Coordinated control of three- and single-phase inverters coexisting in low-voltage microgrids

Brandão, Danilo I.; Araujo, Lucas S.; Caldognetto, Tommaso; Pomílio, José Antenor

doi:10.1016/j.apenergy.2018.07.082

Cited by 23 publications

(14 citation statements)

References 21 publications

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“…The evaluation of a CPT‐based four‐leg compensator is discussed in [5]. The coordinated control of three‐ and single‐phase inverters is suggested in [9] for a master–slave MG architecture system to assign compensation tasks among the DGs. Enhanced control with the three‐phase droop control strategy which provides good performance under both unbalanced and non‐linear loads is proposed in [58], which aims to control the circulating currents and minimise the DG synchronisation transients as well.…”

Section: Unbalance Mitigation In the Islanded Modementioning

confidence: 99%

“…Initially, studies were focused on unbalance management at the converter level. The unbalance compensation issue was initially investigated by Charles P. Steinmetz in 1917 [9], and a year later the Fortescue symmetrical component theory [10] was proposed. For linear circuits, the superposition principle provides the opportunity for individual sequence control of these components.…”

Section: Introductionmentioning

confidence: 99%

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Unbalance mitigation strategies in microgrids

Vijay

Doolla

Chandorkar

2020

IET Power Electronics

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Unbalance or asymmetry in the distribution network is a well-known power quality issue. In the modern active distribution system, with the increasing penetration of renewables, this phenomenon becomes more pronounced. In the context of microgrids (MGs), several works have been proposed for the management and mitigation of the unbalance, for both the sharing of unbalanced load and maintaining the voltage quality in the islanded mode and for the control of distributed generators in the grid-connected mode during unbalanced conditions. This study comprehensively reviews, summarises, and classifies the various strategies of the unbalance mitigation techniques for the islanded and grid-connected modes of operation for threephase MGs and presents the possible challenges and avenues for future investigations on the topic.

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Section: Unbalance Mitigation In the Islanded Modementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unbalance mitigation strategies in microgrids

Vijay

Doolla

Chandorkar

2020

IET Power Electronics

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“…Almost all previous studies are based on centralized control and, therefore, they are generally complex and need local measurement and communication between a centralized master controller and the prosumers [22]. By contrast, in a distributed control scheme, control actions of each prosumer are decided based on local measurements [23][24][25].…”

Section: Prior Studies' Historical Context To the Researchmentioning

confidence: 99%

Photovoltaic Power Converter Management in Unbalanced Low Voltage Networks with Ancillary Services Support

et al. 2019

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The proliferation of residential photovoltaic (PV) prosumers leads to detrimental impacts on the low-voltage (LV) distribution network operation such as reverse power flow, voltage fluctuations and voltage imbalances. This is due to the fact that the strategies for the PV inverters are usually designed to obtain the maximum energy from the panels. The most recent approach to these issues involves new inverter-based solutions. This paper proposes a novel comprehensive control strategy for the power electronic converters associated with PV installations to improve the operational performance of a four-wire LV distribution network. The objectives are to try to balance the currents demanded by consumers and to compensate the reactive power demanded by them at the expense of the remaining converters’ capacity. The strategy is implemented in each consumer installation, constituting a decentralized or distributed control and allowing its practical implementation based on local measurements. The algorithms were tested, in a yearly simulation horizon, on a typical Portuguese LV network to verify the impact of the high integration of the renewable energy sources in the network and the effectiveness and applicability of the proposed approach.

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“…The PVG battery link is provided by a static interface consisting of Continuous-Continuous Static Converters (CCSC). A Maximum Power Point Tracking (MPPT) command will be used to extract the optimal power of the PVG regardless of the variation in weather conditions [9]. Several optimization techniques are developed in the literature [10].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

Ndiaye

Faye

et al. 2020

International Journal of Photoenergy

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This paper presents a method of intelligent control of a photovoltaic generator (PVG) connected to a load and a battery. The system consists of charging and discharging a battery. An intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) is presented in this work. It performs two separate tasks simultaneously. First, it is used as a PVG Maximum Power Point Tracking (MPPT) command. This same algorithm is used secondly for protecting the battery against deep charges and discharges. A regulation of the DC bus voltage is also carried out by means of a PI corrector for a good supply of the load. The simulation results under MATLAB/Simulink show that the method proposed in this work allows the PV system to function normally by charging and discharging the battery whatever the weather conditions.

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Coordinated control of three- and single-phase inverters coexisting in low-voltage microgrids

Cited by 23 publications

References 21 publications

Unbalance mitigation strategies in microgrids

Unbalance mitigation strategies in microgrids

Photovoltaic Power Converter Management in Unbalanced Low Voltage Networks with Ancillary Services Support

Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

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