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The algorithms of the present generation of practical differential relays are based on two methods. The first method is based on the ratio of the harmonic content of the differential current, and the second one is based on the length of the time interval between the zero‐crossing points of the differential current, called the gap‐detection method. However, these methods suffer from the installation of the fault current limiters (FCLs) in the power system and current transformer (CT) saturation phenomenon. This paper deals with a simple practical method for differential relays to secure their performance. In the suggested method, a classifying algorithm is used to categorize the input signal of the relay and then the best method of the present generation of differential relays is employed. By this simple decision, the method takes the advantage of harmonic‐based and gap‐detection methods, while avoiding their drawbacks. To prove the robustness of the suggested method, a test bench with a resistive solid‐state fault current limiter (SSFCL) is implemented and examined in different situations. The results validate the consistency and the accuracy of the modified technique not only in the absence and the presence of the FCL but in the case of CT saturation. [ABSTRACT FROM AUTHOR]

This paper deals with the utilization of printed circuit board Rogowski coils (PCBRCs) for differential protection of electric arc furnace (EAF) transformers in Mobarakeh Steel Company (MSC), Isfahan, Iran. Because of a high level of current (in the range of 100 kA) in the EAF transformers in the MSC, employment of differential protection is quite challenging. Conventional current transformers (CTs) cannot offer reliable performance for very high current levels. The PCBRCs are a branch of Rogowski coils (RCs) that can be manufactured with high precision using computer‐aided design approaches and have the capability for the measurement of very large currents. In this study, we have designed and manufactured two sets of PCBRCs to measure current in the primary and secondary sides of the EAF's transformers in the MSC. The PCBRCs are followed by a signal condition circuit (SCC) which includes a low‐pass filter, amplifier, and voltage‐to‐current (V‐to‐I) converter block. Thanks to the SCC, the high current of the transformers can be properly sensed and transmitted over a long cable that connects the PCBRCs to the relay. Furthermore, a novel model is suggested for the EAF that includes characteristics of both time and frequency domain models. The suggested model is developed using a large number of recorded data from the MSC and can model the non‐linear and time‐variant behaviours of the EAF. The proposed PCBRCs, SCC, and EAF modelling are evaluated by some simulations and experiments in the MSC. [ABSTRACT FROM AUTHOR]

Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs an additional block to distinguish between internal faults and external transient disturbances. The protection system should, first, be able to perform based on raw data, second, be able to learn fully temporal features and sudden changes in the transient signals, and, third, impose no assumption on noise. To address these challenges, a fast RNN, namely fast gated recurrent neural network (FGRNN). By removing reset gate in the gated recurrent unit (GRU), the proposed network is capable of learning abrupt changes in addition to significantly reducing the computational time. Furthermore, a loss function based on an information theory concept is formulated in this article to enhance the learning ability as well as robustness against non-Gaussian/Gaussian noises. A generalized form of mutual information is also adopted to form a noise model-free loss function, then incorporated with the designed deep network. Simulated and experimental examinations engaging various external factors, in addition to comparison between the proposed FGRNN, GRU, and seven firmly established methods indicates the faster and more reliable performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article puts forward a new algorithm based on Kullback–Leibler divergence (KLD) for discriminating inrush and internal fault currents in power transformers. Specifically, the main idea of this algorithm is to utilize the current signal discrepancy of the distribution with ideal fault current waveforms. To such an aim, the proposed index reproduces the differential current signal. Utilizing fast modified least squares technique, the differential current signal is generated. Applying the reconstructed differential current and ideal sinusoidal waveforms to the KLD indicator, the distribution discrepancy of the current signal from ideal sinusoidal waveform discriminates inrush and internal fault currents. Also, the internal/external identification index is introduced that utilizes the phase content of the signals from current transformers (CTs) on both sides to distinguish internal and external faults, especially the faults accompanied with CT saturation. As a result, the proposed method can distinguish inrush currents, internal and external fault currents with/without CT saturation, and internal fault currents during transformer energization. The performance of the proposed index is evaluated using practically recorded data and is further compared with the state of the art. [ABSTRACT FROM AUTHOR]

