Transformer Inrush Currents Research Articles

Dependability Analysis of the Negative-Sequence Turn-to-Turn Fault Protection Schemes for MMC-HVDC Converter Transformers

This paper addresses two complex areas of research: the detection of turn-to-turn faults (TTF) in power transformers and the impact of inverter-based resources on the TTF protection scheme operation. Detecting turn-to-turn faults in power transformers by protection algorithms poses a challenge due to the minimal fault currents observed at transformer terminals. Yet, the demand for dependable TTF protection is very high because of the high fault currents inside the shorted turns and the resulting damage consequences. On the other hand, for such sensitive protection, adverse conditions such as transformer inrush currents or CT errors may lead to protection maloperation. Moreover, the fault current characteristic of the inverter-based source infeed is very different compared to the synchronous machine infeed, particularly concerning the negative-sequence current used in the TTF protection schemes, which calls for thorough research analysis. A simulation model of the converter transformer capable of simulating TTFs, and the Modular Multilevel Converter (MMC) for a High Voltage Direct Current (HVDC) transmission link, has been developed. The test results of turn-to-turn fault protection schemes in inverter-based generation-dominated power systems compared to the synchronous generator infeed are presented. The negative-sequence current protection quantities are analysed in more detail for TTFs with small and large number of shorted turns, i.e. without and with reactive negative-sequence current injection by the MMC control. Finally, the paper assesses the dependability of the transformer differential protection and sensitive TTF protection schemes in detecting faults with different numbers of shorted turns and fault resistance for TTFs occurring in the star and delta winding of the converter transformer.

Application of saturated core transformer as inrush current limiter

An inrush current is a transient current that results from a sudden change in the exciting voltage across a transformer's windings. Reducing the inrush current of transformers is desirable due to mechanical stresses and negligent operation of protective relay systems such as differential relays. This paper investigates the application of a saturated-core transformer as an inrush current limiter (ICL). Analytical methods of inrush current and its electromagnetic forces on transformer windings with and without application of proposed ICL are presented. Also, the core saturation of the ICL and transformer, in addition to the voltage drop on the ICL in transient and steady states, are investigated. A single-phase transformer is modeled in two dimensions (2-D), and the forces due to inrush current are calculated by the finite element method (FEM) in Maxwell software. The radial and axial force distributions of transformer windings with and without the ICL are investigated. The results show the capability of ICL to limit the transformer inrush current and reduction of its electromagnetic forces. Our simulations demonstrate a significant reduction in inrush current (75 %) using the proposed Inrush Current Limiter (ICL). Additionally, the ICL effectively mitigates electromagnetic forces within the transformer. Compared to a scenario without ICL, the ICL reduces radial forces by 80 % and axial forces by a substantial 90 %. These findings suggest that the ICL offers a promising solution for mitigating both inrush current and the associated electromagnetic forces in power transformers.

Practical Experiments with a Ready-Made Strategy for Energizing a Suitable Pre-Magnetized Three-Column Three-Phase Dy Transformer in Unloaded State for Inrush Current Computations

This article presents the results of an experimental verification of three-phase Dy transformer dynamics under no-load conditions. This study is motivated by previous ferroresonance analyses where the occurrence of inrush currents has been observed. The measurements covered all available electrical quantities in a transient state (12 measured and 3 additionally computed waveforms) during the device’s start-up under no-load conditions, as well as in a long-term steady state. A detailed analytical analysis is carried out for the obtained comprehensive set of measurement results. As a result of the conducted research, the mathematical model of the pre-magnetized three-phase Dy transformer is modified. Particular attention is paid to the issue of residual magnetism of the transformer core and its consideration in further research. The original strategy for energizing a three-column three-phase Dy transformer with a suitable pre-magnetization of its columns and original control switching system with a given/set value of the initial phase in the supply voltage is put to the test. The evolution of the induced inrush phenomenon up to the quasi-steady state under given (forced) conditions is documented (currents, voltages and the dynamics of changes taking place in the core (hysteresis loops)). This article represents a continuation of ongoing work on the study of transient states (dynamics of transformer inrush currents). At present, the Dy three-phase transformer is analyzed because of the requirements of industrial operators.

