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This paper proposes a new hybrid static VAR compensator (SVC) with a series active filter (AF). The proposed hybrid SVC consists of a series AF and SVC. The series AF, which is connected in series to phase-leading capacitors in the SVC, performs for a resistor for source-side harmonic currents. A sinusoidal source current with a unity power factor is obtained with the series AF, although the thyristor-controlled reactor generates harmonic currents. A digital computer simulation was implemented to confirm the validity and high practicability of the proposed hybrid SVC using PSIM software. The simulation results demonstrate that sinusoidal source currents with a unity power factor are achieved with the proposed hybrid SVC.

Hydro-Québec's electrical transmission system is an extensive, international grid located in Québec, Canada with extensions into the northeastern United States of America. For large power systems such as this, one of the major issues is to maintain the steady-state performance of the network. From this point of view, flexible AC transmission system (FACTS) devices could be effective tools to improve power system security by reducing the power flow on overloaded lines, which in turn would result in an increased loadability of the power system, reduced transmission line losses, improved stability and security and, ultimately, a more energy-efficient transmission system. Therefore in this study, the authors will present the effects of different types of FACTS devices on the performance of Hydro-Québec's power system. The optimal locations and rating of these FACTS controllers will be determined with a view to improving network security using an optimisation algorithm based on a genetic algorithm. The effects of six different FACTS devices including static VAR compensator (SVC), thyristor-controlled series capacitor (TCSC), thyristor-controlled voltage regulator (TCVR), thyristor-controlled phase-shifting transformer (TCPST), unified power flow controller (UPFC) and static synchronous compensator (STATCOM) with energy storage are compared. Using the presented results, the effects of different types of FACTS devices on the Hydro-Québec network will be analysed and compared with those of a STATCOM equipped with energy storage from the viewpoints of static loadability and losses.

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.

An adaptive damping control scheme based on classification and regression tree (CART) using wide-area signals is proposed in this study. A family of robust controllers is designed off-line and are used in real-time after the corresponding operating point is retrieved from phasor measurement unit (PMU) data with CART interpolation. When the power system is operating precisely at a pre-selected operating point, only the corresponding controller is active. When the power system is not exactly at the operating point, a combined controller is formed for damping. A 68-bus, 16-generator system is used as a test system. A thyristor-controlled series capacitor, a static var compensator and an energy storage device are used as actuators, and remote PMUs frequencies as added inputs are employed as the example control. The simulation results demonstrate the effectiveness and robustness against the wider range of operating changes even without knowing precisely the model of disturbances that occurred in the dynamic system.

The authors propose a novel sliding-mode variable structure controller for cascade STATic var COMpensator (STATCOM), which integrates the direct feedback linearisation control for the balanced system and the individual phase instantaneous current tracking control for the unbalanced system, to achieve a versatile STATCOM controller. It can adapt automatically to various distribution system conditions. Based on the degree of system unbalance, the authors design a switching function to make the STATCOM work reliably with satisfactory performance, when the serious voltage sag occurs or the distribution system enters serious asymmetrical condition. The authors also propose a hierarchical DC-link voltages balance control strategy. The authors build a detailed ± 10 MVar STATCOM model with power system CAD and electro magnetic transient in DC system (PSCAD/EMTDC) and validate the effectiveness and advantage of the proposed control strategy with the simulation study.

Stability of a standalone hybrid power system (HPS) in a smart grid is always a challenging task. Further, the operational stability of the power system depends on the associated communication infrastructure. Therefore, it is always crucial to pick up a controller that can assure system's stability along with performance, despite disturbances like (load and input wind variations) with communication delays. Present study focuses on reactive power management and voltage stability issues of an isolated HPS. The stability aspects of HPS are improved through reactive power compensation, by custom power devices like static var compensator. The control aspects of SVC as well as the whole hybrid system are taken care by H ∞ linear matrix inequalities approach. Further, H‐infinity control, Lyapunov stability along with linear matrix inequalities techniques estimate the delay boundary of controllers. The iterative performance of the proportional–integral–derivative controllers, and robust H ∞ damping controller of the HPS, are designed through LMI approach. Later experimental study of the HPS is done, with a prototype model in dSPACE real‐time control environment. In this case, dSPACE 1104 is added for data acquisition and control. Adaptability and robustness of the proposed controllers are verified under fluctuating loads and uncertain wind power input.

Compressed-air energy storage (CAES) is considered a promising energy storage system for many grid applications, including managing renewable variability and grid capacity concerns. However, compared with conventional generation such as coal or hydro, the cost of storage power of CAES is still high, which impedes its deployment. Therefore a standing question is how to operate CAES in the most efficient and economical fashion, that is, to exploit the system functions for maximum-possible benefit. This study investigates the CAES dynamic reactive capability used to stabilise wind farms under grid fault conditions. Two considered operation modes are motor mode with leading power factor and synchronous condenser mode. Analysis with a 60-MW wind farm and two types of popular wind turbines, namely stall-regulated and doubly fed induction-generator-based WTs, shows that the CAES performance is comparable or better than that of an static var compensator in most situations investigated. Therefore the reactive-power-supply function should be considered in CAES design and operation to increase the system efficiency and value.

An adaptive damping control scheme based on the classification and regression tree (CART) using wide-area signals is proposed. A family of robust multiple-input multiple-output controllers are designed offline and used in real-time after the current operating point is retrieved from phasor measurement unit (PMU) data with CART interpolation. When the power system is operating close to a previously set operating point, only the corresponding controller is active. When the power system is not close to any previously set operating point, a combined controller is formed. A 16-machine 68-bus system is used for simulation test. Thyristor controlled series compensation, static var compensator and energy storage device are used as actuators and the remote PMUs frequencies are employed as added inputs in the example control. Simulation results demonstrate good damping performance of the proposed control method against a wide range of changes in operating state.

Wind shear and tower shadow produce a periodic pulse reduction in mechanical torque captured from wind energy resulting in wind energy conversion system (WECS) active power oscillations. In this study, an adaptive neuro-fuzzy controller for static VAR compensator, used in power networks integrated with WECS, is presented to address the torque oscillation problem. The proposed controller consists of a radial basis function neural network representing a third-order auto-regressive and moving average system model and performing the prediction, and a main controller with adaptive neuro-fuzzy inference system providing the damping signal. A modified two-area four-machine power network with WECS integration is applied to validate the proposed implementation, compared with conventional lead/lag compensation. Time-domain simulations prove that the proposed controller can provide a damping signal to improve the active power oscillation and system dynamic stability, influenced by torque oscillations under WECSs synchronised operating condition.

This study addresses the problem of designing robust stabilisation control for a large class of uncertain single-machine infinite-bus electrical power systems with static var compensator (SVC). This class of systems may be perturbed by plant uncertainties, unmodelled perturbations and external disturbances. An adaptive neural network-based dynamic feedback controller is developed such that all the states and signals of the closed-loop system are bounded and the stabilisation error can be made as small as possible. As the small perturbations in the input weighting gains are neglected, an H∞ control performance can be guaranteed. The adaptive neural network approximation systems are designed to learn the behaviours of the unknown functions, and in turn a modified procedure is proposed such that the number of the neural network basis functions can be significantly reduced. Consequently, the intelligent robust control scheme developed here possesses the properties of computational simplicity and easy implementation from the viewpoint of practical applications. The developed robust control scheme not only can handle a large class of uncertain SVC-driven power systems, but also achieve the aim of enhancing the stability performance. Finally, simulations are provided to demonstrate the effectiveness and performance of the proposed control algorithm.