Transformer Energization Research Articles

Enhanced analysis of oscillatory undamped overvoltages in transformer energization

This paper proposes an enhancement on the methodology commonly used for overvoltage analysis motivated by the excitation of electric network resonance points due to injection of harmonic currents by equipment with nonlinear characteristics. The improvement proposed comprehends inclusion of effects caused by nonlinear equipment into the frequency scan study, harmonic content survey, transformation of currents and/or voltages from phase to sequence domain, and application of the Windowed Fast Fourier Transform (FFT). This enhanced methodology was applied in the investigation and identification of unexpected resonant behavior causes observed in pre-operational electromagnetic transient studies for an autotransformer energization.

Prototype of a Negative-Sequence Turn-to-Turn Fault Detection Scheme for Transformers

Digital relays are capable of computing the negative-sequence current on both primary and secondary sides of the transformer along with the phase difference between these two negative-sequence currents. By using both phase and magnitude information, negative-sequence current could be used to detect minor turn-to-turn faults involving 3% of the transformer's windings or more. Turn-to-turn faults may still occur even if no current is flowing on one side of the transformer, such as during energization. With no current flowing in the secondary windings of the transformer, negative-sequence current-based algorithms become insensitive. This paper introduces a relay prototype, using both negative-sequence current and negative-sequence voltage, which retains its sensitivity during energization. The relay's performance for several commonly encountered system scenarios, such as overexcitation, current-transformer saturation, nonzero fault resistance, transformer energization, and external faults were also examined. The experimental results presented in this paper indicate that the algorithm proposed in this paper is faster and more sensitive than restrained current differential protection and is capable of detecting turn-to-turn faults occurring during transformer energization.

Transformer Switching With Vacuum Circuit Breaker: Case Study of PV Inverter LC Filters Impact on Transient Overvoltages

This paper presents the analysis of possible transient overvoltages that can be generated during vacuum circuit-breaker (VCB) operation at the connection point of a photovoltaic power-plant. A typical switching event that is related to the VCB concerns energization and de-energization of the unloaded transformer. However, at the typical photovoltaic powerplant, the transformer is fed by an inverter equipped with LC (or LCL) filters that are necessary for limitation of harmonics and ripple in voltage and current. From the point of view of switching operations, the inductance and capacitance of the low-voltage (LV)-side connected filter affect the natural frequency of the transformer, which is reflected by different transient system response during VCB operation. In this paper, laboratory measurement of overvoltages generated during distribution transformer switching by means of VCBs is analyzed. The impact of the LC filter connected at the LV side on the overvoltages suppression was studied. Electromagnetic Transients Program-Alternative Transients Program simulations were conducted in order to verify the possibility of transients elimination by means of the additional series-connected RL choke at the transformer medium-voltage side.

An Improved Differential Protection of Large Power Transformer using Fuzzy based Protective Relay for Inrush Current and Harmonic Components

This paper deals with the analysis of various operating conditions of power transformer such as energization, inrush current, over excitation and CT saturation. In this work a new relaying algorithm is used to enhance the fault detection sensitivities of conventional techniques by using a fuzzy logic approach. An improved differential protection scheme is proposed which uses voltages and current to detect the abnormalities in the different zone of protection. The magnetizing inrush current during transformer energization is taken with a simplified excitation curve. Discrete fourier transform is used for extraction of fundamental and higher harmonic components of the differential current. The fuzzy based protective relaying algorithm will analyze inrush current, residual flux, saturation flux, harmonics restraint and percentage differential characteristics curve. This method of fuzzy based protective relaying algorithm is tested with MATLAB simulink model and a performance comparison of conventional scheme and fuzzy logic based protection scheme is provided.

