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An accurate model of electric arc furnaces (EAFs) is required to analyse their impact on the system's power quality. One of the well‐known approaches to model EAF is using the power balance equation. This model has two terms, each one has a coefficient named the model parameter. Also, both terms are powered to values, which are called model orders. So, the model includes two model parameters and two model orders. Model orders highly affect the modelling precision. The optimum model orders have not been obtained yet in previous studies. Here, based on many actual records from EAFs, the best model orders for refining, melting, and scrapping stages are attained. Unlike the original power balance equation which has constant model parameters, the first proposed model uses the attained optimal model orders with the time‐varying model parameters. In the second proposed model, the model orders are also considered time‐varying. Model parameters, which are updated every 10 ms, are obtained using autoregressive moving average (ARMA) models. The correctness of the models is assessed by calculating some important electric quantities using the actual data and the proposed models. Finally, the time‐varying parameters are generated using two MATLAB codes. [ABSTRACT FROM AUTHOR]

Optimal coordination of the protection devices (PDs) is performed here simultaneous with optimal allocation of them and the DGs. The PDs are a set of directional overcurrent relays (DOCRs) and voltage relays, each with its own fast switch (FS). In previous optimal DG placement studies the coordination of the PDs has been considered as a constraint. So, it was not possible to optimal coordination of the PDs simultaneously. In the present study, the coordination between the PDs is considered in calculating the value of the energy not supplied (ENS) that is a part of an overall cost function. The cost function considered here includes cost of purchase, installation, and maintenance of the DGs and the PDs, cost of the energy losses, cost of the ENS, cost of the purchased energy from the main grid and the DGs, and the penalty cost for emitting environmental pollutants. Here, output of the renewable DGs and the network loads during the year, in a 96‐h model, has been considered as variable. To solve the desired optimization problem, an algorithm based on evolutionary algorithms such as genetic algorithm (GA) is proposed. Finally, various cases have been simulated by considering a standard 33‐bus radial network. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the most disturbing loads in the power system as it is highly time‐varying and nonlinear. In this paper, new models based on the instantaneous EAF voltage and current, recorded from EAFs in Mobarakeh steel company in Isfahan/Iran are proposed. The focus of this paper is on developing efficient models based on the Schwarz model where three modified types are proposed. The original Schwarz model has four constant parameters. Unlike the original Schwarz model which uses invariant parameters, the time‐varying nature of the arc's parameters is considered in the proposed models. The first modified model has two constant model orders and two time‐varying coefficients. Additionally, to further enhance the model's accuracy, in the second modified model, six parameters include three constant model orders, and three time‐varying coefficients exist. The third model uses all the original model's parameters as time‐series parameters. The parameters of the time‐varying model are derived using autoregressive moving average (ARMA) models. The accuracy of the proposed model is evaluated by calculating the active power, harmonics, instantaneous flicker, and short‐term flicker. Finally, three MATLAB codes are provided to generate the model parameters time series. [ABSTRACT FROM AUTHOR]

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]

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]

Since the penetration level of the inverter‐based distributed generations (IBDGs) into microgrids (MGs) is increasing, the protection issues of such networks have become more challenging. The present work aims to design a protection framework for fault detection and classification in IBDG dominated MGs. In the proposed approach, the current waveforms measured from one end of the protected lines are processed using superimposed components. Then, the faults are identified using the Euclidean distance concept. A Pearson correlation coefficient‐based approach is also developed to characterize the faulty phases in the proposed fault classification unit. Furthermore, an auxiliary index is defined to discriminate load change conditions from the faults. In addition to being simple and efficient, the proposed technique is highly capable of functioning in different MG operating modes and configurations without changing its settings. Also, it demonstrates robust performance against IBDG impacts, noises, and stimulating fault scenarios. To assess the scheme proficiency, a modified IEEE 15‐bus system with highly penetrated IBDGs is simulated. The results confirm the quickness and high accuracy of the proposed technique. Moreover, the method is validated in an experimental laboratory test bench. Finally, the superiority of the method is confirmed by a comparative study with other recent methods. [ABSTRACT FROM AUTHOR]

Series arc faults (SAFs) are the most common faults in photovoltaic systems. If a SAF is not detected in time, it may lead to some catastrophic damages to the system. This article presents a new method for detecting SAFs in photovoltaic systems based on determinism in the voltage signal. The determinism of the signal is derived using the recurrence plots method. The recurrence plots provide a two-dimensional plot of the signal, from which the degree of determinism in the signal is derived as a criterion for detection of SAFs. The performance of the method is evaluated using plenty of experiments carried out in different SAF and normal conditions. The results confirm the high accuracy, high speed, and low computational burden of the proposed method. [ABSTRACT FROM AUTHOR]

