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DC series arc faults are known as a great threat to photovoltaic (PV) systems. Due to component aging and the high level of dc voltage, the occurrence of dc series arc fault is a serious concern in PV generation units. This article deals with driving effective dc series arc models from the original Nottingham arc model using the recorded practical data in a PV system. The original Nottingham arc model has three constant parameters and suffers significant inaccuracy for modeling the series arc faults. To avoid the inaccuracy in the modeling, at first, a Nottingham model is developed that is relevant to voltage and current of the fault with three parameters including one constant model order for exponents of the current and two time-series coefficients. Also, to further improvement in the model's accuracy, a two-fold Nottingham model is proposed that is relevant to voltage and current of the fault with five parameters including two constant model orders for exponents of the current and three time-series coefficients. Finally, a full-time-variant Nottingham model is proposed that mathematically contains three time-series parameters including one time-variant model order of the current exponent. In the full-time-variant Nottingham model, the optimal parameters corresponding to each sliding window of data are utilized. In all the proposed models, the arc coefficients are estimated using a new formulation using the least-squares error technique. Verifying the effectiveness of the proposed models through actual data, the accuracy of the proposed models is compared with the original Nottingham model. [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]

A novel scheme is suggested for change detection and fault classification in ac microgrids (μGs) with different operating modes. The proposed method utilizes a Teager–Kaiser energy operator based approach for analyzing the current-based signal to detect changes in the μG. The current-based signal is a summation of squared three-phase currents (SSC), measured at one end of a line in the μG. Since μGs have different operating modes, a proper signal should be defined to have some valuable signatures of the changes in different conditions. The SSC signal is constant during normal conditions of the grid-connected and islanded μG, whereas in any possible change condition experiences considerable variation. After the detection, the faulty phase is determined using three similar indices, derived from the squared currents. These indices are the ratios of squared currents for each phase with half-cycle window length, before and after the change detection. The proposed protection scheme is able to handle both operation modes of the μG. The proposed technique is evaluated in a MATLAB/Simulink simulated network and laboratory small-scaled test bench. The results confirm high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods. [ABSTRACT FROM AUTHOR]

Directional relays have a key role for protection of transmission and distribution networks. They are used as supervisory element for distance and overcurrent protections. Therefore, accuracy and performance of these protections are dependent on the performance of directional relays. In this paper, a high-speed current-based directional algorithm is presented. For a specified interval, the fault current samples are multiplied by corresponding prefault current samples and summation of the results is calculated as a criterion. Sign of the summation and direction of power flow at normal condition determine fault direction. In spite of most high-speed techniques, just accurate current transformers are needed to obtain the criterion and determining normal power flow can be relegated to simple voltage transformers at steady-state condition. The method is evaluated using plenty of simulations and experiments. Also, performance of the proposed algorithm is compared with some similar approaches. The results confirm reasonable accuracy, speed, and simplicity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient strategy for control of multiple grid connected microgrids (MGs) through converters, to improve network management during normal, islanding, and fault conditions. In normal condition, the converters control the bidirectional power flow between the MGs (downstream) and the main network (upstream). When the upstream is disconnected from downstream, if one of the MGs cannot handle its local loads, the deficit of power is provided by the other MGs. Transferred powers of the MGs are controlled regarding power quality (PQ) preserving criteria of the network. If PQ in a point of common coupling of an MG degrades, its converter goes into semi-isolated mode. Furthermore, in case of fault occurrence in the upstream, the converters limit fault current contribution of the MGs, in order to preserve the MGs PQ. However, in case of fault occurrence in the MG, the converters are bypassed by the relevant static switches. Therefore, short circuit current contribution of the upstream will be maximized to improve the MGs PQ. This strategy can also overcome the problems related to the system short circuit capacity increment in case of a newly added distributed generation unit, such as loss of the over current relays coordination. [ABSTRACT FROM PUBLISHER]