Arc Flash Hazard Reduction at Incoming Terminals of LV Equipment

Where transformers are used to step down from medium voltage to low voltage, a widely used low-cost solution for primary protection is a fused load break switch. While this device provides good short-circuit protection, it provides limited protection for arcing faults on the transformer secondary and the incoming terminals of the downstream equipment connected to it. Consequently, the arc flash (AF) energy levels in this region can be very high ( $ > 40 \mbox{cal/cm}^2$ ) when compared with the rest of the electrical distribution system and may result in dangerous operating conditions. This paper examines a range of options for primary and secondary protection, identifying those that may result in higher levels of AF energy, and presents guidelines for the selection and application of solutions that have been implemented in existing and new installations, achieving a significant reduction in incident energy levels $( , while addressing concerns for reliability and selective coordination. Piecewise, linear modeling of transformer energization transient response is presented and compared with conventional engineering practice for selecting primary protection. Common assumptions made in modeling instantaneous overcurrent elements will be analyzed, modified, and exploited to achieve reliable application of blocking signals between devices in a protective scheme. Finally, the physical layout of electrical equipment will be discussed as a criteria for optimizing protection schemes to achieve safety by design.

Assessment on energization sequence for reducing sympathetic interaction between wind turbine transformers

Increasing installations of large offshore wind farms are being designed and commissioned. The collection grids of these wind farms usually accommodate hundreds of wind turbine transformers. Energization of these transformers is likely to cause sympathetic inrush due to the close electrical distances between them. The wind turbine transformers are consequently subjected to long duration mechanical and thermal stresses under various energization conditions. In this paper, a network model of wind farm collection grid, developed and validated against field measurement results, is utilized to assess various energization sequences for reducing the sympathetic inrush. Deterministic assessment under a critical energization condition and stochastic assessment under stochastic energization condition were conducted. Both indicate that the optimum sequence is to energize wind turbine transformer from the one closest to the offshore platform to the one farthest from the platform. Copyright © 2014 John Wiley & Sons, Ltd.

Voltage Stress in a Transformer Winding During Very Fast Transients Caused by Breaker Closing Event

Transformers connected at the medium-voltage level in cable grids, such as wind park collection grids and industrial grids, are exposed to the stress of very fast transients. These electric transients are mainly generated during breaker switching operations and the rise time of the transient voltage in such systems is much shorter compared to the rise times of transients generated in transmission systems at a high-voltage level. In this paper, the internal voltage stress is studied during very fast transients generated during transformer energization. Instead of using a breaker, the energizing tests are performed using a low-impedance pulse generator that can generate lightning impulse-shaped waveforms and voltage steps with rise times varying between 35 and 500 ns. Experiments show that during very fast transients with a 35-ns rise time and 1-p.u. magnitude, the interturn voltage exceeds the level obtained with a lightning impulse-shaped voltage waveform of 4.4 pu. Furthermore, during a specific switching scenario with delta-connected transformers, where the winding is excited from both ends, the same 1-p.u./35-ns voltage step generates an interturn voltage that exceeds the 1-p.u. level, which is more than 2.5 times higher voltage stress than during a lightning impulse test.

Power Quality Improvement and Sag Voltage Correction by Dynamic Voltage Restorer

Power quality is a very important topic of the recent power system network topics. It refers to maintaining a sinusoidal waveform of bus voltages. Unfortunately, the power system faces many disturbances which affect the supply waveform, such as under voltage, notching, harmonics, sag and swell voltages. In practice, sag voltage and harmonics are the major problems which can cause malfunctioning or tripping of equipment and many other problems on the system. One of the causes of these events is the inrush current during the energization of no-load transformers. This paper presents an effective and economic solution for the protection of sensitive loads from sags and harmonics using the dynamic voltage restorer (DVR). It also highlights the full functions of the DVR, showing that it can be considered as a proper solution to overcome the inrush current effects. Sag indices concepts on the distribution system are also presented, showing the performance of the DVR to get better results. The increasing of the DVR effectiveness as well as its simplicity and low cost are reasons for considering it as the best solution for many quality disturbances. Simulation results using MATLAB/SIMULINK are presented to verify the effectiveness of the proposed scheme.