Abstract—This article presents an efficient method for discriminating magnetizing inrush and internal fault currents in three-phase transformers based on the auto-correlation function. For each phase, the auto-correlation function of the differential current is calculated for a specified range of lags. Their standard deviation is determined in the next step. Criterion function is simply defined as the difference between the maximum and minimum of the auto-correlation function lags standard deviations for the three phases. Performance of the proposed method is evaluated and compared with eight well-known methods by a large number of simulation and experimental data, covering different cases. The results confirm that the proposed technique is more efficient than the other methods from speed response, reliability, and accuracy points of view. To eliminate the current transformer saturation impact on the performance of the method, the currents are compensated by a current transformer saturation correction algorithm. Also, the proposed algorithm is evaluated in the case of a transformer that is fed through a feeder with series capacitors. [ABSTRACT FROM PUBLISHER]

• Proposing discrimination index based on fundamental phase angle of the current signal. • The proposed index's threshold does not require transformer parameters. • Classifying internal/external faults and inrush currents in sub-cycle considering CT saturation. • Enhancing reliability and response time in the various fault/inrush conditions. Power transformer differential protection may confront mal-functioning in authentic discrimination between inrush and internal faults. To tackle the latter mal-functioning, a new two-stages algorithm based on phase content of the current signal of the current transformers (CTs) is put forward. The proposed algorithm is designed based on the fact that the fundamental phase angle of a fault signal ideally remains constant during the fault. However, during inrush cases, the phase angle varies. Also, during the internal fault, the phase angles of the current signals of CTs are in phase while during the external fault, the phase angles of the current signals of CTs are 180° out of phase. In the first stage, the proposed algorithm calculates the fundamental phase angles of the current signals of the CTs using sub-cycle modified recursive least squares (MRLS). Afterward, normalized mean residue (NMR) is employed to measure distance between the estimated phase angles of the CT's currents. MRLS and NMR algorithms require limited samples (i.e. 10 and 5 samples respectively) for executing their calculations. Performance evaluation with simulated and experimental recorded current signals shows the ability of the proposed method in discrimination of the internal faults from inrush and external fault currents. [ABSTRACT FROM AUTHOR]

• Proposing discrimination index based on the rate of phase angle change of current signal. • The proposed discrimination index does not depend on transformer parameters. • Classifying fault and inrush currents in sub-cycle considering CT saturation. • Enhancing reliability and response time in the various fault/inrush conditions. Magnetizing inrush currents are known as the most critical potential threat in maloperation of the transformer differential protection. Therefore, there is a need to detect the inrush current from the internal fault current to avoid the differential protection maloperation. Due to the possibility of a current transformer (CT) saturation at a high level of inrush and fault current, the discrimination algorithm should function appropriately in CT saturation conditions. Here, a method is developed based on the rate of phase angle change (RoCoPA) to perform the discrimination process. The proposed discrimination index employs the RoCoPA of the three-phase differential currents and calculates the standard deviations of the RoCoPA of the three-phase differential currents. The discrimination procedure is designed based on the fact that during fault scenarios, the signal remains almost sinusoidal, and as a result, the RoCoPA has the minimum variations. On the contrary, owing to the non-sinusoidal wave shape of the inrush current, the RoCoPA has notable variations. Based on the various simulated and practical scenarios, the proposed index's performance is evaluated considering challenging scenarios. The results reveal that the proposed discrimination index has promising accuracy with average response delay with about half a cycle. [ABSTRACT FROM AUTHOR]

• A hybrid time-frequency model for EAF. • A four-winding model for the EAF's transformer. • Modeling of the tap changer mechanism. • An efficient differential protection algorithm. This paper deals with the modeling of EAF feeding system taking into account the tap transition for assessment of a suggested scheme of differential protection. There are several challenges in the implementation of the differential protection in the EAF transformer which reveal the importance of the system modeling. Large current in the secondary side of the transformer, similarity of normal and fault conditions, and frequent tap variation in the transformer during the melting procedure are the most serious concerns. Considering the mechanism of the tap changers, the new tap setting is readjusted in the algorithm to prevent mal-operation of the protection. These challenges can be rectified by suitable modeling of EAF feeding system and an efficient differential protection scheme. To this end, the EAF as an electrical load, the EAFs' transformer, and the mechanism of the tap changing are properly modeled in this work. The proposed model of the EAF is based on real recorded data from EAFs used in a steel company, to fully simulate the nonlinear and time-variant characteristics of this electrical load. The proposed models of the EAFs' transformer and tap changer enable us to simulate different tap variations and internal faults in the system. The performance of the suggested differential protection algorithm is evaluated using plenty of simulations in different conditions of the system. [ABSTRACT FROM AUTHOR]