This study deals with a new approach to tackle the challenges encountered in the filters-based reactive power calculation techniques utilized in the control system of static VAr compensators. In this approach, step and time-varying responses are improved by a combination of differential and filter-based methods. Then, optimum values of the main parameters of the low-pass and band-stop filters are determined considering three criteria: accuracy, speed, and capability for following the time-varying loads. To assess the performance of the proposed method, a new objective function is introduced, calculated for two sets of signals, captured from a real field in Mobarakeh Steel Company in Isfahan/Iran. Then, the proposed and the other reactive power calculation methods are assessed in practice and simulation. Also, the algorithm is implemented using a TMDSCNCD28335 as the processor to assess the real-time feasibility of the method. To do so, the reactive power variation of an electrical arc furnace is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [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]

Accurate modelling of the Electric arc furnace (EAF) is necessary to study its impact on the power systems. Here, a new model for the EAF is presented in which both non‐linearity and time‐varying characteristics are taken into account. The model consists of seven parallel current sources which represent the fundamental and harmonic currents. Magnitude and phase of the fundamental harmonic and magnitude of the second to seventh harmonics are considered as time‐varying which are updated every 0.01 s. To do so, the actual data recorded from eight EAFs in the Mobarakeh steel company (MSC), Isfahan, Iran are used. Autoregressive moving average (ARMA) models are employed to derive the parameters of the time‐varying model. Also, different orders and coefficients of the ARMA model are considered at each run of the model, which enables the proposed model to predict the non‐stationary and stochastic behaviours of the EAF over long‐time periods. It is shown that the proposed combined time‐varying‐harmonic model can even model the generated inter‐harmonics. To evaluate the accuracy of the proposed model, the derived power spectral density (PSD) of the EAF time‐varying parameters, the instantaneous flicker, and short‐term flicker by the proposed model are compared with the actual recorded data. [ABSTRACT FROM AUTHOR]

DC series arc faults are known as a great threat in photovoltaic (PV) systems that may lead to fire and electric shock hazards. This article concentrates on driving effective models based on Cassie and Mayr equations for dc series arc using actual recorded data. Despite Cassie and Mayr equations that use invariant parameters during arc modeling, the proposed models employ the combinations of both Cassie and Mayr equations in which the time-variant nature of arc’s coefficients is taken into account. While the original Cassie and Mayr equations have two influential parameters for arc modeling, the first model presents a developed Cassie–Mayr equation with three parameters. Also, to further improvement in the model’s accuracy, the second model presents a developed Cassie–Mayr equation with five parameters. While the first and second models provide more prevision comparing with the original Mayr and Cassie–Mayr modeling, a third model is presented that treats with parameters of the original model as time-series parameters. To obtain the arc’s coefficients, a new formulation for coefficient estimation based on Levenberg–Marquardt (LM) algorithm is presented. Evaluating the proposed models with the actual recorded data, the results show the proposed models have more accuracy in comparison with the original Mayr and Cassie–Mayr. [ABSTRACT FROM AUTHOR]

Fast reactive power variation in wind farms creates flicker. Static VAR compensators (SVCs) are the most commonly used devices for flicker mitigation. The reactive power calculation unit is the main component in the control system of SVCs. This article deals with two new approaches for reactive power calculation. In both methods, the main parameters of a filter-based technique are optimized using a novel optimization approach. The first method has a non monotonic step response which is converted to a desirable monotonic response by the second method. This results in a better response to a time-varying reactive power. The problem formulation and definition of a new objective function are presented. A data pool is gathered from Manjil wind farm, Iran, for different conditions. This data pool is used in the objective function to derive the optimal setting. The performance of the proposed methods is assessed using this dataset and is compared with other techniques. Also, real-time implementation of the algorithm is evaluated using TMDSCNCD28335 as the processor. To do so, the reactive power variation in a wind generation unit is emulated in a prototype test bench and the algorithm is run in real-time by the processor during the system operation. [ABSTRACT FROM AUTHOR]

• Four new indices are defined to quantize branches responsibility to sag/swell and flicker. • The indices are calculated using a cumulative approach in real-time, so the decision can be made upon a long-time duration. • The indices are based on the RMS values of the voltage and current which can be communicated every cycle. • The calculation algorithms are implemented in a recursive way to minimize the computational burden. The purpose of this study is to find simple cost-efficient algorithms for real-time quantizing the responsibility for power quality issues in every branch in an industrial network. The study is focused on voltage sag/swell and flicker which are caused by the time variation characteristics of loads. Here by using the RMS values of the voltage and current, four indices are defined. Cumulative-recursive equations are used to reduce the computation load and to be able to evaluate the power quality problems in real-time. Two indices quantize the loads' responsibility in sag/swell events. The duration of the sag/swell events is considered in the second index. Two indices are proposed for flicker. The first flicker index is based on the FFT of the flicker powers in the individual flicker frequencies. The second flicker index is based on the FFT of the calculated time-varying load admittance corresponding to flicker range frequencies. The load variation coherency of the different branches affects the first flicker index while it has no impact on the second one. The performance of the proposed indices is tested using simulation data, actual records from electric arc furnace and a wind farm, and a laboratory test. Results reveal that the proposed indices efficiently quantize branches' responsibility in the sag/swell and flicker events. The sag/swell indices increase after every event caused by the responsible branch in proportion with the event depth and duration. The flicker indices increase in proportion to the flicker powers according to IEC868 flicker sensitivity coefficients. [ABSTRACT FROM AUTHOR]

