Transformer Protection Scheme Research Articles

Power Differential Protection for Transformer Based on Fault Component Network

Current differential protection has been widely used as the primary protection of transformers. However, inrush currents during transformer energization can cause misoperation of the protection when the second harmonic restraint algorithm fails. This paper proposes a power differential protection scheme based on the fault component network (FCN), which is free from inrush detection and computationally cheap. First, the fault component differential power (FCDP), defined as the differential active power of a transformer in the FCN, is analyzed under different conditions. Second, a transformer protection scheme is presented based on the FCDP and the traditional differential power. A removal algorithm of decaying direct current (DDC) offset is developed to accurately estimate the FCDP. The performance of the scheme is tested under various conditions, including internal faults, external faults and transformer energization. The simulations using PSCAD, and Real Time Digital Simulator (RTDS), experiments in the Electrical Power Dynamic Laboratory (EPDL) and real recording data validate that the proposed method can protect the transformer reliably.

A denoising-classification neural network for power transformer protection

Artificial intelligence (AI) can potentially improve the reliability of transformer protection by fusing multiple features. However, owing to the data scarcity of inrush current and internal fault, the existing methods face the problem of poor generalizability. In this paper, a denoising-classification neural network (DCNN) is proposed, one which integrates a convolutional auto-encoder (CAE) and a convolutional neural network (CNN), and is used to develop a reliable transformer protection scheme by identifying the exciting voltage-differential current curve (VICur). In the DCNN, CAE shares its encoder part with the CNN, where the CNN combines the encoder and a classifier. Based on the interaction of the CAE reconstruction process and the CNN classification process, the CAE regards the saturated features of the VICur as noise and removes them accurately. Consequently, it guides CNN to focus on the unsaturated features of the VICur. The unsaturated part of the VICur approximates an ellipse, and this significantly differentiates between a healthy and faulty transformer. Therefore, the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability. Finally, a CNN which is trained well by the DCNN is used to develop a protection scheme. PSCAD simulations and dynamic model experiments verify its superior performance.

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.

Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers

Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.

Discrimination of Transformer Inrush Currents and Internal Fault Currents Using Extended Kalman Filter Algorithm (EKF)

The discrimination of inrush currents and internal fault currents in transformers is an important feature of a transformer protection scheme. The harmonic current restrained feature is used in conventional differential relay protection of transformers. A literature survey shows that the discrimination between the inrush currents and internal fault currents is still an area that is open to research. In this paper, the classification of internal fault currents and magnetic inrush currents in the transformer is performed by using an extended Kalman filter (EKF) algorithm. When a transformer is energized under normal conditions, the EKF estimates the primary side winding current and, hence, the absolute residual signal (ARS) value is zero. The ARS value will not be equal to zero for internal fault and inrush phenomena conditions; hence, the EKF algorithm will be used for discriminating the internal faults and inrush faults by keeping the threshold level to the ARS value. The simulation results are compared with the theoretical analysis under various conditions. It is also observed that the detection time of internal faults decreases with the severity of the fault. The results of various test cases using the EKF algorithm are presented. This scheme provides fast protection of the transformer for severe faults.

Phase Shifting Transformer Electromagnetic Model Dedicated for Power System Protection Testing in a Transient Condition

Complex phase shifting transformer protection scheme and complexity of the object itself created a need to use simulation programs for their analysis. Often phase shifting transformer (PST) are modeled as a simplified series impedance and quadrature voltage source which cannot be used for power system protection analysis, especially in a transient condition. Therefore, the procedure of building realistic PST model was presented by using available transformer models with calculation of their parameters including interconnections between units. Paper consist calculations based on case study with symmetrical dual-core PST example. Additionally, theoretical background of PST principle, operation, and their impact of power system protection were introduced with numerus examples of PST model verification.

Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions

To increase the classification accuracy of a protection scheme for power transformer, an effective convolution neural network (CNN) extreme gradient boosting (XGBoost) combination is proposed in this work. Data generated from various test cases are fed to one-dimensional CNN for high-level feature extraction. After that, an efficient classifier tool XGBoost is used to properly discriminate different transformer internal faults against outside abnormalities. A portion of an Indian power system is considered and simulated in PSCAD software using the multi-run feature to collect a large number of data for various fault/abnormal situations. The generated data are used in MATLAB software where the proposed algorithm is programmed. A high-performance CPU is used for training and testing purpose of the projected artificial intelligent technique. The obtained results for classification accuracy as well as discrimination time shows that the proposed scheme is competent enough to properly discriminate transformer operational conditions. Further, the combined CNN-XGBoost technique is compared with existing relevance vector machine and hierarchical ensemble of extreme learning machine classifier techniques. Moreover, a hardware experiment is performed in a laboratory prototype of 50 kVA, 440/220 V transformer to verify the authenticity of the developed protective scheme. After analyzing a variety of experiments, the authors note that the presented method provides promising classification accuracy within a short time period.

