 standard is globally accepted as a reliable technique to quantify dissolved gases in transformer oil samples. However, this technique incurs running cost and requires an expert to conduct the test. Furthermore, due to the complexity of the equipment, measurement is only performed in a laboratory environment and takes long time to get the results since the extraction of the oil sample from the transformer. To overcome these issues, various online DGA techniques have been developed and currently used by industry. However, these techniques are still considered in development stage with some limitations that have to be addressed to fully accept its results. This paper proposes a new method to estimate the concentration of various dissolved gases in transformer oil using near infrared-to-infrared (NIR-IR) spectroscopy that can be conducted instantly onsite with relatively inexpensive equipment. The proposed measuring technique does not call for expert person to conduct the measurements and has the potential to be implemented online. Results show a good correlation between oil spectral response and the concentration of each dissolved gas in the oil at particular wavelength range. Fuzzy logic model approach is employed to model this correlation and estimate the concentration of each dissolved gas.

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.

This paper presents a finite element modelling (FEM) of a 126 MW salient pole synchronous generator considering rotor eccentricity fault. The simulation model uses a 2-D time-stepping finite element analysis to calculate the induced voltage and current of the stator winding. The line voltage waveforms under healthy and faulty conditions are analyzed and compared. Results show that harmonic contents in the obtained waveforms under eccentricity fault differ from the one obtained under normal operating conditions. This can be used as a base for detecting rotor eccentricity fault. The investigated synchronous generator is designed with 2 parallel connected windings. During normal conditions, there is no current circulating in these parallel branches. When rotor eccentricity takes place, a large circulating current emerges between the parallel branches. This paper also analyzes this circulating current through simulation analysis carried out using Ansys Maxwell software.

Power transformer is one of the most important assets for any electricity utility. The overall power transformer health condition can be assessed through the condition of its insulation system. Recent result of transformer oil measurements for parameters such as furan, dissolved gas analysis, water content, breakdown voltage, interfacial tension, acidity, and oil color scale are commonly used to assess the health condition of power transformers. Degradation rate of power transformer insulation system, especially paper insulation, reflects the transformer aging condition and can be used to estimate the useful remnant life of the asset. Unfortunately, continuous aging process of power transformer insulation system cannot be avoided due to the severe operating conditions that a transformer is operating on. So, it is very important to regularly monitor the condition of power transformer insulation to maintain its reliability. Historical data of transformer insulations are very substantial to define the history of power transformer health condition. This paper introduces two methods, first one to assess the degradation rate of power transformers insulation, and then estimate the insulation condition index based on historical data.

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

With the global trend to produce clean electrical energy, the penetration of renewable energy sources in existing electricity infrastructure is expected to increase significantly within the next few years. The solid state transformer (SST) is expected to play an essential role in future smart grid topologies. Unlike traditional magnetic transformer, SST is flexible enough to be of modular construction, enabling bi-directional power flow and can be employed for AC and DC grids. Moreover, SSTs can control the voltage level and modulate both active and reactive power at the point of common coupling without the need to external flexible AC transmission system device as per the current practice in conventional electricity grids. The rapid advancement in power semiconductors switching speed and power handling capacity will soon allow for the commercialisation of grid-rated SSTs. This paper is aimed at introducing a state-of-the-art review for SST proposed topologies, controllers, and applications. Additionally, strengths, weaknesses, opportunities, and threats (SWOT) analysis along with a brief review of market drivers for prospective commercialisation are elaborated.

As a vital piece of equipment, electronic voltage transformer is widely used in the power system measurements. The current calibration techniques of this asset class exhibit several issues including complex real time implementation. In contrary with existing calibration techniques, this paper proposes a new correction system for electronic voltage transformers, which can be conducted online without causing any interruption to the electricity grid. The new proposed correction system utilizes an SF6 cylinder capacitor and employs an advanced signal processing technique based on the improved Rife frequency shift algorithm. The improved Rife frequency shift algorithm is a combination of the Hanning self-convolution window and the conventional Rife algorithm, which features simple calculation and high accuracy. Results show that the accuracy of the proposed algorithm is higher than the conventional commonly used interpolation algorithms. Compared with the existing traditional calibration techniques, the proposed system features easy online implementation, simple calculation and high accuracy. The robustness of the proposed system is validated through simulation analysis and practical field measurements.

With the global trend to digitalize substation automation systems, International Electro technical Commission 61850, a communication protocol defined by the International Electrotechnical Commission, has been given much attention to ensure consistent communication and integration of substation high-voltage primary plant assets such as instrument transformers, circuit breakers and power transformers with various intelligent electronic devices into a hierarchical level. Along with this transition, equipment of primary plants in the switchyard, such as non-conventional instrument transformers, and a secondary system including merging units are expected to play critical roles due to their fast-transient response over a wide bandwidth. While a non-conventional instrument transformer has advantages when compared with the conventional one, extensive and detailed performance investigation and feasibility studies are still required for its full implementation at a large scale within utilities, industries, smart grids and digital substations. This paper is taking one step forward with respect to this aim by employing an optimized network engineering tool to evaluate the performance of an Ethernet-based network and to validate the overall process bus design requirement of a high-voltage non-conventional instrument transformer. Furthermore, the impact of communication delay on the substation automation system during peak traffic is investigated through a detailed simulation analysis.

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.