• Two improved types of accurate models based on the Schavemaker model are developed. • Unlike the original Schavemaker model which uses invariant parameters, the time-varying nature of the arc's parameters is considered in the proposed models. • The first improved model has time-varying coefficients. • In the second improved model, a new term has been added to the schavemaker model that includes time-varying coefficients. • A large number of the actual data are used. Electric arc furnace (EAF) is more and more applied in large industrial systems and represent one of the major sources of perturbations for the power system because it is highly non-linear and time-varying. New models are suggested in this research based on instantaneous EAF voltage and current measurements taken from EAFs at the Mobarakeh steel mill in Isfahan, Iran. This paper aims to develop two improved types of accurate models based on the Schavemaker model. The original Schavemaker model has three constant parameters. Unlike the original Schavemaker model which uses invariant parameters, the time-varying nature of the arc's parameters is considered in the proposed models. The first improved model has time-varying parameters. Additionally, to further enhance the model's accuracy, in the second improved model, a term has been added to the schavemaker model that includes time-varying parameters. Compared to the original Schavemaker model, all of the developed Schavemaker models are more precise. Finally, auto-regressive moving average (ARMA) models are used to attain the time variation characteristic of the parameters. [ABSTRACT FROM AUTHOR]

Summary: Generally, photovoltaic (PV) panels are affected by variety of internal and external faults. Hotspot is regarded as an internal and permanent fault that is highly destructive in solar panels. In this study, a novel approach based on PV output current trajectory is proposed for hotspot detection in PV strings. The hotspot detection criterion consists of two parts. First, the Teager‐Kaiser energy operator technique is applied for current change detection. Then, a novel hotspot detection index based on the cumulative rate slope calculation of PV current is applied. The proposed index has the advantage to distinguish between hotspot condition and other faults only by utilization of current measurement. The thermal behavior and the current trajectory are evaluated during hotspot condition. It is shown that the current characteristic of the cell during hotspot condition has a unique behavior, which is also justified by mathematical analysis. Different shading scenarios are considered in this study. However, the proposed approach can easily detect the one which cause hotspot condition and distinguish it from other type of scenarios. Therefore, the proposed approach can detect hotspot fault before the solar cell destruction. MATLAB simulations are applied in order to confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Photovoltaic networks are outdoor infrastructures, faced to different harsh conditions, which may experience various failures such as parallel arc fault (PAF) and series arc fault (SAF). Although PAF is more severe than SAF, the detection of SAF is more problematic, and its hazards including fire and the risk of staff electrocution are more serious. This paper proposes a new method for timely and reliable detection of SAF in photovoltaic systems. In this method, one of the blind-source separation algorithms called the principal component analysis (PCA) is employed. This method separates the nondependent components of some measurable quantities such as voltage and current using eigenvectors of their covariance matrix. In a normal condition, these signals include the dc component, switching components, and the network disturbances. When a SAF occurs, some new components by the electrical arc also add to the system. Using PCA, a suitable index is derived to discriminate signatures of SAF from the other components. Performance of the method is evaluated using plenty of experiments in different conditions. [ABSTRACT FROM AUTHOR]

Fault detection in induction motors has drawn growing attention due to importance of these motors in different industries. Analysis of motor currents is a common method for the fault detection. This method is preferred compared with other approaches because of its economic and technical superiorities. In this paper, a modified model of an induction motor including stator fault and a new technique are proposed for detection of stator winding faults. Firstly, Walsh-Hadamard transform is applied to stator currents. Then, variation of Walsh-Hadamard transform is used as the criterion for the detection. Plenty of simulation cases are utilized to evaluate the method. To enhance accuracy of the model, isolation layer resistance (Rf) is also taken into account. For further evaluation, a large number of experimental data were recorded from different experiments to assess the method in practice. [ABSTRACT FROM AUTHOR]

In order to improve the dependability and security of transformer differential protection, the energisation conditions should be effectively distinguished from internal fault conditions. In this study, a new method with a low computational burden and high robustness against measurement noises is proposed for discriminating between the transformer inrush current and internal faults. In the proposed method, sine waves are fitted to the sample points of the normalised differential currents for different phases using recursive extended least‐squares (RELS) algorithm in an online manner. The extended kernel enables the algorithm to effectively estimate the dynamics of the measurement noises. Three residual signals which are defined as the differences between the normalised differential currents and the fitted sine wave signals are considered as the decision criteria. Based on the selected criteria, the energisation condition is identified in about a half‐cycle of power system frequency. A large number of simulation and experimental test cases are used to demonstrate the effectiveness of the proposed method. The results show that the proposed algorithm achieves an accuracy of ∼98% and has a relatively low operating time, while the computational burden for the embedded implementation is favorably low. [ABSTRACT FROM AUTHOR]