Development of an adaptive differential protection scheme for transformer during current transformer saturation and over‐fluxing condition

Development of an adaptive differential protection scheme for transformer during <scp>current transformer</scp> saturation and over‐fluxing condition

2_TD$DIFF]Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer

• Total Harmonic Distortion (THD) based inrush and fault identification scheme for power transformer is proposed in this work. • Extensive PSCAD simulation is carried out with FFT analysis to estimate the THD during different operating condition of transformer. • Suitable THD limit is proposed for normal, inrush and fault condition of power transformer. • Validation of the suggested scheme has been carried out by software algorithm and displayed results shows satisfactory performance. • Hardware prototype has been developed and various inrush and fault cases are performed on the transformer to validate the proposed technique. The non linear characteristic of transformer magnetizing core leads more current/voltage signal distortion in the event of system disturbance/fault. The harmonic penetration in the current signal depends of the transformer core, characteristic of load and abnormal conditions. Different situations or abnormalities have different harmonics content and consequently different waveform signature. In this article, authors have utilized the harmonics content to identify normal, inrush condition and fault condition in power transformer. Total Harmonic Distortion (THD) of the current waveform for one cycle, is measured by performing Fast Fourier Transformation (FFT) analysis. Authors observed that the percentage THD is generally high in the case of inrush condition due to initial flux setup in magnetic core. Conversely, during fault condition, the %THD will be low (within a particular range) due to symmetric waveform. The magnitude of %THD below predefined range will be considered as normal condition and excess THD will be considered as inrush condition. On the other hand, if THD remains within a particular range then, it is considered as fault condition. In this article, authenticity of the presented scheme is checked by performing various test cases like initial inrush, internal fault, switching of considered transformer in faulty condition and CT saturation during fault condition. The hardware validation proves the competency of the proposed THD based discrimination scheme for transformer protection. The scheme presented here is simple and capable to solve complex problem of classification of inrush condition and fault condition.

Design of transformer protection scheme for DC system

Back-to-back flexible DC system can be used for interconnection of asynchronous power grids. In view of the different characteristics and operation modes of connected transformer equipment in back-to-back flexible DC system, this paper puts forward some special problems that need to be considered in the design of connected transformer protection, and puts forward a complete configuration scheme of connected transformer protection in view of its particularity. Finally, we establishes a real-time digital simulation model to verify the validity of the model using actual engineering parameters. The simulation results show that the theoretical analysis is correct and the proposed protection scheme can effectively guide the engineering application.

Study on the transformer protection scheme based on double thresholds and waveform similarity

As the key device of power transmission, the power transformer has higher requirements for the protection sensitivity and reliability. However, the operation time of the differential protection is limit to the data window of the Fourier transform algorithm. In addition, the differential protection cannot recognize the slight transformer turn-to-turn fault. Moreover, it may maloperate when the current transformer (CT) is saturated. To solve above problems, a novel transformer protection scheme based on double thresholds and waveform similarity is proposed. By comparing the current waveform similarity with the double thresholds, the external faults, internal faults and CT saturation scenarios can be effectively identified, while the waveform similarity is obtained by calculating the Hausdorff distance of the current at each side of the transformer. The PSCAD/EMTDC based simulation results show that the proposed protection scheme can operate quickly and the operation time is as low as 5.5ms. In addition, it has high sensitivity and can effectively identify 0.5% or higher turn-to-turn faults, and it will not be affected by CT saturation.

Adaptive ANN based differential protective relay for reliable power transformer protection operation during energisation

Power transformer is the most expensive equipment in electrical power system that needs continuous monitoring and fast protection response. Differential relay is usually used in power transformer protection scheme. This protection compares the difference of currents between transformer primary and secondary sides, with which a tripping signal to the circuit breaker is asserted. However, when power transformers are energized, the magnetizing inrush current is present and due to its high magnitude, the relay mal-operates. To prevent mal-operation, methods revolving around the fact that the relay should be able to discriminate between the magnetizing inrush current and the fault current must be studied. This paper presents an Artificial Neural Network(ANN) based differential relay that is designed to enable the differential relay to correct its mal-operation during energization by training the ANN and testing it with harmonic current as the restraining element. The MATLAB software is used to implement and evaluate the proposed differential relay. It is shown that the ANN based differential relay is indeed an adaptive relay when it is appropriately trained using the Network Fitting Tool. The improved differential relay models also include a reset part which enables automatic reset of the relays. Using the techniques of 2nd harmonic restraint and ANN to design a differential relay thus illustrates that the latter can successfully differentiate between magnetizing inrush and internal fault currents. With the new adaptive ANN-based differential relay, there is no mal-operation of the relay during energization. The ANN based differential relay shows better performance in terms of its ability to differentiate fault against energization current. Amazingly, the response time, when there is an internal fault, is 1 ms compared to 4.5 ms of the conventional 2nd harmonic restraint based relay.

