Magnetizing Inrush Current Research Articles

Preventing maloperation scheme for saturation protection of converter transformers based on waveform prediction of magnetizing inrush current

Preventing maloperation scheme for saturation protection of converter transformers based on waveform prediction of magnetizing inrush current

Wavelet scattering and multiclass support vector machine (WS_MSVM) for effective fault classification in transformers: a real-time experimental approach

The dependable operation of the protective system is affected by residual magnetism and magnetic bias during the activation of the transformer. Challenges arise in distinguishing between inrush current and fault current when the transformer circuit breaker switching angle and remnant flux attain specific values. This situation can lead to a lack of sensitivity or failure in the operation of the transformer protection system. Thus, this paper proposes an effective approach for classifying different faults in transformers (internal and non-internal faults) using wavelet scattering and multiclass support vector machine (MSVM) technique (WS_MSVM). The wavelet scattering method begins by transmitting the data through a sequence of wavelet transforms, nonlinear operations, and averaging processes. This series of operations aims to generate low-variance representations of time series data. Wavelet time scattering produces signal representations that remain unaffected by shifts in the input signal, while still preserving the ability to distinguish between internal and non-internal faults. Internal faults are those occurring within the zones of the current transformer (CT) connected on both sides of the transformer. On the other hand, non-internal faults consist of magnetizing inrush current, sympathetic inrush current, and external faults (faults occurring outside the CT zones). The efficacy of the proposed WS_SVM is evaluated on the real-time experimental results obtained from the differential path of a laboratory transformer. The experimental data generated over one power frequency cycle under diverse operating conditions is employed in MATLAB to assess the effectiveness of the proposed WS_SVM algorithm.

Analysis and application of suppression measures for protection misoperation caused by excitation inrush current of 15.75kV transformer

Abstract This article briefly described the mechanism of transformer magnetizing inrush current, and introduced the principle of differential protection magnetizing inrush current suppression of Siemens 7UT682 protection device. Through the analysis and research of a differential protection malfunction trip event when a no-load closing power of a overhauled transformer, the method of optimizing the relevant parameter configuration of the protection device to avoid the excitation inrush current was proposed and achieved good application results.

Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations

The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

Simulation of Magnetizing Inrush Current of Three-Phase Three-Legged Transformers by Direct Measurement of Coils Inductance

This paper presents a method for simulating magnetizing inrush current for three-phase three-legged transformers. A method is showed for easy and fast needed transformer parameters obtaining. It is shown that magnetizing inrush current is strongly dependent on transformer core saturation, and a straightforward method is presented for taking into account core saturation. Finally, a real transformer simulation results are presented and compared with those obtained by direct measure of magnetizing inrush current, discussing method suitability.

An Experimental Set-up Involving Low-cost Digital Controller to Study the Magnetizing Inrush Current in a Transformer using Point-on-Wave Switching Technique

Abstract Generally in under-graduate studies, magnetizing inrush current (MIC) is discussed theoretically without giving much practical exposure. This paper presents the development of a low cost experimental set-up using a digital controller to study the MIC and the different parameters which can affect the same. This also helps to show how the inrush current can be minimized. This set-up also provides a hands-on experience of MIC and its control in under-graduate study, which can help an upcoming practitioner in industry as well as in further research. This paper presents a brief description of MIC, followed by a short analysis. Here, a pair of anti-parallel thyristors are connected in series with the primary winding of a single-phase power transformer. The turningon instant of this switch, with respect to the zero-crossing instant of the input supply voltage, may be adjusted through a firmware, in a PIC18F4620 from Microchip Technology microcontroller development board from Microchip Technology to control the transformer energisation instant. The firmware is developed in MPLABX-IDE from Microchip Technology, and the scheme is verified via simulations in Proteus simulation software. A suitable circuit to support the microcontroller development board to achieve the above function is designed and fabricated.

Varying the energisation condition to mitigate sympathetic inrush current

Transformers are generally easy to access and can contribute significantly to entire power system. When a transformer is turned on for the first time, it produces a magnetising inrush current which acts as a starting current. Energisation of transformer has a substantial impact on inrush current and transformer that are connected in parallel. Sympathetic inrush current is a phenomenon that appears when a transformer is switched-on in network whereas the other transformers that was earlier energised. Besides, when sympathetic inrush phenomena occur, the peak and period fluctuate significantly. In this paper, the transformers will be energised in three different ways and each condition will be explored in depth. The operation time of the transformer’s energisation whether it is energised simultaneously or at different times are tested and analysed in terms of their characteristics. It is performed using power system computer aided design (PSCAD) software, starting with a develop model of the energisation and then generate the outcomes. The results of the simulation demonstrate that energising the transformer in different ways can give different effect on the sympathetic inrush current, as well as the variables that affect it and methods for reducing it.