A Novel High-Impedance Fault Detection Technique in Smart Active Distribution Systems

Identification of high impedance fault (HIF) is important in smart active distribution systems (SADSs). The existing protection methods fail to detect HIF, as HIFs have peculiar fault current characteristics. However, if HIFs are left undetected, they result in severe fire hazards and arcing. The paper proposes a novel fault detection indicator that utilizes the differential energy associated with the positive sequence current components during faults. IEEE-13 bus and IEEE-34 bus system is used for simulation in Real Time Digital Simulator (RTDS). The results show that proposed technique accurately identify HIFs and perfectly discriminates the transients due to HIFs, non-linear loads and voltage and reactive power compensation devices. Furthermore, it differentiates HIFs from other switching transients like capacitor or load switching events. Other factors, like transformer inrush currents, effect of noise and sampling rate are also considered to ensure the accuracy and reliability of the proposed method. Moreover, the practical feasibility of the proposed method is also experimentally validated.

Automatic identification of transformer excitation inrush based on second-order Taylor coefficient

In order to solve the time-consuming problem of the existing automatic identification methods of transformer inrush current, an automatic identification method of transformer inrush current based on second-order Taylor coefficient was designed. Ignore the leakage resistance and winding resistance of transformer winding, get the second harmonic characteristic quantity, assume the transformer variable ratio value is fixed, identify the transformer winding parameters, introduce the decay coefficient to describe the characteristic of excitation inrush current gradually decaying with time, construct the excitation inrush current Taylor expansion phase model, and set the automatic identification mode based on the second order Taylor coefficient. Experimental results: The designed transformer excitation inrush current automatic identification method and the other two transformer excitation inrush current automatic identification method of the elapsed time were: 55.32ms, 71.52ms, 71.04ms, indicating that after the integration of the second-order Taylor coefficient, the designed transformer excitation inrush current automatic identification method better performance.

Comparative Analysis between Dynamic Voltage Restorer and PWM-Switched Autotransformer in Voltage Sag Mitigation

This research aims to address the problem of voltage sag, which is a significant power quality issue in power systems. Voltage sag can lead to power transmission and distribution line tripping and cause damage to electrical equipment. Various voltage sag mitigation devices have been developed to reduce the impact of voltage sag. In this study, we analyze the effectiveness of three types of voltage sag mitigation devices under different voltage sag conditions: three-phase fault, multi-phase fault, and energizing transformer inrush current. The three types of mitigations analyzed are Dynamic Voltage Restorer (DVR) with Park's transformation, DVR with Artificial Neural Network (ANN) controller, and PWM-switched autotransformer with Proportional-Integral (PI) controller. The simulation results using MATLAB demonstrate that all voltage sag conditions have been mitigated except for PWM-switched autotransformer during voltage sag caused by energizing transformer inrush current due to limitations of the PI controller scheme. Based on the analysis of input and output voltage waveforms, three-phase voltage magnitude before and after mitigation, and Total Harmonic Distortion (THD) value after mitigation, DVR with ANN controller is identified as the most effective voltage sag mitigation device, followed by DVR with Park's transformation and PWM-switched autotransformer.

Transformer Faults Classification Based on Convolution Neural Network

This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily.

Suppression strategy for the inrush current of a solid-state transformer caused by the reclosing process

The automatic reclosing strategy is an effective measure to improve the reliability of a distribution network. It can quickly clear instantaneous faults in the grid. The traditional transformer has proven to be reliable and robust during the reclosing process. However, the influence of the reclosing process on the operational characteristics and reliability of solid-state transformers (SST) is still unclear. The reclosing action may generate a huge inrush current, resulting in shutdown and even damage of the SST. To address this problem, this paper proposes an inrush current suppression strategy. First, the operational performance of the SST under a reclosing process is discussed, and the inrush current generation mechanism is analyzed in detail. Then, considering the controllability of distributed generation (DG), a novel DG-supported inrush current suppression strategy is proposed. The suppression ability of the DG on inrush current in different initial conditions is analyzed. Finally, the effectiveness of the proposed strategy is verified by simulation and experiment. These show that the proposed strategy can help to enhance the FRT capability of the SST, as well as support the SST to maintain continuous power supply and physical integrity during grid faults.