A method based on extreme learning machine (ELM) is suggested to locate faults in multi-terminal high-voltage direct current systems. S-transform and wavelet transform are used for extraction of the features used for the learning. The accuracy of the technique for various types of input signals and different lengths of the analyzed window is investigated. Two different approaches are considered for employing the ELM in this application. In the first approach, an ELM is used for total length of the line. In the second one, a multi-ELM technique is applied to different sections of the transmission line. In this approach, one ELM is considered for each of the divided sections. It is proved that the performance of the method is improved by the multi-ELM approach in comparison with the single ELM one. The performance of the ELM approach is compared with the artificial neural network and support vector regression techniques. [ABSTRACT FROM AUTHOR]

Multi-layer perceptron neural networks (MLP-NN) are widely utilized in forecasting applications. However, optimal training of these networks is still a challenge. A comprehensive assessment of MLP training approaches comprising of three stages is performed in the present paper. First, the prediction performance is evaluated using twelve training algorithms. Next, optimization algorithms are utilized to enhance the best obtained network parameters obtained from the first step and the performance of eight optimization algorithms is evaluated. Finally, a novel modification is used to improve the performance of the optimization algorithms. The proposed methodologies are applied to two case-studies and statistical metrics are employed for their efficiency evaluation. Wavelet transformation is used to extract the features which will be fed to the MLP-NN as input data. [ABSTRACT FROM AUTHOR]

In this paper, a simple and fast approach is suggested for fault detection in transmission lines. The proposed technique utilizes a modified cumulative sum approach for a modal current to identify faults. The modal current is derived by proper linear mixing of three-phase currents. Since different types of faults may occur in transmission lines, all three-phase currents should be considered during fault analysis. By converting three-phase currents to a modal current, the processing time is reduced and less memory is needed. In this paper, a modal current is processed instead of three-phase currents. The modified cumulative sum approach presented in this paper is capable of decreasing computational burdens on the digital relay and accelerating the fault detection procedure. The proposed fault detection technique is evaluated in four different systems. Moreover, some real recorded field data were deliberated in the efficiency assessment of the proposed method. The results denote high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods from different aspects. [ABSTRACT FROM AUTHOR]

Expansion of cogeneration technologies, such as combined heat and power units, has boosted the growth of multi-carrier energy systems. A two-step method is presented to enable residential demand response (DR) programmes in the multi- carrier energy systems. In the first step, the energy management system at each home solves an optimisation problem to achieve the desired energy cost and demand schedule for the customer according to received price signals. In the second step, the system operator revises the demand scheduling by running another optimisation problem to minimise the total electrical losses, subject to the operational characteristics of electrical and natural gas systems. In order to persuade customers to participate in the DR programmes, it is guaranteed that the resulted cost of the second step is not more than the desired cost of the customer in the first step. Results of applying the proposed method, incorporating different penetration levels of customer participation in a time-of-use programme is studied in a test energy system. Simulation results verify the effectiveness of proposed method in minimising the total electrical losses, improving the operational characteristics of the energy system as well as providing customers' utilities. [ABSTRACT FROM AUTHOR]

Summary: Two approaches exist for modeling electric arc furnace, the largest electrical load. Distinct mathematical equations are used as the first approach. However, the process is nonstationary and the equation is limited to 1 form and unable to extract all nonlinear components. Black‐box models such as artificial neural networks are used as the second approach. However, using a black‐box model for a process with partially known equations is not efficient, as it cannot benefit from known knowledge of the process. This paper proposes a combination of the 2 mentioned approaches. The first phase consists of nonlinear and dynamic terms. The appropriate model orders relate to dynamic and nonlinear terms are attained. In the second phase, artificial neural network is used to model the rest of the nonlinear components, which are not captured in the first phase. Numerous actual records are used to compare the proposed model with several electric arc furnace models. [ABSTRACT FROM AUTHOR]

Expansion of cogeneration technologies, such as combined heat and power units, has boosted the growth of multi‐carrier energy systems. A two‐step method is presented to enable residential demand response (DR) programmes in the multi‐carrier energy systems. In the first step, the energy management system at each home solves an optimisation problem to achieve the desired energy cost and demand schedule for the customer according to received price signals. In the second step, the system operator revises the demand scheduling by running another optimisation problem to minimise the total electrical losses, subject to the operational characteristics of electrical and natural gas systems. In order to persuade customers to participate in the DR programmes, it is guaranteed that the resulted cost of the second step is not more than the desired cost of the customer in the first step. Results of applying the proposed method, incorporating different penetration levels of customer participation in a time‐of‐use programme is studied in a test energy system. Simulation results verify the effectiveness of proposed method in minimising the total electrical losses, improving the operational characteristics of the energy system as well as providing customers' utilities. [ABSTRACT FROM AUTHOR]

Load flow analysis is an essential part of system operation and planning. Up to now, load flow can be categorized as deterministic and probabilistic. In the deterministic load flow, the generations and loads are fixed. However, the state variables of power systems have uncertain nature due to the random load variations and stochastic distributed generations. In this regard, the probabilistic load flow (PLF) is implemented. The inputs of PLF are probability density functions (PDFs) of buses powers and the outputs are PDFs of system states. Therefore, the relation between the system variables over the time will be lost. To overcome this issue, analytic time series load flow (analytic TLF) is introduced in this work. TLF considers the effect of time synchronization and correlation between different loads in the network. Auto regressive moving average (ARMA) models are used to model the time varying loads and generations. The inputs in analytic TLF are ARMA model parameters of active and reactive powers of load buses and the active power of generation buses. The outputs are ARMA parameters of the voltage magnitude and phase of load buses as well as voltage phase of generation buses. The performance of the proposed analytic TLF is evaluated using several examples. [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]

