Load–frequency control in microgrids using target‐adjusted MPC

Banis, Frederik; Guericke, Daniela; Madsen, Henrik; Poulsen, Niels Kjølstad

doi:10.1049/iet-rpg.2019.0487

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…MPC is a type of controller that has gained the attention of researchers in many power engineering applications, e.g., in control of power converters [27], load frequency control [28], and stabilization of dc microgrids [29]. MPC can be implemented as a reference tracking application by calculating the proper future values of the inputs for the plant.…”

Section: Basic Concepts Of Mpcmentioning

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: Basic Concepts Of Mpcmentioning

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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“…In [17], a novel robust LFC technique using equivalent input disturbance is suggested to improve the frequency regulation of the power system. Other than the robust control methods mentioned above, Intelligent [18,19], non-linear [20], MPC-based [21][22][23][24], and Event-triggered [25] controllers are among various types of controllers for frequency regulation which are suggested in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal frequency regulation in an uncertain islanded microgrid: A modified direct search algorithm

Abolpour

Javanmardi

Dehghani

et al. 2022

IET Renewable Power Gen

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This paper presents a methodology for frequency regulation in a microgrid involving renewable energy sources (RES) using a dynamic controller, which is an output feedback controller (OFC). The parameters of OFC are tuned by searching the design space of the controller. Since the RES model is not exactly known, the uncertain model is derived and the OFC is considered for it. The goal of controller tuning is to find appropriate parameters of the controller such that the norm of frequency deviations, even in presence of uncertainties in the RES parameters is minimized. An algorithm based on searching the controller design space is suggested to find the suitable controller gains. The algorithm assumes the controller parameters lie in a convex space and searches the space systematically such that an appropriate solution is found. The method is proved mathematically and two theorems are mentioned, accordingly. Finally, a simulated model of a RES is utilized for algorithm evaluation and the results demonstrate the algorithm capability in optimal frequency regulation.

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“…However, their main disadvantage is their inability to provide constant energy. Therefore, it is important to regulate frequency against load or renewable energy fluctuations [8][9][10][11]. Frequency regulation can be performed by designing a controller for nonstorage-based devices or using storage-based controllers.…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

Load frequency fractional‐order controller design for shipboard microgrids using direct search alghorithm

Bahrampour

Dehghani

Asemani

et al. 2022

IET Renewable Power Gen

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Load frequency control (LFC) is one of the controversial topics in power systems, especially in shipboard microgrids (SMG). In such applications, renewable energies such as wind, solar, or wave energies provide the power supply. However, changes in the weather conditions or loads can disrupt the frequency. To have a constant frequency in the presence of such turbulence in SMG, a fractional‐order dynamic output feedback controller (FDOFC) is proposed. As a result, the closed‐loop system is extended to a non‐identical fractional‐order system in the state space model. First, the non‐identical fractional‐order system is transformed into an identical one using the least common multiple techniques to determine control parameters. Subsequently, using a direct search algorithm, a considered initial design space is divided into smaller totally stable (TS) sub‐spaces. Then, among all TS controller parameters, the H∞$H_{\infty }$ controller is designed to reduce the effects of disturbances. The study is carried out on a practical SMG system to show the practicality of the proposed FDOFC. Finally, to evaluate the proposed method's efficiency, the SMG results are compared with the results obtained with μ synthesis, H∞$H_{\infty }$, Bilinear Matrix Inequality (BMI)‐based, and fuzzy fractional optimization methods. Compared to the state‐of‐the‐art approaches, more promising results are obtained for H∞$H_{\infty }$‐norm of the closed‐loop system about ten times smaller, that is, false|false|normalΔffalse|false|∞=0.00026$||\Delta f||_\infty = 0.00026$ which is such a negligible norm that concludes perfect disturbance rejection. Moreover, in more scenarios, the robustness of the designed framework is evaluated under parameter variations and time delays in communication. The results show that the maximum frequency deviation is 2.5×10−3$2.5 \times 10^{-3}$, which is very small. Therefore, the controller also works impressively under deviations of SMG system parameters, and communication time delays.

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Load–frequency control in microgrids using target‐adjusted MPC

Cited by 16 publications

References 39 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

Optimal frequency regulation in an uncertain islanded microgrid: A modified direct search algorithm

Load frequency fractional‐order controller design for shipboard microgrids using direct search alghorithm

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