Time domain differential protection scheme applied to power transformers

This paper presents a new differential protection scheme for power transformers based on Intrinsic Time-Scale Decomposition (ITD) and Ensemble of Bagged Tree (EBT) as a classifier. The ITD decomposes the input nonstationary signal into Proper Rotation Components (PRCs) and a monotonic trend signal. In the proposed scheme, statistical time-domain features extracted from the ITD output are fed to the EBT classifier, which classifies different events, including internal fault, internal fault combined with inrush current, different inrush currents (energizing transformer, sympathetic, and recovery inrush current), and external fault. The combination of ITD and EBT tools requires only a limited sample of post-fault data to protect the transformer. The proposed scheme is tested under various operating conditions to confirm its accuracy, and the effect of current transformer (CT) saturation on the scheme's performance is evaluated. The scheme is immune to noisy conditions and does not require transformer parameters. The power systems were simulated using PSCAD/EMTDC and MATLAB software.

Sympathetic Transformer Inrush Detection and Relay Blocking During Power System Restoration

A classic inrush current occurs during normal energization of reactive grid elements like transformers. It can simulate short-circuit currents. Hence inrush currents have to be detected and protection relays need to be blocked for avoiding their overfunction. Conventional detection criteria are the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{\textrm {nd}}$</tex-math></inline-formula> harmonic content and some kind of current waveform analysis. A classic inrush can evolve into a sympathetic inrush event, if the energization of a single transformers drives other transformers into magnetic saturation. The risk of facing a sympathetic inrush current is particularly high during a power system restoration process. Its characteristic waveform differs from the classic inrush. While the classic inrush has unipolar current peaks, the sympathetic inrush is characterized by bipolar peaks. The conventional detection criteria tend to fail in this case. This paper proposes a new approach to detect sympathetic transformer inrush currents. The approach includes the detection of classic inrush currents as well as the distinction against short-circuit currents for ensuring the relay blocking. First it introduces the basic physical mechanism behind the evolution of a classic to a sympathetic inrush. The higher risk of facing a sympathetic inrush during the restoration process is shown by the physical mechanism as well. The proposed detection method is then introduced in detail. Three new differentiation criteria will be applied and merged together to a final decision. The paper closes with practical lab tests of the new approach on simulated sympathetic and measured classic inrush currents. All lab tests showed good results and promise a practical implementation in the grid.

Discrimination between internal faults and inrush currents in power transformers based on the discriminative-feature-focused CNN

Overcoming the drawback of the incorrect action of differential protection induced by the inrush current is a crucial issue for transformer protection. This paper proposes a deep learning protection scheme based on a discriminative-feature-focused convolutional neural network to improve the protection accuracy. Considering that internal faults and inrush currents can be reliably distinguished based on the unsaturated part of the differential current, the three-module network and specialized loss function are designed to guide the proposed network to extract the discriminative features of the unsaturated part of the differential current. By focusing on discriminative features with invariance and robustness, the proposed method presents high accuracy and remedies the problem of the limited generalization ability of artificial-intelligence-based protection in engineering applications. Test results on various datasets show that the proposed scheme can accurately identify transformer states with good robustness to noise and generalization to complex scenes, such as CT saturation, overexcitation, and field data. A time-consuming test deployed on hardware shows that this algorithm meets the protection time requirements. The comparison with previous methods confirms the superior reliability and generalization ability of the proposed scheme.