Typical Fault Characteristics Analysis of Distribution Network

China has built the largest power network in the world, forming a pattern of “nationwide interconnection, sending power from west to east, mutual supply between south and north.” As the user-oriented terminal power network, the safe operation of distribution network is the basis of power system stability. However, the distribution network system is huge, widely distributed and prone to failure. In order to quickly and accurately judge the fault category, improve the efficiency of fault location and reduce the non-fault outage area after the fault occurs in the distribution network system. Four typical faults of distribution network, namely single-phase grounding fault, phase-to-phase short circuit fault, ferromagnetic resonance and magnetizing inrush current, are studied in this paper, their fault characteristics are analyzed, and fault simulation analysis is carried out by MATLAB. Finally, the theoretical analysis results are verified by the actual on-site fault recording data, which proves the scientific and feasibility of the theoretical analysis.

A Soft-Start-Based Method for Active Suppression of Magnetizing Inrush Current in Transformers

The occurrence of high-amplitude magnetizing inrush current during the energization of a transformer without load poses significant challenges to the stable operation of both the transformer and the power grid, potentially leading to malfunctions in relay protection devices. This paper analyzes the underlying mechanisms of transformer inrush current and presents a novel approach utilizing a soft-start-based method for effectively suppressing inrush currents. The proposed method employs an inrush current suppressor comprising anti-parallel thyristors and filters to mitigate the adverse effects caused by the inrush current. A comparative study is conducted to evaluate the filtering efficacy of three types of filters incorporated in the inrush current suppressor: an LCLC damping filter, a high-order single trap filter and a high-order double trap filter. Through careful analysis and optimization, the high-order double trap filter with parallel RC damping damper is selected as the optimal configuration. To ensure effective suppression, it is necessary to incorporate a filter at the termination point of the anti-parallel thyristor. Additionally, a closed-loop control strategy is implemented to ensure a smooth start and actively suppress the magnetizing inrush current. To validate the effectiveness of the proposed method, comprehensive simulations are performed using Matlab/Simulink. The results demonstrate the successful suppression of inrush current and the maintenance of stable operation for the transformer. This inrush suppression method does not require considering the influence of residual magnetism and the grounding mode of the transformer’s neutral point. It is also suitable for various transformer structures and wiring methods, which makes it highly applicable.

Research on Intelligent Identification of Magnetizing Inrush Current based on Empirical Modal Decomposition

The accurate identification of the converter transformer magnetizing inrush current can ensure the safety of the operation of the UHV DC transmission system. In the differential protection of the converter transformer, it is extremely hard to distinguish between the excitation inrush current generated when the transformer is closed and the Fault current due to internal fault, which leads to frequent misoperation of the transformer protection to the extent that the transformer access fails. This paper proposes a current identification model based on empirical modal decomposition (EMD) and probabilistic neural network (PNN) algorithm with particle swarm (PSO) optimization. The EMD algorithm decomposes the current waveform, and the eigenmode functions (IMFs) of the three highest correlation bars are selected. The energy, cliffs, and standard deviation of the IMFs are extracted as the original waveform eigenvectors.

Magnetizing Inrush Current Elimination Strategy Based on a Parallel Type Asynchronous Closing Hybrid Transformer

When the power transformer is connected to power grids, due to the existing remanent magnetism (RM) and the voltage transient component, the transformer will generate a large magnetizing inrush current (MIC). The MIC not only seriously threatens the safe operation of the transformer, but also causes the misoperation in the differential protections. To solve the aforementioned problems, a novel MIC elimination strategy of the hybrid transformer (HT) based on an asynchronous closing technology is proposed in this article. First, through the rational design of the HT topology, the parallel auxiliary winding (PAW) of the HT is established and the magnetic coupling mechanism is realized, which lay the magnetic circuit foundation for the MIC management. Second, a step-type synchronous magnetic field is established through PAW without considering RM. After the action of step-type magnetic field, the core flux increases sinusoidal linearly and enters into the rated steady stable. Finally, by controlling the stabilization time of the oblique wave and the primary voltage signal of the power grid, when the core flux is stabilized, the nonsynchronous closing of the HT can effectively alleviate the generation of the MIC. The validity and feasibility of the proposed scheme are verified by building an HT prototype platform, which provides some reference for the difficulty of measuring RM and avoiding MIC in the power system.

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer’s internal fault leads to high breathing process in the transformer breather, never for inrush currents. During inrush currents, CT temperature is increased. Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pixels detect the causes of CT saturation and differentiates maloperation. Hybrid wavelet threshold image analytics (HWT-IA) based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95% for identification of the false tripping of differential protection system of transformer.

