Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks

Zuo, Shan; Altun, Tuncay; Lewis, Frank L.; Davoudi, Ali

doi:10.1109/tsg.2020.2992118

Cited by 76 publications

(45 citation statements)

References 40 publications

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“…In (7), y is the output of the ANN and wo,j is the weight factor between the j th neuron of the hidden layer and the neuron of the output layer. Also, b0 is the bias factor of the neuron in the output layer and fo(.)…”

Section: Proposed Sensorless Application Based On Artificial Neural Networkmentioning

confidence: 99%

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Sensorless Control of DC Microgrid Based on Artificial Intelligence

Akpolat

Habibi

Dursun

et al. 2021

IEEE Trans. Energy Convers.

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Abstract--Nowadays, DC microgrids are preferred in the field of renewable energy. The autonomous DC microgrids aim to provide smooth power flow from renewables to loads. While satisfying certain load profiles and sustaining the power as the desired level, the control of power converters is considerable. To ascend the resilience of DC microgrids, battery storage systems (BSSs) are also used as a backup unit for supplying uninterrupted power. The main task of BSSs is to compensate for the lack of power when the load is higher than supplied power or store the surplus of power in case that the load demand is less than the extracted power. In other words, by draining and storing the power, BSSs help to increase the flexibility of the system and keep the main DC bus voltage within acceptable bounds. This study introduces artificial intelligence (AI)-based method to diminish the number of implemented sensors and control power converters without reducing efficiency. In this paper, artificial neural networks (ANNs) as a subset of AI are exploited. Diminishing the number of sensors in the control layer makes the system more reliable. To validate the effectiveness of the proposed system, offline and online time-domain simulations are performed in MATLAB/Simulink.

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Section: Proposed Sensorless Application Based On Artificial Neural Networkmentioning

confidence: 99%

“…The main benefit of a DC microgrid is that the system can perform easily with DC types of DERs [6]. So, encountered problems in a DC microgrid can be coped with easier than an AC microgrid [7].…”

mentioning

confidence: 99%

Sensorless Control of DC Microgrid Based on Artificial Intelligence

Akpolat

Habibi

Dursun

et al. 2021

IEEE Trans. Energy Convers.

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“…The secondary control provides the active and reactive power references to the primary control. The primary control consists of the droop, the power control, and the current controls (Lou et al, 2019;Xu et al, 2019;Zuo et al, 2020). The power calculation is determined by Eq.…”

Section: Der Control and Stability Analysismentioning

confidence: 99%

Decentralized Fast Delayed Signal Cancelation Secondary Control for Low Voltage Ride-Through Application in Grid Supporting Grid Feeding Microgrid

2021

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In this study, a distributed secondary control is proposed alongside the conventional primary control to form a hierarchical control scheme for the Low Voltage Ride-Through (LVRT) control and applications in the inverter-based microgrid. The secondary control utilizes a fast Delayed Signal Cancelation (DSC) algorithm for the secondary control loop to control the reactive and active power reference by controlling the sequences generated. The microgrid consists of four Distributed Energy Resources (DER) sources interfaced to the grid through interfacing inverters coordinated by droop for effective power-sharing according to capacities. The droop also allows for grid supporting application for microgrid’s participation in frequency and voltage regulation in the main grid. The proposed decentralized fast DSC performance is evaluated with centralized secondary and traditional primary control using OPAL-RT Lab computation and MATLAB/SIMULINK graphical user interface for offline simulations and real-time digital simulator verification. This study presents and discusses the results.

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“…The secondary control layer (SCL) is utilized to overcome this drawback and compensate for f & V deviations result from the droop control method. The SCL can be implemented in three manners: centralized [10][11][12], decentralized [13,14], and distributed [15][16][17][18][19][20][21]. The centralized control method employs a central control module and a communication grid between each DG and the central controller.…”

mentioning

confidence: 99%

“…The decentralized control strategy is not usually employed in the secondary control due to the lack of sufficient information to coordinate and harmonize all available MGs resources [13,14]. To overcome the centralized and decentralized controls problems, inspired by the idea of multi-agent systems, distributed control methods using sparse communication networks have been recently presented [15][16][17][18][19][20][21]. In the distributed control scheme, each agent only communicates with the neighboring agents employing a sparse communication grid.…”

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confidence: 99%

A New Consensus-based Distributed Adaptive Control for Islanded Microgrids

Keshavarz

Doroudi

Kazemi

et al. 2021

IJE

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This paper proposes a novel distributed adaptive secondary controller for microgrids (MGs) in islanded operation. To enhance the dynamic behaviour of a microgrid considering uncertainties and disturbances, the proposed controller uses a consensus-based adaptive control structure. A novel consensus protocol is proposed to restore the frequency and voltage (f &V) of a microgrid to their rated values. A Lyapunov function is presented to assure the asymptotic stability of the controller and the ultimate boundedness of the global neighborhood consensus error. The nonlinear nature of MGs has been also considered in the algorithm. Unlike other methods in this field that require complete information of distributed generators (DGs), the proposed controller requires only power droop coefficients and is independent of DGs parameters. Different simulations are conducted in MATLAB/SimPower Toolbox on a typical microgrid and under various disturbances to judge the performance of the adaptive controller. The simulation results show the effect of the proposed controller on increasing the resilience of an MG.

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Distributed Resilient Secondary Control of DC Microgrids Against Unbounded Attacks

Cited by 76 publications

References 40 publications

Sensorless Control of DC Microgrid Based on Artificial Intelligence

Sensorless Control of DC Microgrid Based on Artificial Intelligence

Decentralized Fast Delayed Signal Cancelation Secondary Control for Low Voltage Ride-Through Application in Grid Supporting Grid Feeding Microgrid

A New Consensus-based Distributed Adaptive Control for Islanded Microgrids

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