Accurate transformer inrush current analysis by controlling closing instant and residual flux

This paper proposes a comprehensive process for analyzing accurate transformer inrush current by elaborately controlling the closing instant of a circuit breaker and the level of residual flux in the iron core. The effectiveness of the proposed analyzing process is verified with a laboratory-scaled test system consisting of a 100 kVA 380/320 V Dyn11 three-phase dry-type transformer and a thyristor-based point-on-wave device. Moreover, the measured inrush currents by the proposed process are compared with those calculated by widely accepted inrush current equations. The proposed analyzing process is highly effective to demonstrate the inrush current under the specified energizing condition concerning the closing instant of the voltage and the residual flux in the iron core. The results show that there is a big difference between the measured and calculated inrush currents under the identical energizing condition of the voltage angle with 0° and the residual flux density with approximately 70%.

An NPE-PCA-based Detection Method of Distribution Transformer Closing Surge Waveform

In order to effectively detect the waveform characteristics of the closing inrush current of distribution transformers and distinguish between the magnetizing inrush current and the fault current when closing, this paper proposes a new method based on neighborhood preserving embedding (NPE) and principal component analysis (PCA) transformer closing inrush current detection method. This method can detect and process the global and local feature information of the data. First, the NPE-PCA algorithm is used to reduce the current data to two-dimensional space, and then the fitting error σ is obtained by fitting the two-dimensional space data. The relationship between σ and a given threshold is used to identify the magnetizing inrush current when closing. Finally, a model is built on the ATP/EMTP platform to test the proposed method for detecting the waveform characteristics of the closing inrush current. The simulation results show that the NPE-PCA inrush current waveform detection algorithm proposed in this paper can effectively identify the waveform characteristics of the transformer closing inrush current, which is consistent with the second harmonic wave. Algorithm comparison analysis shows that the performance of this algorithm is better.

Research on Suppression of Fuses of Potential Transformer Fuse

In this paper, through the analysis of an electromagnetic Potential Transformer (PT) fuse fault in the distribution network, it is found that the cause of this type of fault is the low-frequency oscillation and inrush current of the voltage transformer caused by the disturbance, and its occurrence mechanism is studied and explained. After analyzing the advantages and disadvantages of the common PT fuse-blowing fault control measures, this paper proposes a treatment plan for the PT neutral point to suppress the fuse blowing through a nonlinear flow-sensitive harmonic elimination device. The harmonic device has a remarkable suppression effect on the PT fuse blown fault.

Rotating phasor-based algorithm for the identification of inrush currents of three-phase transformers

• We study low-second-harmonic current contents in three-phase transformers. • A rotating phasor-based algorithm is proposed for the second harmonic content. • Simulations and recorded data tests show that the algorithm improves the current identification accuracy. Identification of the inrush current is a prominent research issue pertaining to transformer protection. This study first analyzes the possible low-second-harmonic content of the differential current when a three-phase transformer with delta-connected windings is energized by wye windings. Further, we propose a rotating phasor-based algorithm for the second harmonic content. The algorithm first rotates the corresponding phasors of the fundamental wave and the second harmonic of one phase current, then recombines them with the fundamental wave and the second harmonic of another phase current to form a new second harmonic content calculation formula. Finally, several tests were conducted using PSCAD/EMTDC and recorded data of field transformer protection. The simulation and test results show that the algorithm can lead to considerable improvements in the accuracy of inrush current identification of the three-phase transformer. The proposed algorithm provides a new identification method for the per-phase blocking of differential protection.

Discrimination between Inrush and Internal Fault Currents in Power Transformers Using Hyperbolic S-Transform

Discrimination between Inrush and Internal Fault Currents in Power Transformers Using Hyperbolic S-Transform

Practical Rules to Adjust the Numerical Overcurrent Functions of Protective Relays to Avoid Maloperation due to Inrush Currents of Downstream Distribution Transformers

