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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]

In this study, a ring‐type multi‐microgrid (MG) system comprising of three MGs with solar energy‐based generation, wind energy‐based generation, and static synchronous compensator as primary sources along with batteries as emergency back‐up is proposed. The voltage‐source converters at the individual MG level serve as the interface between the neighbouring MGs and the grid. An augmented proportional sliding‐mode‐control‐based approach is proposed in the primary control level to improve the power quality of the system during balanced and unbalanced loading conditions. A negative‐sequence controller is also included to mitigate the negative‐sequence voltage from the inverter output voltage. The multi‐MG (MMG) system is capable of operation in grid‐connected, islanded, or interconnected mode with balanced and unbalanced loads. The bidirectional DC ‐DC converters interfacing the batteries at the DC link is controlled by a hysteresis‐function‐based sliding‐mode control. A comparison of the proposed inverter primary control with proportional ‐integral controller, quasi‐proportional ‐resonant controller, and hysteresis‐band current controller is carried out. The comparison reveals that the total harmonic distortion and voltage unbalance factor improves by employing the proposed control when compared with conventional control. The proposed MMG system with its control strategies is modelled in MATLAB/Simulink environment and is experimentally verified using 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]