• Proposing discrimination index based on symmetric Kullback–Leibler divergence of the current signal. • The proposed discrimination index does not require transformer parameters. • Classifying fault and inrush currents in sub-cycle considering CT saturation. • Enhancing reliability and response time in the various fault/inrush conditions. Differential protection failure in discrimination of inrush and internal fault currents will impose significant challenges and damages both on the power transformers and power systems. To cope with this issue, this paper investigates the application of Probabilistic Distance Measure (PDM) in discrimination of the inrush and internal fault currents in power transformers. The PDMs are essentially employed to determine the dissimilarities between two sets of distributions. This paper utilizes Symmetric PDM, to determine the discrepancy between the differential current signal and the reconstructed differential current waveform. To such aim, at the first step, the reconstructed differential current waveform obtained from CTs is reproduced. The reconstructed current signal contains only sinusoidal parts of the fault current and thus are reproduced using the modified least squares algorithm. Eventually, the reconstructed current and the differential current signals are fed to PDM to determine the distribution discrepancy between the signals. The performance of the proposed PDM criterion is evaluated utilizing experimentally recorded data and is further compared with the state-of-the-art. The obtained results reveal that the proposed index can be successfully applied for the discrimination of inrush and fault currents even in the case of current transformer (CT) saturation. [ABSTRACT FROM AUTHOR]

The accuracy of the current transformers (CTs) supplying the generator differential relay is essential in power plant stations. One important factor which decreases the CT accuracy is the stray flux originated from the adjacent phases. During external fault conditions, the currents of adjacent phases contain a considerable decaying DC component. Thus, corresponding fluxes also contain a decaying DC component which cannot be counteracted by bus bar enclosure and generator shell, completely. Hence, the CT output signal deviates from the correct waveform which may lead to undesirable operation of the differential relay supplied by the CT. In fact, there is no internal fault in the generator but the differential relay trips the breaker due to negative impact of stray flux on CT accuracy. In this paper, a real power plant bus bar is modeled using three-dimensional (3D) finite element method. Then, the effect of the stray flux on the accuracy of the CTs is studied. Finally, flux equalizing windings are considered on the CT’s secondary winding. According to finite element simulation results, the negative impact of the stray flux on the CT accuracy is decreased considerably by the flux equalizing windings. [ABSTRACT FROM PUBLISHER]

• We propose twofold discrimination indices based on kurtosis of the current signal. • The proposed discrimination indices do not require transformer parameters. • It allows sub-cycle classification of fault and inrush current under CT saturation. • It enhances reliability and response time in various fault/inrush conditions. Differential protection is a widely-used approach for power transformer protection, the performance of which may be, at times, challenged due to the inrush current and current transformer (CT) saturation. This paper presents an enhanced statistical algorithm for discrimination of fault and inrush current signals under CT saturation scenarios. This paper proposes twofold discrimination indices centered on the concept of fourth-order statistic moment, the so-called kurtosis on the current signal. Mathematically proven, the algorithm performance only depends on the current signal phase angle and it does not require transformer parameters. The mathematical closed form formulation in this paper classifies fault condition and inrush currents in sub-cycle timeframes even in the CT saturation conditions. The performance of the proposed algorithm is verified under various fault and inrush current scenarios with and without CT saturation. The numerical results demonstrate that the proposed algorithm can successfully classify the disturbances and detects saturations quickly with promising accuracy. Index Terms: Inrush current; internal fault; current transformer saturation; differential protection [ABSTRACT FROM AUTHOR]