Power Transformer Protection Scheme Research Articles

Time–Frequency Decomposition Based Protection Scheme for Power Transformer— Simulation and Experimental Validation

Time–Frequency Decomposition Based Protection Scheme for Power Transformer— Simulation and Experimental Validation

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.

Wavelet-Based Differential Protection Scheme for Power Transformers

Wavelet-Based Differential Protection Scheme for Power Transformers

Duty Cycle-Based Differential Protection Scheme for Power Transformers

In general, a power transformer is equipped with differential protection relay with the second harmonic restraint criterion to improve reliability. However, this criterion may fail in some cases, resulting in incorrect operation of protection relay. Therefore, to further upgrade the performance of differential protection, this paper proposes a duty cycle-based scheme as complement. Firstly, the waveform of the differential current in one cycle and its two derived waveforms, constructed by translating and taking absolute, are transformed into corresponding square waves, respectively. Then, duty cycles of these square waves are calculated, and three criteria are presented to quantify the differences between the duty cycles of disturbances and faults. Finally, the criteria and the second harmonic restraint are combined through three logic gates to form the proposed scheme for protection. A large number of simulation experiments have verified the effectiveness of the proposed scheme, which outperforms the frequently used protection schemes.

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.

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.

Differential protection scheme for power transformers using matched wavelets

A new approach for differential protection based on the mother wavelets which match in shape with inrush and fault current waveforms is proposed in this work. It is demonstrated that if the matched wavelet is used for processing inrush and fault waveforms of transformer then its detection capability improves. In case of false tripping of transformer, the equipment is to be opened for inspection for faults. This leads to equipment outage because there is no mechanism to ascertain that there was no fault. It is needed that inrush and fault be detected independently not only for the purpose of faster and better detection but also for studying mal-operations and post-analysis. For a faster and better detection, a new protection scheme is also proposed making use of independent detection of fault and inrush waveforms. To find matched inrush and fault wavelets, differential evolution algorithm is used. Out of various possible solutions for different number of wavelet coefficients, a choice is made by further testing on various switching angles. The effectiveness of the proposed method is tested through PSCAD simulations on transformers of different ratings.

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.

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.

Power transformer protection scheme based on time-frequency analysis

SUMMARY This article presents a new approach for power transformer protection based on a hybrid pattern recognition scheme. The hyperbolic S-transform (HST) is a very powerful tool for analysis of the nonstationary waveforms because it is able to extract the information from transient signals simultaneously in both time and frequency domains. Magnitude, frequency, and HST contours are the main attributes obtained from the output matrix of HST. At first, differential current waveforms of different conditions such as normal, internal and external faults, inrush, and over-excitation conditions are analyzed by HST and some potential useful features are extracted from the abovementioned contours. To decrease the dimension of feature vector and to increase the classification accuracy of the proposed algorithm, the most effective features are selected by using well-known feature selection methods namely sequential forward selection, sequential backward selection, and genetic algorithm. Selected features are trained by a probabilistic neural network as an effective classifier core, which has advantages regarding learning speed and generalization capability compared with feed-forward neural network. The classification accuracy of the proposed algorithm has been used as a criterion function for the selection of the best subset features. The proposed protection scheme is evaluated for various operating conditions of three different transformers using the PSCAD/EMTDC package. Extensive simulation results show that the proposed algorithm relies only on the waveshape properties, and it is independent of the value of transformer parameters and consumed power. Copyright © 2012 John Wiley & Sons, Ltd.

PSO trained ANN-based differential protection scheme for power transformers

In this paper, a novel scheme is proposed which monitors and discriminates the different operating conditions (normal, magnetizing inrush current, over excitation of the core, internal faults and CT saturation due to external faults) of power transformers. The Particle Swarm Optimization (PSO) technique is used to train the multi-layered feed forward neural networks to discriminate the different operating conditions. The proposed scheme has been realized through two different Artificial Neural Network (ANN) architectures. One is used as an internal fault detector and the other one detects and discriminates the other operating conditions like normal, inrush, over excitation, and CT saturation due to external faults. These two ANNs were trained using Back Propagation Neural Network Algorithm (BPN) and PSO technique and the simulated results are compared. Simulations are performed for the practical power transformer ratings obtained from Tamilnadu Electricity Board (TNEB), India. Comparing the simulated results of the above two cases, training the neural network by PSO technique gives more accurate (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) results than BPN. The PSO trained ANN-based differential protection scheme provides faster, accurate, more secured and dependable results for power transformers.

ANN-Based Protection Scheme for Power Transformer

The implementation of a pattern recognizer for power system diagnosis can provide great advancement in the protection field. The work reported in this paper demonstrates the use of an Artificial Neural Network (ANN) as a pattern classifier for differential relay operation in the protection scheme for power transformer protection. The proposed relay takes care of maloperation due to inrush current, overexcitation, and ensures operation during internal fault. The off-line experimental result presented in this paper shows that a FeedForward Neural Network (FFNN) can be considered as an attractive alternative method to make the discrimination among normal, magnetizing inrush, overexcitation, and internal fault currents in a digital relay implementation. Results showing the performance of the protection scheme indicate that it is fast and reliable.