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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]

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]

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]

The time varying nature of electric arc furnace (EAF) gives rise to voltage fluctuations, which produces the effect known as flicker. Employing reactive power compensation devices such as static VAr compensator (SVC) is one of the main approaches to mitigate this phenomenon. By utilising prediction methods to forecast EAFs reactive power consumption for a half‐cycle ahead, performance of SVC can be enhanced substantially. This study proposes a rolling Grey model and a Grey–Markov method to predict the actual reactive power of Mobarakeh Steel Company, Isfahan/Iran. To investigate the efficiency of the proposed methods the results are compared with the results of EAFs reactive power compensation when no prediction method is employed. Furthermore, autoregressive moving average (ARMA) models with updating coefficients, which are studied in the literature are used to predict EAF reactive power. Various methods for updating ARMA coefficients including normalised least mean square, recursive least square method and an online genetic algorithm are used. By comparing the indices which are defined using the concept of flicker frequency and power spectral density, the superiority of Grey–Markov and rolling Grey model over the aforementioned prediction methods is investigated. [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]

• 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]