Improving DC Microgrid Dynamic Performance Using a Fast State-Plane-Based Source-End Controller

Bianchi, Marco Andrés; Zurbriggen, Ignacio Galiano; Paz, Francisco; Ordonez, Martin

doi:10.1109/tpel.2018.2878383

Cited by 20 publications

(3 citation statements)

References 34 publications

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“…D IRECT current (DC) microgrids offer more advantages when compared to ac microgrids, e.g., higher reliability, more efficiency, less complex control, and also easier interface with electronic loads, energy storage systems, and renewable energy resources because of their natural dc behavior [1], [2]. Distributed generators are connected to the microgrids commonly by controllable converters [3].…”

Section: Introductionmentioning

confidence: 99%

Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids

Habibi

Baghaee

Blaabjerg

et al. 2022

IEEE Systems Journal

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Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc microgrids are also vulnerable to cyber-attacks. False-data injection attacks (FDIAs) are a common type of cyber-attacks, which try to inject false data into the system in order to cause the defective behavior. This article proposes a method based on model predictive control (MPC) and artificial neural networks (ANNs) to detect and mitigate the FDIA in dc microgrids that are formed by parallel dc-dc converters. The proposed MPC/ANNbased strategy shows how MPC and ANNs can be coordinated to provide a secure control layer to detect and remove the FDIAs in the dc microgrid. In the proposed strategy, an ANN plays the role of the estimator to implement in the cyber-attack detection and mitigation strategy. The proposed method is examined under different conditions, physical events and cyber disturbances (i.e. load changing and communication delay, and time-varying attack), and the results of the MPC-based scheme is compared with conventional proportional-integral controllers. The obtained results show the effectiveness of the proposed strategy to detect and mitigate the attack in dc microgrids. Index Terms-Artificial neural network (ANN), cyber-physical dc microgrid, false-data injection attack (FDIA), model predictive control (MPC). I. INTRODUCTIOND IRECT current (DC) microgrids offer more advantages when compared to ac microgrids, e.g., higher reliability, more efficiency, less complex control, and also easier interface with electronic loads, energy storage systems, and renewable energy resources because of their natural dc behavior [1], [2]. Distributed generators are connected to the microgrids commonly by controllable converters [3]. To control the dc microgrids, three control layers are implemented in a hierarchical control Manuscript

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Section: Introductionmentioning

confidence: 99%

Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids

Habibi

Baghaee

Blaabjerg

et al. 2022

IEEE Systems Journal

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“…For the instability of this cascade system, the damping factor estimation algorithm [11] and the negative impedance detection method [12] can be used to compensate for the system damping adaptively in real-time. Moreover, the fast state-plane-based source-end controller solves bus voltage oscillation caused by constant power load and improves the dynamic performance [13]. Since the constant power load is a non-linear load, various non-linear control methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Phase Shaping Method for Negative Input Admittance of Buck Converter Based on Sliding Mode Disturbance Observer

et al. 2021

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The power electronic converter that supplies power to the unmanned ship's servers can cause self-excited oscillation of the LC input filter under heavy load. To suppress voltage oscillation, an input impedance shaping method based on a sliding mode disturbance observer is presented. The self-excited oscillation mechanism of the LC filter caused by the buck converter's positive feedback is analyzed by using the small-signal model. The sliding mode disturbance observer calculates the input voltage according to the output voltage, inductance current and duty cycle of the buck converter. The feedforward compensation of the observed voltage reshapes the input admittance of the buck converter, making the system not meet the conditions of self-excited oscillation and suppressing the voltage oscillation of the LC filter. The phase shaping of input admittance is compared with the traditional amplitude shaping by the Bode diagram and Nyquist diagram. The experimental results show that the phase shaping method could better suppress the LC filter's self-excited oscillation and the sliding mode disturbance observer reduced the hardware cost.

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“…O crescente consumo de energia elétrica promove o aumento do uso de energias renováveis que são comumente utilizadas para alimentar microrredes CC, além da fácil aplicação das fontes renováveis, as microrredes CC possuem alta eficiência (Bianchi et al, 2019). Para ajustar o nível de tensão das fontes e cargas ao nível do barramento CC é necessário o uso de diversos conversores.…”

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Comparação de técnicas de controle via LMI aplicadas em microrredes CC com CPL

Carvalho

Medeiros

Ayres

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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DC microgrids have gained a lot of prominence due to their high efficiency and compatibility with renewable sources, however, these systems in the presence of constant power loads (CPL) can become unstable due to the negative impedance characteristic of CPLs. Linear Matrix Inequations (LMI) is a powerful tool used to synthesize control projects, in which the results are reliable and accurate. Thus, it was proposed to compare the methods of controlling pole allocation, Lyapunov stability, and minimizing the H 2 standard via LMI, applied to the buck converter microgrid system with CPL. The pole allocation region was defined by the intersection of a strip region and a cone region, forming a convex region. The Lyapunov stability is presented by the inequality of the Lyapunov function in sets with a convex region formed by the same region as the pole allocation method. And when minimizing the H 2 norm, a convex restriction region equivalent to that used in other methods was used. When selecting convex regions to solve the problems in LMI the solutions are obtained in a faster and more satisfactory way, different from the classic solution in which only the desired poles are selected. Resumo: As microrredes CC tem ganhado bastante destaque devido sua alta eficiência e compatibilidade com as fontes renováveis, no entanto, estes sistemas na presença de cargas de potência constante (CPL) podem torna-se instáveis devido a característica de impedância incremental negativa das CPLs. Inequações de Matrizes Lineares (LMI) são uma poderosa ferramenta utilizadas para sintetize projetos de controle, em que os resultados são confiáveis e precisos. Desta forma, foi proposta a comparação dos métodos de controle de alocação de polos, estabilidade de Lyapunov, e minimização da norma H 2 via LMI, aplicado ao sistema de microrrede do conversor buck com CPL. A região para alocação de polos foi definida pela interseção de uma região de faixa e uma região de cone, formando uma região convexa. Já a estabilidade de Lyapunov é apresentada pela inequação da função de Lyapunov em conjuntos com uma região convexa formada pela mesma região do método de alocação de polos. E ao minimizar a norma H 2 foi utilizado uma região convexa de restrição equivalente a usada nos demais métodos. Ao se selecionar regiões convexas para solucionar os problemas em LMI as soluções são obtidas de forma mais rápida e satisfatória, diferente das solução clássica em que se seleciona apenas os polos desejados.

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Improving DC Microgrid Dynamic Performance Using a Fast State-Plane-Based Source-End Controller

Cited by 20 publications

References 34 publications

Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids

Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids

Phase Shaping Method for Negative Input Admittance of Buck Converter Based on Sliding Mode Disturbance Observer

Comparação de técnicas de controle via LMI aplicadas em microrredes CC com CPL

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