This article shows practical rules which were developed for the adjustment of Numerical Overcurrent Functions (NOCF) of microprocessor-based protective relays in order to avoid their maloperation due to Transformer Magnetizing Inrush Currents (TMIC). The TMIC waveforms are decaying and nonsinusoidal, and their effects are different on instantaneous, definite-time, or inverse-time NOCF; thus, specific rules are necessary for each type of NOCF. Experimental methods, specifically developed to perform tests of NOCF with TMIC, were applied to four commercial relays from different manufacturers. As each relay has different types of curves, a total of 26 inverse-time curves were included in the set of tests. In practice, the specific behavior of a relay cannot be assumed as known during the coordination of protection; therefore, the rules are based on the worst cases of results (i.e., in order to avoid the incorrect trip of any of the analyzed relays). The input data for the developed rules are the estimated cumulative RMS values of TMIC, assumed as known since there is a long tradition of their estimation. The estimated cumulative RMS values of TMIC, at different time intervals, should be divided by the numerical factors which were specifically proposed for each type of NOCF (to obtain suitable limits to avoid their inappropriate operation). In order to illustrate the application of the proposed rules, the solutions to two cases related to a simple example are shown at the end of this article.

A Statistical Approach to Discriminate the Magnetising Inrush Current and Internal Fault Current of a Transformer

Abstract: The transformer is one of the important elements of electrical power system. So, it is required to provide the good protection scheme for transformer, which enhance the reliability &amp; economy of the system. There are many methods available for the protection of transformer out of which differential protection is the most commonly used method. It is observed that the conventional differential protection scheme mal-operates during the magnetizing inrush current. The mal-operation of differential relay will affect the continuity of supply to customer and hence the economy of our system. To avoid such type of mal-operation of the relays, proper discrimination between the magnetising inrush current and fault current is required. This paper presents Teager Energy Operator (TEO) and Statistical parameters based novel approach for the discrimination between the magnetizing inrush current and the internal fault current of a transformer. In this paper, TEO of the differential current of the transformer is calculated and compared with its threshold value to detect the abnormal condition. When TEO is more than threshold, statistical parameters like variance &amp; standard deviation are calculated and if the calculated value is more than the threshold then it’s an internal fault condition and hence relay gives the trip signal. Else relay does not issue the trip signal. The suggested algorithm is tested using the experimental data. The results demonstrated that the suggested algorithms are capable of accurately differentiating between the transformer's internal fault current and inrush current.

An aspect of transformer inrush current

Paper analyzes a sparking phenomena that were observed on power transformers during their first few energizing. Sparks occurred on the flange between tank cover and tank side, in spite of the fact that a jumper (copper link) was used. The phenomenon was observed on transformers from different manufacturers, with various transformer ratings, three or five limb core. Paper proposes a simple electromagnetic model for analyzing the phenomena. Three-phase power transformer is modeled in finite element method (FEM) magnetic software. Simplified 3D transformer model is used in order to simulate well known core saturation during the inrush current event. Voltages between tank cover and tank side are calculated for simulated conditions. The same model is used for calculation of possible currents flowing between tank cover and tank side, showing that a value of several kA can be reached. In spite of the fact that the observed phenomenon is harmless mitigation measures are proposed. However, by doing nothing, sparking would disappear very soon.

Advanced Transformer Differential Protection Under GIC Conditions

This paper presents an advanced differential protection scheme for protecting transformers during geomagnetic disturbance (GMD) conditions and the flow of geomagnetically induced current (GIC) in the transformer. In this study, the inaccuracy of the existing harmonic blocking (HB) methods of the differential protection is investigated under the GIC conditions. It is shown that there are scenarios in which the HB-based differential relays fail to clear the internal faults since they are blocked due to the GIC-related harmonic currents. By detecting both GIC conditions and internal short-circuit faults, a solution scheme is proposed which overrides the harmonic inhibition signals issued by the HB module and makes the differential relay capable of tripping the transformer. Extensive time-domain simulations within the EMTP-RV verify the effectiveness of the proposed scheme in (i) unblocking the transformer differential protection for the internal faults during the GIC, (ii) preventing the malfunction for the external faults, (iii) distinguishing the transformer inrush and sympathetic inrush currents from the GIC conditions, and (iv) immunity to current transformer (CT) saturation. Furthermore, the simulation results prove that the differential protection equipped with the proposed approach outperforms the existing HB methods under the GIC conditions.