A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators for lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.

An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method.

A droop controlled microgrid with distribution static compensator (DSTATCOM) is developed to improve the power quality in this study. Due to the reactive power/voltage Q-V droop characteristic and the existence of the unbalanced, linear inductive and nonlinear loads, the power quality problems, including the voltage drop, unbalanced currents, lagging power factor (PF) and current harmonics, are very serious in the islanded microgrid. Moreover, owing to the instantaneous power following into or out of the DC-link capacitor of the DSTATCOM under load variation, the performance of the DSTATCOM for power quality improvement is seriously degenerated. Hence, to effectively improve the power quality of the droop controlled microgrid and the transient response of the DC-link voltage of the DSTATCOM under load variation, an online trained polynomial petri fuzzy neural network (PPFNN) controller is firstly proposed as the DC-link voltage controller to supersede the conventional proportional-integral (PI) controller in the DSTATCOM. The network structure and the online learning strategy of the proposed PPFNN are detailedly derived. Finally, the effectiveness of the DSTATCOM using the proposed PPFNN controller to improve the unbalanced currents, the total harmonic distortion (THD) reduction of the current and to compensate the reactive power for the voltage support and PF correction in the droop controlled microgrid is certified.

A Petri recurrent wavelet fuzzy neural network (PetriRWFNN) controller and a simple presynchronization estimation are proposed for the operations of seamless switching and grid reconnection in a microgrid system. The microgrid using master/slave control consists of a storage system, photovoltaic (PV) system and loads, and can be operated in either grid-connected mode or islanded mode. Since the different control algorithm is adopted in the master distributed generator at different operation modes, the transient deterioration in voltage and active power output of the microgrid system is obvious during the mode switching. Moreover, when the microgrid is operated in islanded mode and the power grid returns to normal operation, the microgrid cannot directly reconnect with the power grid to avoid a large inrush of current and failed grid reconnection due to the asynchronous angle between the islanded microgrid and the grid angle. Therefore, a novel PetriRWFNN controller is proposed to improve the transient responses of the voltage and active power of the microgrid during mode switching. The network structure and the online learning algorithm of the proposed PetriRWFNN are introduced in detail. Furthermore, a simple and fast presynchronization estimation for grid reconnection during the switching from the islanded mode to the grid-connected mode is also proposed in this article. Finally, some experimental results are provided to certify the effectiveness and feasibility of the microgrid system using the proposed PetriRWFNN controller and presynchronization estimation for the operations of seamless switching and grid reconnection.

An online trained intelligent voltage controller is developed in this article to improve the transient responses of the voltage and frequency of an islanded microgrid with droop control and achieve fast load shedding. Although the droop control algorithm has been extensively adopted in microgrid, some shortcoming of the droop control, such as poor disturbance rejection and slow transient response, seriously affect the power quality and stability in islanded microgrid. Moreover, when the power consumption of the demand exceeds the generated power of the distributed generators in microgrid, the decreased frequency will lead to the microgrid blackout owing to the slow load shedding characteristics of the droop control. Hence, in this article, an online trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is developed as the voltage controller to replace the traditional proportional--integral controller in a battery energy storage system for an islanded microgrid with droop control to achieve fast load shedding. The network structure and the online learning algorithm of the proposed PPWFNN are detailedly introduced. Finally, some experimental results are provided to validate the effectiveness of the microgrid using the proposed PPWFNN controller for the improvements of the power sharing and load shedding.