In power grids, directional relays are used in situations that direction of fault current is not fixed. Distribution networks including distributed generation (DG) resources and transmission networks are some of these situations. Directional relays as key elements of distance and over current relays provide selectivity for these protections to improve their performance and power systems stability. In this study, a directional algorithm is proposed, which is able to discriminate fault direction just by sign of imaginary part of post-fault current phasor. To evaluate the algorithm, plenty of simulations and experiments are carried out in different fault conditions. Moreover, impact of power flow direction at normal operation of the power system is considered in the evaluation of the algorithm. Also, its performance is compared with some other directional algorithms. The results confirm that the proposed algorithm is able to detect fault direction with high degree of accuracy. Moreover, simplicity of the algorithm represents its competence for practical implementation in transmission and distribution networks. [ABSTRACT FROM AUTHOR]

In power grids, directional relays are used in situations that direction of fault current is not fixed. Distribution networks including distributed generation (DG) resources and transmission networks are some of these situations. Directional relays as key elements of distance and over current relays provide selectivity for these protections to improve their performance and power systems stability. In this study, a directional algorithm is proposed, which is able to discriminate fault direction just by sign of imaginary part of post‐fault current phasor. To evaluate the algorithm, plenty of simulations and experiments are carried out in different fault conditions. Moreover, impact of power flow direction at normal operation of the power system is considered in the evaluation of the algorithm. Also, its performance is compared with some other directional algorithms. The results confirm that the proposed algorithm is able to detect fault direction with high degree of accuracy. Moreover, simplicity of the algorithm represents its competence for practical implementation in transmission and distribution networks. [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]

This study deals with a method for discrimination between magnetising inrush and internal fault currents in three‐phase transformers based on sine‐wave curve fitting. A sine wave is fitted to the normalised differential current by using the least‐squares technique for each phase. The difference between the normalised differential currents and the fitted signals are calculated, which are hereafter named residual signals (RSs). Maximum range of the RSs variations is utilised for the definition of a simple criterion. Based on this criterion, the discrimination is realised within less than a half cycle of power frequency. In order to eliminate the impact of the current transformer (CT) saturation on the performance of the proposed technique, the currents are compensated by a CT saturation compensation algorithm in the first step. The proposed method is evaluated and compared with some well‐known methods by a large number of simulation and experimental data. Furthermore, the proposed algorithm is evaluated in the case of a transformer, which is fed through a feeder with series capacitors. The results confirm that reliability, accuracy and speed response of the proposed technique is more than the other methods. [ABSTRACT FROM AUTHOR]

• 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]

• It has been Mathematically proven that, due to the use of the rate of change of fault current signal, the proposed index has very little sensitivity to the decaying DC component. • Unlike previous methods, the proposed index does not suffer from voltage-drop issues for fault near the directional algorithm. • In the proposed index, a half-cycle moving average indicator is employed and as a result, the presence of the proposed index reduces the sensitivity of the algorithm to noise. • Comparing to the state-of-the-art algorithms, the proposed method requires a low sampling rate. Also, low-complexity. The proliferation of Distributed Generations (DGs) in distribution networks has provided power system operation improvement while raising some protection challenges. Turning to bi-directional, the protection schemes of the deregulated distribution networks should be able to deal with an out-of-zone fault. More specifically, DGs impose bi-directional fault current and directional relay should be employed to identify correct fault direction so that the protective relays are prevented from mal-operation of out-of-zone fault. This paper introduces a current-based directional algorithm that utilizes a pre-fault current signal as the reference. This algorithm is designed based on the pre-fault current and rate of change of the fault current. As it can be inferred from the mathematical basis of the proposed method, it has low sensitivity to decaying DC and noise components. Also, the proposed index has a certain and straightforward range of variation between (-1, 1) for backward and forward fault direction, respectively. The performance of the proposed algorithm is evaluated for different scenarios such as variation of fault resistance, sampling frequency, and types of faults in three simulated systems and a laboratory test bench. The simulation and experimental evaluation results show the accuracy and speed of the proposed algorithm in comparison with similar algorithms. [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]

• 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]