Monitoring of the Induction Motors (IMs) through stator current for different faults diagnosis has considerable economic and technical advantages in comparison with the other techniques in this content. Among different faults of an IM, stator and bearing faults are more probable types, which can be detected by analyzing signatures of the stator currents. One of the most reliable indicators for fault detection of IMs is lower sidebands of power frequency in the stator currents. This paper deals with a novel simple technique for detecting stator turn-fault of the IMs. Frequencies of the lower sidebands are determined using the motor specifications and their amplitudes are estimated by a Kalman Filter (KF). Instantaneous Total Harmonic Distortion (ITHD) of these harmonics is calculated. Since variation of the ITHD for the three-phase currents is considerable in case of stator turn-fault, the fault can be detected using this criterion, confidently. Different simulation results verify high performance of the proposed method. The performance of the method is also confirmed using some experiments. [ABSTRACT FROM AUTHOR]

The output active and reactive powers of wind farms are highly time-varying even in extremely short time periods which cause flicker. In many wind farms static VAr compensators (SVCs) are employed to mitigate this flicker. In this study it is shown that reactive power variations of wind farms are even faster than those which can be fully compensated by SVC. This inability of SVCs comes from the delays in the reactive power measurement and thyristor ignition circuit. To overcome this limitation, the reactive power of a wind farm is predicted for a half cycle (10 ms) ahead. Large numbers of data were recorded from the Manjil wind farm in different weather conditions. Auto regressive moving average (ARMA) models were used for the prediction, and model adequacy checking was employed to determine the best ARMA model order. To evaluate the performance of proposed method, with a novel procedure the wind farm is simulated using the actual recorded data. The instantaneous flicker sensation and short term flicker were calculated and compared for three cases; without SVC, using SVC with the traditional control method, and using SVC with the proposed prediction method. The results confirm the superiority of the proposed technique. [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]

The output active and reactive powers of wind farms are highly time‐varying even in extremely short time periods which cause flicker. In many wind farms static VAr compensators (SVCs) are employed to mitigate this flicker. In this study it is shown that reactive power variations of wind farms are even faster than those which can be fully compensated by SVC. This inability of SVCs comes from the delays in the reactive power measurement and thyristor ignition circuit. To overcome this limitation, the reactive power of a wind farm is predicted for a half cycle (10 ms) ahead. Large numbers of data were recorded from the Manjil wind farm in different weather conditions. Auto regressive moving average (ARMA) models were used for the prediction, and model adequacy checking was employed to determine the best ARMA model order. To evaluate the performance of proposed method, with a novel procedure the wind farm is simulated using the actual recorded data. The instantaneous flicker sensation and short term flicker were calculated and compared for three cases; without SVC, using SVC with the traditional control method, and using SVC with the proposed prediction method. The results confirm the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

Integration of distributed generations into distribution systems has various impacts on power networks such as reconstruction of distribution networks and increase in short‐circuit capacity. Reconstruction of distribution networks magnifies importance of detecting faults directions, which is relegated to directional relay. On the other hand, one of the most attractive approaches to mitigate the increased short‐circuit capacity is utilising fault current limiters (FCLs). Since FCLs can drastically affect voltage and current signals during the first quarter cycle after fault occurrence and due to overlap between operation time of FCLs and directional relays, the affected voltage and current signals can lead to mal‐operation of directional relays. To investigate this issue, performance of some well‐known directional relay algorithms is investigated with and without the presence of FCL using plenty of simulations and some experiments. It is concluded that the FCL has different impacts on the investigated algorithms, so that some of them are not affected and some others are considerably affected. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the largest non‐linear loads in power systems. It is highly time‐varying which leads to low‐frequency voltage fluctuations in the system, called flicker. Static VAr compensator (SVC) is always used in EAF plants to compensate the reactive power and reduce voltage fluctuations. The reactive power calculation unit is one of the most important units in SVC control system. Since SVC can only compensate the fundamental component, the calculated reactive power should not be sensitive to other harmonics. On the other hand, since the reactive power changes every half cycle, its calculation should be fast enough to track the changes. Hence, a calculation method with minimum delay and lowest harmonic sensitivity should be utilised to achieve an effective compensation. Among different already proposed methods for calculation of reactive power, differential method has the lowest delay. However, it is very sensitive to harmonics. In this study, Kalman filter is used to extract the fundamental components of voltage and current with a little delay and then they are used as inputs for the differential method. The proposed method is evaluated using some simulation signals and practical records from several EAFs in Mobarekeh steel company in Esfahan – Iran. [ABSTRACT FROM AUTHOR]