765 kV power transformer losses upon energizations: A comparison between field test measurements and EMTP-RV simulations

Transformer energization tests were performed in Hydro-Québec Chateauguay substation on a 300 MVA single-phase 765/125/12.5kV Yg-Yg-Δ power transformer in order to improve power transformer modeling for harmonic over-voltage evaluations upon energizations and fault removals close to HVDC converters with associated filters and capacitor banks. From these results, an enhanced transformer model has been developed in EMTP-RV Software. Subsequent simulations show that accurate modeling of the power transformer losses does only have minor influences on harmonic over-voltages damping. It is shown however that the usage of the proposed model is essential to predict adequately the risks of ferroresonance and generated over-voltages in power systems.

Power Transformer Protection Using a Multiregion Adaptive Differential Relay

This paper presents a new adaptive approach to improve the performance of the power transformer differential relay. The proposed approach distinguishes internal faults and disturbances based on the differential current trajectory and adopted weighting factors depending on the differential current locus in the relay characteristic. The traditional dual-slope differential characteristic is divided into three operating regions with the corresponding weighting factors. Two other control regions, that is, inrush and current-transformer (CT) saturation detection regions are utilized to avoid maloperation of the proposed relay in the case of inrush and CT saturation conditions. Meanwhile, a combination of phase differential units is introduced as a new technique to decrease relay operation time. The proposed relay rapidly detects all studied internal faults, while it is stable during transformer energization and CT saturation due to external faults. A real 230/63-kV power transformer is simulated using PSCAD/EMTDC for evaluating the performance of the proposed relay. Extensive simulation studies show that the proposed approach results in a faster and smarter differential protection scheme.

Application of Hilbert--Huang transform and support vector machine for detection and classification of voltage sag sources

Power quality disturbances, including voltage sag, swell, harmonics, flicker, and notch, are one of the main concerns for industries and electrical equipment. Among these disturbances, voltage sag, due to its irrecoverable economic effects on industries, is particularly important. In this paper, the detection and classification of voltage sag sources containing motor starting, short circuit, transformer energizing, and the reacceleration of motors after fault clearance using the Hilbert--Huang transform (HHT) and support vector machine (SVM) are studied. A voltage sag waveform includes several oscillating modes; for separating these oscillating modes, which are called intrinsic mode functions (IMFs), empirical mode decomposition is used. Next, by applying the HHT to these IMFs, some required features of each IMF are extracted. Finally, these features are given to the SVM for classification. The results of this classification method as compared with other methods show the high efficiency of the proposed method.

Stochastic assessment of voltage dips caused by transformer energisation

Energisation of large power transformers may cause significant voltage dips, of which the severity largely depends on a number of parameters, including circuit breaker closing time, transformer core residual flux and core saturation characteristic, and network conditions. Since most of the parameters are of stochastic nature, Monte Carlo simulation was conducted in this study to stochastically assess the voltage dips caused by transformer energisation in a 400 kV grid, using a network model developed and validated against field measurements. A dip frequency pattern was identified over 1000 stochastic runs and it was found to be sensitive to residual flux distribution but insensitive to closing offset time distribution. The probability of reaching the worst case dip magnitude (estimated under the commonly agreed worst energisation condition) was found to be lower than 0.5%; about 80% of the dips are likely to be with magnitudes lower than 0.6 pu of the worst case. Nevertheless, there are dips with magnitudes exceeding the worst case dip magnitude, indicating the inadequacy of deterministic assessment approach by using the commonly agreed worst energisation condition.

Influence of sympathetic inrush on voltage dips caused by transformer energisation

Energising a transformer with other adjacent transformers in service may induce sympathetic inrush that could lead to long duration voltage dips. This study addresses this issue through analysing voltage dip events observed on a 400/132 kV grid when energisations of several generator step-up transformers were performed. With the help of a number of field measurements of voltage dips involving sympathetic inrush, a detailed network model in alternative transients program/electromagnetic transients program has been developed and validated. Based on the network model, comprehensive simulation studies have been performed. In addition, because of the nature of multiple controlling parameters, sensitivity assessment has been carried out to identify the key influential parameters. It is found that the sympathetic inrush, although does not affect voltage dip magnitude, can prolong voltage dip duration by 143% under the worst energisation condition. The sympathetic inrush occurring in substation transformers gives rise to a network-wide sympathetic interaction, resulting in further prolonged voltage dips on the 132 kV grid. Finally, it is proven that application of static var compensator is able to speed up the voltage dip recovery.