New protection scheme for power transformer based on superimposed differential current

This study presents a new power transformer protection scheme based on extraction of positive and negative sequence components of superimposed differential currents. Different types of internal faults, magnetising inrush, and over-excitation conditions have been generated by modelling in service power transformer of power transmission system of India using PSCAD/EMTDC software package. The simulation results clearly indicate the efficacy of the proposed scheme in detecting different types of internal faults like turn-to-turn, inter-winding, line-to-ground, line-to-line, and line-to-line-to-ground. Together, the proposed scheme is competent to achieve effective discrimination between various external events (over-excitation condition and various magnetising inrushes) and internal faults. Moreover, it provides equally promising results during current transformer (CT) saturation condition as well as for different connection and rating of the power transformer. Further, it remains stable during errors in the CT and tap change condition. Additionally, authenticity of the proposed scheme has been evaluated using internal fault and magnetising inrush data recorded in the real field. At the end, comparative assessment of the proposed scheme with the existing and conventional techniques clearly indicate its superiority in terms of better sensitivity for internal faults and higher stability during external events.

Power transformer differential protection using current and voltage ratios

The main challenge in transformer protection is to find a fast and efficient differential relay algorithm that isolates the transformer from the system causing least damage. Algorithm should also avoid mal-operation while differentiating between the operating conditions. This paper presents an improved differential protection scheme for power transformer. The proposed scheme is based on the ratio of the absolute difference and absolute sum of the primary and secondary currents of each phase, supplemented by the ratio of the absolute difference and absolute sum of the primary and secondary terminal voltages of each phase. The proposed algorithm aims at avoiding mal-operation, possible with the conventional three-phase transformers differential protection scheme due to transient phenomena, including the magnetic inrush current, simultaneous inrush with internal fault, and faults with current transformer saturation. Investigation of the proposed differential protection scheme using both current and voltage ratios shows that it can provide fast, accurate, secure and dependable relay for power transformers.

Ann Based Fault Protection Scheme for Power Transformer

3 Abstract: This paper introduced fault diagnosis system for power transformer using an Artificial Neural Network (ANN). This system is to identify, localize and classify the faults. This system consists of various stages. In first stage, a preprocessing procedure for input data is performed. In second stage an ANN is designed to identify fault and to localize its side. In third stage, there are two sub diagnosis systems. Each system has one ANN designed to classify the fault. The performance of fault diagnosis system is evaluated using samples from local measurements (three-phase primary voltage and primary& secondary currents).The faults that are detected and cleared are single winding, double winding, and inrush currents.

Protection of power transformer using multi criteria decision-making

Abstract Power transformers are protected by different relays that operate independently. Malfunction of each relay has a major role in reducing the reliability of the protection system. In order to mitigate the main drawbacks of the power transformer relays, an overall protection scheme is presented in this paper. This scheme proposes a novel multi criterion algorithm using decision-making based on fuzzy logic. In this paper the outputs of restricted earth fault relay and a directional check unit, are combined with the output of the differential protection relay. Therefore, problems that are pertaining to independent operation of each relay have been mitigated and the relays cover protection blind spots of each other. The improved power transformer protection (IPTP) scheme enhances the sensitivity and reliability of the power transformer protection. Extensive simulations are used to measure the effectiveness and merit of the proposed IPTP relay. The above efforts result in a multi criteria approach for protection of power transformers.

A suggested differential protection scheme for power transformer

This paper integrates a Real Power Differential Scheme (RPDS) for power transformer protection. The suggested RPDS for power transformer computes the active power loci during normal operation, switching, normal, and internal, involves turn to turn, and external faults at varied load angles. The proposed RPDS concept is based on monitoring and comparing the transformers primary and secondary active and reactive powers. The dynamic response of the proposed RPDS is tested 300 MVA, 220/66 kV, Y/Δ transformer. Furthermore, the suggested scheme is simulated with the use of Matlab/Simulink then tested for various fault and switching conditions. Moreover, the RPDS is checked for inter turn fault conditions at primary and secondary sides. The evaluation of the suggested scheme confirms the superiority of the proposed scheme to distinguish internal and external faults as well as magnetizing inrush currents with good selectivity, high speed, sensitivity, stability limits and high accuracy response of the power differential scheme. Finally, the suggested scheme is able to detect correctly the turn to turn faults for wide range of fault resistances but fails at very low values.

Power transformer protection scheme based on MRA-SSVM

In the literature of power transformer protection, the essential issue consists of discriminating between transformer inrush current and internal fault current. This paper presents a novel scheme for the protection of power transformers utilizing Multi Resolution Analysis (MRA) and Smooth Supported Vector Machine (SSVM). Some important characteristics of differential currents are extracted based on MRA. The combination of MRA and SSVM is proposed as an effective and fast technique for discrimination of the faulted and unfaulted conditions. The Particle Swarm Optimization (PSO) is used to obtain optimal values of adjustable parameters of the applied classifier. The efficacy of the proposed scheme has been tested using numerous inrush and internal fault currents. The results confirm that the combined MRA-SSVM scheme yields high detection accuracy even in the noisy conditions.

A novel probabilistic neural network‐based algorithm for classifying internal fault in transformer windings

AbstractThe major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients‐based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two‐winding three‐phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high‐frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first‐scale high‐frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

An efficient power differential scheme for power transformer protection

An efficient power differential scheme for power transformer protection