There are many different techniques for discrimination between inrush current and internal faults. However, due to their inherent limitations, the methods have not reached a practical level yet, and the conventional methods are still widely employed in practice despite of their well‐known shortcomings. Current transformer saturation and large inrush currents are the most reported cause of the discrimination algorithms mal‐operation. This paper deals with an efficient approach to discriminate internal faults from switching conditions in the power transformers which can solve these problems. The proposed method is based on utilising the feature of the non‐saturation zone. The performance of the proposed method is tested using the simulations data besides to data collected from a prototype laboratory three‐phase power transformer. Also, the proposed method is evaluated and compared with some well‐known discrimination schemes including conventional and some new presented schemes by the same simulation and experimental data sets. The results validate the consistency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the largest non-linear loads in power systems. It is highly time-varying which leads to low-frequency voltage fluctuations in the system, called flicker. Static VAr compensator (SVC) is always used in EAF plants to compensate the reactive power and reduce voltage fluctuations. The reactive power calculation unit is one of the most important units in SVC control system. Since SVC can only compensate the fundamental component, the calculated reactive power should not be sensitive to other harmonics. On the other hand, since the reactive power changes every half cycle, its calculation should be fast enough to track the changes. Hence, a calculation method with minimum delay and lowest harmonic sensitivity should be utilised to achieve an effective compensation. Among different already proposed methods for calculation of reactive power, differential method has the lowest delay. However, it is very sensitive to harmonics. In this study, Kalman filter is used to extract the fundamental components of voltage and current with a little delay and then they are used as inputs for the differential method. The proposed method is evaluated using some simulation signals and practical records from several EAFs in Mobarekeh steel company in Esfahan – Iran. [ABSTRACT FROM AUTHOR]

Due to increase in penetration of Distributed Generations (DGs) in power systems, fault current level is being increased, which results in some problems in the systems. Fault Current Limiters (FCLs) are attractive devices to tackle these problems for transmission and distribution systems. The utilized FCLs may have considerable impact on the signals used for differential protection of power transformers, which leads to mal-operation of these protections. It seems a comprehensive analysis is necessary for performance evaluation of differential protection algorithms in presence of FCLs. This paper deals with investigation of FCLs impact on power transformers’ differential protection. The performance of some well-known differential protection algorithms for discrimination between internal fault current and magnetizing inrush current with and without presence of FCL are evaluated. [ABSTRACT FROM AUTHOR]

The recently proposed techniques for detection and classification of different faults are amenable to statistical-based methods such as correlation or alienation coefficients. In this paper, an improved alienation coefficient-based classification algorithm is proposed. In the method, a modified alienation coefficient formula is suggested, by which different faults could be identified more accurately. Switching of heavy loads is also considered in the proposed algorithm. The performance of the method is evaluated by simulation of a typical power system in different conditions. The results confirm high capabilities of the proposed method for detection and classification of different faults. Furthermore, the results are verified using a low-scale test bench system in the laboratory. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Phase‐to‐ground fault is the most probable fault type in power systems, which is generally limited through neutral grounding resistor. The grounding resistor may have negative impacts on the system such as decrease in sensitivity of the differential protection. Fault current limiter (FCL) is a suitable alternative for the grounding resistor. However, the input signals of differential protections may be adversely affected by the FCL, resulting mal‐operation of this protection. In this study, effects of FCL installed in neutral line of power transformers on the relevant differential protection algorithms are analysed. Performance of some well‐known differential protection algorithms for discrimination between internal fault and magnetising inrush currents in presence of the neutral FCL is evaluated in detail. Then, a supplementary method based on the least error squares technique is proposed to rectify the deformed portion of the current waveform due to presence of the FCL. The reconstructed signal is considered as a proper input for the discrimination algorithms. Various simulations and experiments are carried out to validate the performance of the suggested technique. The results indicate the consistency of the suggested technique for different power systems, operating conditions, and real applications. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is one of the largest loads in the power systems. Unfortunately, it is highly non‐linear and time varying which causes power quality problems such as harmonics and flicker. Therefore, having an accurate EAF model is necessary. Cassie model is one of the most utilised EAF models in the related fields. However, actual data from electrical system of Mobarakeh Steel Company in Isfahan/Iran show that this model is unable to take into account some important quantities such as the active power and harmonics. Hence, as the first step in this study, different Cassie–Mayr model variants (include the Cassie model) are investigated and the best variant is attained. A novel procedure using large number of recorded actual data is utilised for the models assessment. In the second step, two generalised types of the original Cassie–Mayr model are proposed. Both the generalised types are more accurate than the best‐selected Cassie–Mayr variant. All the proposed models have time‐varying parameters. Their time‐varying nature is studied and by analysing the time series, the proper auto regressive moving average models are attained for every parameter. [ABSTRACT FROM AUTHOR]

Stand-alone micro-grids (MGs) and grid-connected MGs with high penetration level of Distributed Generation (DG) are growing at a fast rate. In these grids, the power quality disturbances such as harmonics, inter-harmonics and deviation from the fundamental frequency are widespread. The influence of these disturbances on two categories of digital metering algorithms is evaluated in this paper. The first category is related to the measurement of the fundamental frequency component of current and voltage signals, which are used as inputs of the digital protection algorithms. Most of the relays only use fundamental frequency component of their input voltage and current signals to fulfill the desired protective functions. The accuracy of the protective relays highly depends on the performance of the applied algorithms for extracting the fundamental components. The second evaluated category is related to the reactive power calculation algorithms, which are used in the static VAr compensator (SVC) control system. Utilizing SVC is growing due to the DGs such as wind turbines magnify voltage fluctuations called flicker. Since the SVC compensates only the reactive power related to the fundamental frequency component, the reactive power signal should not be sensitive to the harmonics. In addition, the reactive power calculation should be fast enough that SVC can follow the abrupt changes to mitigate the flicker, efficiently. In this paper, frequency deviation, harmonic, and inter-harmonic phenomena are considered as the most causes for power quality problems. Their influence in digital metering algorithms is evaluated. It is confirmed that defining new indices is mandatory to fully reflect algorithms׳ accuracy and efficiency. Furthermore, the field measurements of instantaneous voltage and current of a wind farm and electric arc furnaces are used for the evaluation. [ABSTRACT FROM AUTHOR]

The phase‐to‐ground fault is the most probable fault in power systems. Generally, the neutral grounding resistor is utilised to limit the current for this fault. However, this resistor decreases the sensitivity of the differential protection. The fault current limiter (FCL) is an attractive candidate to be used instead of the regular grounding resistor. FCL only operates for high fault currents and has a negligible resistance for normal or low current faults conditions. Therefore, it reduces the high fault currents while not affecting the sensitivity of the differential protection. However, the signals used as inputs of the differential protection may be adversely affected by the FCL and this may lead to wrong operation of this protection. A comprehensive study is required to evaluate the performance of differential protection algorithms in the presence of a FCL in the neutral point. This study analyses the impact of high‐temperature resistive superconducting FCLs (SFCLs) existed in the neutral point on transformer inrush current discrimination methods. Some well‐known methods for discrimination of an internal fault from inrush current are evaluated in the presence of SFCLs. Furthermore, through extensive simulations, their performance is compared with and without the presence of SFCLs. [ABSTRACT FROM AUTHOR]

In this study, a new algorithm for directional protection, based on cross correlation of Fourier transform components of voltage and current for each phase is proposed. The fault is detected using k‐nearest neighbour algorithm. By comparing the fifth‐nearest neighbour of each sample with an adaptive threshold, the fault is detected. The adaptive threshold is derived by a moving window with length equal to 1 ms. Using Fourier transform, the fifth sinusoidal component of the measured voltage and current for each phase is extracted. Then the cross correlation of the voltage and current components is calculated. The calculated cross correlation signal is passed through a low‐pass filter. By comparison of the filtered signal with a predefined threshold, fault direction is determined. It is confirmed that the fault direction can be identified in less than 8 ms with high accuracy of nearly 99%. More than 6500 scenarios are considered for different fault conditions to evaluate the proposed method. Furthermore, using some experimental results the proposed method performance is confirmed. The simulation and experimental results show that the proposed method has high speed, good accuracy, and sensitivity. [ABSTRACT FROM AUTHOR]

In this study, a new algorithm for directional protection, based on cross correlation of Fourier transform components of voltage and current for each phase is proposed. The fault is detected using k-nearest neighbour algorithm. By comparing the fifth-nearest neighbour of each sample with an adaptive threshold, the fault is detected. The adaptive threshold is derived by a moving window with length equal to 1 ms. Using Fourier transform, the fifth sinusoidal component of the measured voltage and current for each phase is extracted. Then the cross correlation of the voltage and current components is calculated. The calculated cross correlation signal is passed through a low-pass filter. By comparison of the filtered signal with a predefined threshold, fault direction is determined. It is confirmed that the fault direction can be identified in less than 8 ms with high accuracy of nearly 99%. More than 6500 scenarios are considered for different fault conditions to evaluate the proposed method. Furthermore, using some experimental results the proposed method performance is confirmed. The simulation and experimental results show that the proposed method has high speed, good accuracy, and sensitivity. [ABSTRACT FROM AUTHOR]

Differential relays are the main protection for power transformers including transmission and industrial transformers. Protection against internal fault is generally relegated to these relays. The most probable internal fault of the industrial transformer is phase to ground type. To limit the fault current in this condition, a limiter resistor is utilised in the transformer. However, it has considerable impact on sensitivity of the differential and restricted earth fault (REF) protections. In this study, an effective approach is proposed to improve the differential and REF protections sensitivity of the industrial transformers. The proposed technique not only realises the fault current limitation function, but preserves sensitivity of the protections. In this method, a developed fault current limiter is utilised in the transformer neutral instead of the neutral grounding resistor. The performance of the proposed technique is evaluated by some simulations and experiments. The results confirm the high capability of the proposed technique in different conditions. [ABSTRACT FROM AUTHOR]