A Model for the Ultra-saturation Phenomenon During Energization of an Unloaded Power Transformer and Its Effect on Differential Protection

Ultra-saturation phenomenon is one of the false trips of the power transformer differential protection during unload transformer energization with additional line/load from the supplying side. This article presents, at first, a new model for investigating the ultra-saturation phenomenon during unload power transformer energization with additional line/load from the supplying side and considers its effect on the differential protection of the power transformer. In this model, the non-linear characteristic of the power transformer core and the effect of current transformer are taken into account. It is assumed that the additional line/load from the supplying side of the power transformer is a resistive and an inductive load. Also, the effect of the residual flux, inception angle, and additional line/load from the supplying side on the ultra-saturation phenomenon is investigated.

Modeling and Analysis of Voltage Sag Sources in Distribution Network

The mechanism of voltage sags due to three main voltage sag sources, such as line faults, startup of large induction motor and transformer energization, was analyzed theoretically. Based on the typical structure of the distribution network of a large industrial enterprise, the paper presented a simplified analytical model for voltage sag sources and conducts a test with the above three kinds of voltage sag sources in Matlab/Simulink. The results show the proposed model is correct and suitable for the analysis of distribution network.

Analysis of Transformer Inrush Current and Comparison of Harmonic Restraint Methods in Transformer Protection

It is well known that differential protection is most suitable for transformer protection. However, inrush current due to transformer energization can appear as fault to the protective relay. To improve the security while maintaining the required levels of sensitivity, many restraint methods have been proposed to inhibit operation of the differential element. This paper first will analyze the magnetizing inrush current during transformer energization with a simplified excitation curve. It derives mathematical equations to compute the inrush current based on the residual flux and saturation flux for the worst case energization event. This paper reviews several popular restraint methods used today. A conceptual logic diagram is provided for each method. Advantages and disadvantages of different methods are analyzed for various systems. Finally, using data recorded from real life events and data generated by digital simulations, a performance comparison of different methods is provided.

The Study of Switching Overvoltages under Power System Restoration Scenario Using Extended Delta-Bar-Delta Algorithm

Abstract This paper presents an intelligent approach to evaluate switching overvoltages during power equipment energization. Switching action is one of the most important issues in power system restoration schemes. This action may lead to overvoltages that can damage some equipment and delay ‎power system restoration. In this work, transient overvoltages caused by power equipment energization are analyzed and estimated using artificial neural network (ANN)-based approach. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. In the cases of transformer and shunt reactor energization, ANNs are trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

Preventing Transformer Energizing Resonant Overvoltages Using Surge Arrester Temperature Rise Index and Controlled Closing Method

Resonant overvoltages caused by transformer energization in relatively weak systems, especially during power system restoration, are investigated in this paper. These transient overvoltages may result in failures, especially for surge arresters. A theoretical analysis is performed to obtain the transformer inrush current harmonics and the transmission-line lengths for which the harmonic resonance is likely to occur. The temperature rise of surge arresters is proposed as an index to diagnose a harmonic resonance case. An appropriate electrothermal model for metal-oxide surge arresters is developed using the thermal model and proposed combined electrical model. Comprehensive transient simulation studies are performed to find out the harmonic resonance cases using the proposed index. Obtained simulation results are in good agreement with the analytical results. To reduce such harmonic resonance cases, controlled closing of the transformer can be utilized. In this paper, the proper instants for transformer energization are proposed in such a way that the overvoltage is reduced to acceptable values.

Delta-Bar-Delta and Directed Random Search Algorithms Application to Reduce Transformer Switching Overvoltages

This paper proposed an artificial neural network (ANN)-based approach to mitigate harmonic overvoltages during transformer energization. Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching overvoltages. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that it's minimum value is corresponding to the best case switching time. Also, in this paper three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train ANNs to estimate the optimum switching instants for real time applications. ANNs training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.

Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration

This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages.