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One of the major challenges in grid-connected wind farms is the flicker emission caused by the reactive power extremely short time variations. A general used solution is installing a static volt-ampere reactive (VAR) compensator (SVC) as the most cost-saving device, which still holds the risk of incomplete reactive power compensation subject to the operating delay time. To this end, an accurate extreme short-term reactive power forecasting structure is essential. Although there are many studies related to the forecasting of wind power in long, medium, and short terms, however, the extremely short-term reactive power prediction of the wind farms is only addressed in few studies. The main aim in this article is to develop a method based on the convolutional neural network (CNN) to directly learn nonstationary and complex features from the raw wind farm reactive power time series and to contribute a predictive controller to mitigate the voltage flicker through an SVC connected to the wind farm. However, high sensitivity of typical mean-squared-based loss functions might lead to considerable errors in the forecasting. To resolve this issue, a modified loss function is proposed to enhance the performance and, consequently, an improved CNN is presented. The evaluation of the proposed method is based on the actual recorded data of a wind farm in Manjil, Iran. The data are directly used in the modeling of the wind farm. A current source, which is based on the actual records its amplitude and phase angle change every 0.01 s, is utilized to model the wind farm. The numerical results in terms of flicker sensation and short-term flicker perceptibility (Pst) measurement are used to verify the performance of the proposed method through comparison with the wind farm performance without SVC and SVC with a common control system. [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 study tries to outline a comprehensive approach established on the kinetic energy of the distributed generations (DGs) for islanding mode detection in micro-grids. More specifically, the proposed index calculates the rate of change of kinetic energy over reactive power (ROCOKORP) for each DG at the point of common coupling (PCC). Despite the islanding detection being the most challenging issue during complete match between the load and generation of DGs, the proposed method can swiftly deal with such an issue, therefore confirming its superiority. Other challenging conditions where islanding detection can either be difficult or confused with other events such as loads inside the non-detection zone (NDZ), various fault types, and loads at various power factors have also been considered to investigate the effectivity of the proposed approach. The proposed method’s performance has been verified and evaluated in comparison to the state-of-the-art via a simulation testbed. [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]

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]

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]

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]

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]

In electrical arc furnaces (EAFs), an unstable arc leads to unfavorable operation of the EAF, which can be rectified by using series reactors. Installation of the series inductor affects some electrical parameters of the EAF, such as arc stability, power factor, arc length, and the EAF efficiency. Furthermore, it reduces the effective transferred power to the EAF. To achieve desired transferred power after installation of the series reactors, the secondary voltage of the EAF transformer should be increased. However, after any tap changing of the EAF transformer, arc stability and other relevant parameters are affected. So the impact on the EAF electrical magnitudes caused by changes in the series reactor and in the EAF transformer tap should be simultaneously studied. This research deals with a new method for optimal setting of the series reactor and EAF transformer tap to achieve the EAF best performance. Using a nonlinear model for the EAF, its electrical characteristics, at nominal transformer current and for all of the possible transformer and reactor taps, are calculated and the results are tabulated. Using these tables, the optimal setpoint for transformer and series reactor taps is determined in different conditions. Finally, the method is investigated from an economic point of view. [ABSTRACT FROM AUTHOR]

There are several definitions for reactive power in non sinusoidal networks. However, there is still no unique definition that is accepted by the electrical communities. In this study seven of the most common power decompositions, are simulated and compared in AC electric arc furnace (EAF) plants. For this purpose a large amount of data was collected for a considerable number of melting processes, including currents and voltages of eight ac EAFs installed in Mobarakeh steel company (MSC), Isfahan, Iran. The reactive power definitions are compared to each other utilizing different methods and some statistic indices. By calculating the average of each definition over all recorded data the definitions that are beneficial to customers and those that are beneficial to the utilities are obtained. The problem encountered here is that the ideal definition for reactive power is unknown and there is no criterion to evaluate and compare the different definitions. So here the mean of the seven most common definitions is considered as the criterion and the distances of various definitions from it are calculated. Also, the capacitance that completely compensates the EAF reactive power is calculated according to each of the discussed definitions. [ABSTRACT FROM AUTHOR]

One of the prominent problems in wind farms is voltage flicker emission. To prevent flicker emission or mitigate the impact as best as possible, a static VAr compensator (SVC) is a great candidate both economically and technically. However, SVCs cannot completely compensate the fast-changing reactive power due to delays caused by the reactive power calculation unit and the triggering fire angle of the SVC. This paper proposes a predictive control system for SVCs, by merging an additional predictive control block into the conventional control system. It is constructed based on deep neural networks, namely adaptive one-dimensional convolutional neural network (1D-CNN). The training process is conducted based on the adaptive learning weights process to enhance the prediction accuracy and training computational complexity of the 1D-CNN. Numerical results on the actual dataset in a wind farm in Manjil, Iran, have verified the forecasting accuracy and flicker mitigation of the proposed controller. [ABSTRACT FROM AUTHOR]