In this study main reactive power calculation methods are evaluated and compared. Furthermore, a novel simple method that can be used for calculation of reactive power in electric arc furnace (EAF) plants is proposed. The focus here is on their application in the control system of static VAr compensators (SVCs). SVC can compensate only the fundamental harmonic component. So the reactive power signal used in the control system should be sensitive only to the main harmonic component. In addition, the reactive power calculation should be fast enough that SVC can follow the quick changes of the load to mitigate the flicker efficiently. To compare various reactive power calculation methods and to evaluate the proposed method, the simulated known loads are used beside huge amount of practical data recorded in Mobarakeh Steel Company in Isfahan/Iran. Results show the proposed method has the best performance for SVC control system. [ABSTRACT FROM AUTHOR]

A simple technique is proposed in this study, aimed at the islanding detection of an inverter-based distributed generation (DG). The main advantage of the proposed technique is its reliance on a simple approach for islanding detection with negligible non-detection zone and keeping the unchanged output power quality. This work attempts to detect islanding conditions through computing the rate of change of voltage phase angle (ROCOVPA) of the point of common coupling by phase-locked loop (PLL) in inverter controllers. Because almost all inverter systems are equipped with the PLL, its implementation for inverter-interfaced DGs is simple and economical. Simulation results show that the durability energy of ROCOVPA is different for islanding and non-islanding conditions. In fact, in non-islanding condition the energy of ROCOVPA reduces to a negligible value after a certain time, but in islanding conditions it has considerable variation. The proposed method is evaluated using the conditions dictated by the IEEE 1547 and UL 1741 standards. The simulation results are presented to highlight the effectiveness of the proposed technique. In addition, the proposed method is compared with several passive and intelligent methods and it is confirmed that the proposed method has better performance and more accuracy. [ABSTRACT FROM AUTHOR]

Islanding is one of the most important concerns of the grid connected distributed resources due to personnel and equipment safety. Many approaches have been proposed for islanding detection, which can be categorized into passive and active schemes. The main concern of the passive schemes is related to their large Non Detection Zone (NDZ), while the main problem of the active methods is related to their negative impact on power quality. This paper propose an efficient and intelligent islanding detection algorithm using combination of an optimal Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO) with a simple active method. The intelligent islanding detection method based on ANN, may have mal-detection in the case of change in the power network structure. In the proposed scheme, ANN is adapted with change in power network structure to reduce NDZ. Optimal parameters of the ANN such as weight coefficients and biases are derived using the PSO in order to minimize the technique NDZ. Also the performance of the various structures of ANN such as Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Probabilistic Neural Network (PNN) in combination with PSO is compared for islanding detection purpose. The proposed method is simulated and tested in various operation conditions such as islanding conditions, motor starting, capacitor bank switching and nonlinear load switching. The test results showed that it correctly detects the islanding operation and does not mal-operate in the other situations and has a small NDZ. [ABSTRACT FROM AUTHOR]

A simple technique is proposed in this study, aimed at the islanding detection of an inverter‐based distributed generation (DG). The main advantage of the proposed technique is its reliance on a simple approach for islanding detection with negligible non‐detection zone and keeping the unchanged output power quality. This work attempts to detect islanding conditions through computing the rate of change of voltage phase angle (ROCOVPA) of the point of common coupling by phase‐locked loop (PLL) in inverter controllers. Because almost all inverter systems are equipped with the PLL, its implementation for inverter‐interfaced DGs is simple and economical. Simulation results show that the durability energy of ROCOVPA is different for islanding and non‐islanding conditions. In fact, in non‐islanding condition the energy of ROCOVPA reduces to a negligible value after a certain time, but in islanding conditions it has considerable variation. The proposed method is evaluated using the conditions dictated by the IEEE 1547 and UL 1741 standards. The simulation results are presented to highlight the effectiveness of the proposed technique. In addition, the proposed method is compared with several passive and intelligent methods and it is confirmed that the proposed method has better performance and more accuracy. [ABSTRACT FROM AUTHOR]

Electric arc furnace (EAF) is a nonlinear and time-varying load that causes power quality problems such as voltage flicker and harmonics. Field measurement of voltage and current from EAFs in Mobarakeh Steel Company in Isfahan-Iran are used to introduce a comprehensive model for EAF. A new dynamic, nonlinear and time-varying model is presented here. In other words, the function between EAF voltage and current is nonlinear, differential and with time-varying parameters. By using the recorded data, the order of the dynamic part is determined by a new approach. The nonlinear characteristic is modeled with proper polynomial functions. All parameters related to the dynamic and nonlinear functions are considered time varying as time series. Autoregressive moving average (ARMA) models are implemented and the suitable ARMA order is attained for all EAF time-varying parameters. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Existing security and reliability based EMS applications (e.g. Online Dispatcher Power Flow, Online Contingency Evaluation, Online Short Circuit Calculation, etc.) normally take real time snapshot of the system and perform necessary studies to evaluate the security and reliability of the network. As these applications do not provide any information about the security level of the system for the near future, the operator in control center may not get enough time to effectively arrange for proper corrective actions in case of security criteria violation. This paper presents an online early warning system based on steady state and dynamic security assessment which looks ahead and generate proper alarm for operators in case security criteria is expected to violate in near future. The proposed system can easily be integrated with SCADA/EMS system for transmission control room application to give operator a wide view of the entire network and help him in better control and monitoring of the power grid. [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]