In this paper, a fully distributed hierarchical control strategy is proposed for operating networked gridsupporting inverters (GSIs) in islanded ac microgrids (MGs). The primary control level implements frequency and voltage control of an ac MG through a cascaded structure, consisting of a droop control loop, a virtual impedance control loop, a mixed H ₂ /H _∞ -based voltage control loop, and a sliding-mode-control-based current loop. Compared to conventional proportional-plus-integral-based cascaded control, the proposed cascaded control does not require a precise model for the GSI system. The proposed secondary control level implements distributed-consensus-based economic automatic generation control and distributed automatic voltage control, which integrates the conventional secondary control and tertiary control into a single control level by bridging a gap between traditional secondary control and tertiary control. Simulation results demonstrate the effectiveness of the proposed hierarchical control strategy.

This paper proposes a novel artificial neural network (ANN) based control method for a dc/dc buck converter. The ANN is trained to implement optimal control based on approximate dynamic programming (ADP). Special characteristics of the proposed ANN control include: 1) The inputs to the ANN contain error signals and integrals of the error signals, enabling the ANN to have PI control ability; 2) The ANN receives voltage feedback signals from the dc/dc converter, making the combined system equivalent to a recurrent neural network; 3) The ANN is trained to minimize a cost function over a long time horizon, making the ANN have a stronger predictive control ability than a conventional predictive controller; 4) The ANN is trained offline, preventing the instability of the network caused by weight adjustments of an on-line training algorithm. The ANN performance is evaluated through simulation and hardware experiments and compared with conventional control methods, which shows that the ANN controller has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly.

A Hybrid AC/DC microgrid (MG) can integrate distributed generation sources and distributed loads on the AC and DC side of the MG by eliminating many unnecessary power conversion devices, which is more flexible and efficient. However, to achieve reliable and economic operation of a hybrid AC/DC MG is challenging due to its complex structure. In this paper, a novel distributed event-triggered hierarchical control strategy is proposed to improve the economic operation of a hybrid AC/DC MG. For the primary control, distributed local controls of AC DGs, DC DGs, and interlinking converters (ICs) are realized by adopting the droop control method. For the secondary control, the distributed economic dispatch, distributed average bus voltage discovery, and distributed proportional power-sharing algorithms are first proposed; then, control objectives of voltage and frequency restoration and economic operation of the hybrid AC/DC MG are realized based upon the developed algorithms. Furthermore, the distributed secondary control is built upon an event-triggered mechanism developed in this paper, which can reduce the communication burden. The simulation results demonstrate the effectiveness of the proposed control strategy.

With the increasing integration of PV generation in AC microgrids, it is challenging to keep the stability of system frequency due to the intermittent and stochastic nature of PVs. Thus, in order to reduce the investment and maintenance costs of storage systems, the electric utility has shown increasing interest in calling on PVs to provide frequency regulation services. In this paper, a novel sliding mode control (SMC) based adaptive power point tracking (APPT) control strategy is proposed to provide bi-directional primary frequency regulation (BPFR) of an AC microgrid. In this strategy, in order to restrain the variation of frequency, the sliding mode surface is adaptively regulated to provide an adaptive power reserve. Thus, the PVs will have a power headroom to regulate the frequency both up and down through releasing the reserved power or increasing the reserves of the PV systems. Furthermore, the sliding mode surface is adaptively regulated only based on the locally measured frequency of the microgrid without the requirements of communication, detailed PV model, irradiance sensors as well as MPP estimators. Thus, the proposed control strategy shows the advantages of easy to implement and reduces the investment and maintenance costs of installing storage systems or irradiance sensors.