A new protection method for the power transformers using Teager energy operator and a fluctuation identifier index

• Using a high-frequency based method, a very fast protection algorithm is provided. • Teager energy operator is a very fast and simple method for the extraction of high-frequency components of the signal. • A very simple index is proposed here which makes the internal fault identification really fast. • A very simple and fast method is provided for the fault type classification. • The algorithm is successfully tested by the experimental signals. Transformers are one of the most important tools of a power system. Differential protection relays with the second harmonic restraint criterion are traditionally used for the transformer protection. This conventional criterion may fail to perform properly in some cases which results in the incorrect relay operation. A novel high-frequency-based algorithm is proposed in this paper to discriminate between the magnetizing inrush currents and the internal faults. Teager energy operator (TEO) is used here to extract high-frequency information from the differential current signals. Differential currents resulted from an internal fault have more high-frequency transients than those resulted from a magnetizing inrush and this is the basic principle used in the proposed algorithm. A very simple and effective operator is proposed in this paper to quantify the overall high-frequency transients of a signal. The proposed algorithm uses a quarter of a cycle of the differential currents along with the very simple mathematical operators to do its task. Therefore, this is a very fast and simple algorithm. The algorithm is tested by the simulated and experimental test signals. Results show the robustness and reliability of the proposed algorithm.

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 & 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 & 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.

Adaptive Restraint Coefficient-Based Differential Protection for Main Transformers in Wind Farms

Influenced by factors such as fault current frequency deviation, current transformer (CT) saturation and abnormal data, the traditional transformer current differential protection scheme contains the risks of slow operating speed or false operation in the wind power system. To guarantee the safe operation of the power system, by improving the traditional current differential protection scheme, a differential protection scheme based on the adaptive restrain coefficient was proposed for the main transformers of wind farms. The proposed protection adaptively adjusts the restraint coefficient according to the time-domain distribution characteristics of differential current. When the internal fault occurs, the restraint coefficient will be adaptively reduced; the restraint coefficient will adaptively increase when the non-internal fault occurs. The proposed protection scheme significantly surpasses the traditional current different protection in terms of operating performance, because it can accurately identify the internal faults and strongly resist abnormal data, CT saturation and magnetizing inrush current.

A new high-frequency-based method for the very fast differential protection of power transformers

A novel algorithm based on Teager energy operator (TEO) and hidden Markov model (HMM) is presented for the transformer differential protection. After detecting any increase in the amplitude of the transformer differential currents, it is needed to know if the over-current is because of an internal fault or an inrush phenomenon. In this paper, a combination of TEO and HMM is used to discriminate between the magnetizing inrush currents and the internal fault currents. TEO is used to extract high frequency components of the signal and present them as a curve, named as Teager output curve (TOC). Then TOC is used as the input for HMM. First, the HMM training process is implemented by an adequate number of the train signals and then, the unknown signals are identified by the testing process. Fault type identification is also performed by the use of TOC. TOC has a separated region for each of the three phases. TEO of the zero-sequence signal has a peak for the grounded and no peak for the un-grounded faults. Train and test signals are generated by the simulated system in PSCAD/EMTDC. Results by means of the simulated and experimental data show the robustness of the proposed algorithm.

Research on magnetizing inrush current of microgrid based on Grey identification

With the large-scale access of wind and light energy, the power system is gradually developing towards the micro grid with low inertia. AC microgrid often uses multiple parallel transformers to supply power to the load at the same time. In case of power failure due to a fault or maintenance, the parallel transformer needs to be powered on and closed without load to restore power supply. The parallel transformer is easier to saturate, the core remanence is higher, and the peak value of inrush current is larger, which has an impact on the stability of microgrid. It will also lead to misoperation of transformer differential protection and affect the safe and stable operation of power system. Therefore, a switching method of parallel transformer based on Grey recognition is proposed. Firstly, the generation mechanism of inrush current is analyzed theoretically, and then the waveform shape characteristics are analyzed. The grey whitening weight clustering method is used to identify the feature set to complete the magnetizing inrush current locking and ensure that the protection does not misoperate. Finally, the PSCAD simulation model is established to simulate the AC microgrid to verify the feasibility of this method.

Design of Hardware Setup Based on IEC 61850 Communication Protocol for Detection & Blocking of Harmonics in Power Transformer

In this paper, the authors have developed a hardware model for blocking even and odd harmonics of a power transformer. In the proposed hardware model, intelligent differential & over-current relays are used for the blocking of harmonics of a power transformer. The harmonic restraint function on the differential relay (7UT61) prevents the relay from tripping during transformer magnetizing inrush current. However, the over-current relays which are used for back up protection does not have a harmonic restraint element, and over-current relay trips due to magnetizing inrush current, causes unwanted interruptions and power failures. The establishment of harmonic blocking scheme for over-current protection in power transformers is an important finding of this research study. IEC61850 standard-Based GOOSE (Generic Object Oriented Substation Event) applications used in power transformer protection for fast detecting and clearing of faults is an interested study presented in the proposed work.

Experimental Investigations of Magnetizing Inrush Current in Power Transformer Using a Proposed Synchronous Switch.(Dept.E)

This paper introduces deep and carful theoretical and experimental investigations of the magnetizing inrush current (M.I.C.) phenomenon in single phase power transformers, All salient parameters influencing M.I.C. waveform and particushed by the control of the instant of switching the supply to the transformer, the factor which appreciably affects M.I.C.