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As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low-voltage ride-through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF-infinite bus power system and the IEEE 39-bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low‐voltage ride‐through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF‐infinite bus power system and the IEEE 39‐bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

A multi-microgrid (MMG) system comprises of a group of self-sufficient microgrids capable of operating in grid-connected, islanded, or interconnected modes. A three-microgrid-based MMG system consisting of a solar photovoltaic system, doubly-fed induction generator-based wind generation, and static synchronous compensator as distributed energy sources are considered. Each microgrid includes a battery interfaced with a bidirectional DC–DC converter. As there are multiple battery units in the MMG system, different states-of-charge (SoC) are evident in the batteries. Hence, there is a need to charge/discharge by following the SoC of the batteries. In this study, an intelligent scheme employing fuzzy logic is applied to monitor the SoC of the batteries and determine the reference current for current control through the bidirectional DC–DC converter in an MMG system. Microgrid frequency control in the MMG system is carried out using the proposed five-membership function-based fuzzy logic control. During sudden transient events, the proposed intelligent frequency control strategy shows an improved transient response as compared to conventional PI control. A multi-input and multi-output fuzzy logic-based power management algorithm is proposed to handle multiple sources, batteries, and loads in the system. The MMG system with the proposed control is simulated using real-time digital simulator OP4510 from OPAL-RT. [ABSTRACT FROM AUTHOR]

A multi‐microgrid (MMG) system comprises of a group of self‐sufficient microgrids capable of operating in grid‐connected, islanded, or interconnected modes. A three‐microgrid‐based MMG system consisting of a solar photovoltaic system, doubly‐fed induction generator‐based wind generation, and static synchronous compensator as distributed energy sources are considered. Each microgrid includes a battery interfaced with a bidirectional DC–DC converter. As there are multiple battery units in the MMG system, different states‐of‐charge (SoC) are evident in the batteries. Hence, there is a need to charge/discharge by following the SoC of the batteries. In this study, an intelligent scheme employing fuzzy logic is applied to monitor the SoC of the batteries and determine the reference current for current control through the bidirectional DC–DC converter in an MMG system. Microgrid frequency control in the MMG system is carried out using the proposed five‐membership function‐based fuzzy logic control. During sudden transient events, the proposed intelligent frequency control strategy shows an improved transient response as compared to conventional PI control. A multi‐input and multi‐output fuzzy logic‐based power management algorithm is proposed to handle multiple sources, batteries, and loads in the system. The MMG system with the proposed control is simulated using real‐time digital simulator OP4510 from OPAL‐RT. [ABSTRACT FROM AUTHOR]

The static synchronous compensator (STATCOM) has attracted considerable attention because it can stabilise severe transients that are caused by power system disturbances. This study presents a novel interval type 2 fuzzy control system (IT2‐FCS)‐based controller for the STATCOM to stabilise bus voltages that are caused by faults or forced wind farm outages in a smart grid. Two IT2‐FCSs are presented to tune increments of the proportional integral (PI) controller, which are optimised by the gradient descent method. The IT2 fuzzy rules, involving upper and lower membership functions, result in a fast and stable system response. Since many possible scenarios may arise in the power system, the Taguchi method is used to design experiments using an orthogonal array, in which all scenarios are mutually independent. A power system that consists of a wind farm and STATCOM is studied. Comparative studies show that the proposed method is superior to traditional PI and type 1 FCS methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]