Under frequency load shedding (UFLS) scheme has been widely implemented as a safety net to prevent system collapse following major disturbances which results in large mismatch between load and generation. However, voltage related issues post activation of UFLS stages are also vital concerns in preserving system stability and should be properly analysed. Fixed and switched shunt capacitors that are in service during normal operation for maintaining system voltage and dynamic MVAR reserve can generate surplus reactive power post operation of UFLS relays which may result in over voltage issues, generator under excitation and some undesirable conditions such as transformer saturation, ferro‐resonance etc. This study addresses the need for coordinated under frequency load and capacitor shedding and its implementation approach to effectively preserve system stability following small and large disturbances. To confirm the feasibility of the approach, the proposed method has been used to design coordinated UFLS and under frequency capacitor shedding (UFCS) schemes for a real and actual power network. In addition, the proposed coordinated UFLS and UFCS scheme has been combined with automatic switching of shunt reactors to optimise the performance of the scheme. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of electric arc furnace (EAF) reactive power is an appropriate solution to compensate for static VAr compensator delay and improve its performance in flicker reduction. A linear autoregressive moving average (ARMA) can only pull out the linear deterministic (LD) component of EAF reactive power time series. For the prediction to be made through both nonlinear deterministic (ND) and LD components, employing nonlinear models is necessary. However, before developing the nonlinear models for prediction, the necessity of the employing them should be verified by investigating the significance of the ND components in the process. This paper presents a novel approach for wide analysis of nonlinear behavior of EAFs reactive power time series related to eight ac EAFs installed in Mobarakeh steel industry, Isfahan, Iran to answer the question about the importance of their ND components. In the approach, a suitable linear auto regressive moving average (ARMA) model with order (4,4) is used for the time series to extract the residual time series. Then, a number of well established nonlinear analysis techniques such as time delay reconstruction, surrogate data, delay vector variance and recurrence plot methods are applied to the original and residual time series. To describe the nonlinear characteristics of the time series, some new indices are defined. They quantify the significance of the ND component in compare with LD component. [Copyright &y& Elsevier]

Abstract: The time varying nature of electric arc furnace (EAF) gives rise to voltage fluctuations which produce the effect known as flicker. The ability of static VAr compensator (SVC) in flicker reduction is limited by delays in thyristor ignition. To improve SVC performance in flicker compensation, EAF reactive power can be predicted for a half-cycle ahead, by using appropriate autoregressive moving average (ARMA) models. This paper uses huge field data collected from ac arc furnaces, and demonstrates that the EAF reactive power models coefficients are different from one data record to another and do not follow any specific law. Therefore, it is necessary to update the model coefficients for prediction purposes. For this purpose, two major adaptation algorithms, the least mean square (LMS) and recursive least square (RLS) are used to determine online the prediction relationship coefficients. By applying the methods to the data records and using some indices such as newly defined indices based on concepts of flicker frequencies and power spectral density, the transient and steady state performances of the methods are studied in EAF reactive power prediction. A simulation example on the application of the predictive models in a SVC control system is presented. [Copyright &y& Elsevier]

• Yet there are few studies for the extremely short-term forecasting of wind power. • Since the proposed approach is nonlinear, it provides more accurate modelling than the ARMA approach. • The proposed approach only uses the EKF algorithm to update the parameters which makes it suitable for online implantation. • It is robust against noisy easements and probabilistic behavior of the wind speed profile. • A proper model for the wind farm to simulate and evaluate the performance of the SVC based on real data is proposed. The inherent time-varying nature of the wind farm power causes undesired voltage flicker in the power network. In order to mitigate the flicker to enhance the performance of the wind power system with very fast dynamics, the static VAr compensator (SVC) is utilized. However, the SVC operates with some delays which negatively affects its performance. This persuades us to predict the reactive power of the wind farm to compensate for the real-world delay. The predicted reactive power is then utilized in the SVC. Therefore, this paper develops a novel fuzzy one-step-ahead prediction approach for the wind farm reactive power. The proposed fuzzy prediction uses a Takagi-Sugeno (TS) fuzzy representation whose unknown parameters are tuned online based on an extended Kalman filter (EKF). The wind farm is modeled as a time-varying current source which its amplitude and phase change every 0.01 s. A large set of the actual data of a wind farm in Manjil, Iran is gathered and directly utilized in the simulation process. Several flicker indices are calculated to evaluate the proposed prediction method. The obtained results show the performance enhancement and flicker mitigation of the suggested power scheme. [ABSTRACT FROM AUTHOR]

Identification of flicker sources is necessary to find who is responsible for the measured flicker and improve power quality. This paper puts forward a new method for identifying flicker sources with minimum measurement units. Contrary to the previous works where flicker sources are considered a single-frequency signal, the autoregressive moving average (ARMA) is used to model active and reactive power variations. First, the envelope of the network voltage at the considered busbars is derived by the Hilbert transform. Then, appropriate flicker indices are extracted from the power spectral density (PSD) of the voltage envelope. A novel two-level structure of a set of ANNs is proposed, which needs a low number of voltage measurement units to locate the flicker sources. Using the captured data from different simulations of various scenarios, the Artificial Neural Networks (ANNs) are trained to categorize flicker sources. [ABSTRACT FROM AUTHOR]

The ability of static VAr compensators (SVC) to reduce the flicker caused by electric arc furnaces and other variable VAr loads depends on their speed, which is limited by delays in reactive power measurement and thyristor ignition. In this paper, a predictive method is proposed to compensate the delay time and, hence, to improve the SVC performance. Previous samples of the load reactive power are used to predict its future values. Predicted VAr is then applied as a reference for reactive power compensation. [ABSTRACT FROM AUTHOR]

Summary: Delays in reactive power measurement and thyristor ignition limit SVC performance in flicker mitigation of electric arc furnaces (EAFs). To overcome this limitation, prediction methods can be employed to forecast the EAF reactive power for half cycle ahead, used as a reference signal of the SVC. The utilized prediction methods in this area can be divided into linear and black‐box approaches. However, the linear approaches cannot extract the nonlinear governed relations, and using a black‐box model is not efficient for linear relations. A Gray‐ANN method is proposed here to take the advantages of the two mentioned approaches. Results from indices based on actual records of Mobarakeh Steel Company confirm superiorities of the proposed method over previously utilized prediction methods in this application. Furthermore, SVC's flicker mitigation ability is evaluated using the actual data. The results confirm the significant reduction of flicker compared with the regular system. [ABSTRACT FROM AUTHOR]