A novel continuous control set model predictive control to guarantee stability and robustness for buck power converter in DC microgrids

Zhou, Jiyao; Hassan, Mustafa Alrayah; Zhang, Jiaxuan; Zhang, Zehua; Wu, Shang; Hou, Mingxuan; Li, Yongjian; Li, Er‐Ping; Guerrero, Josep M.

doi:10.1016/j.egyr.2021.09.101

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Matlab/Simulink is used to build a power supply simulation model of the field power station. Because constant power load has a greater impact on stability than resistive load [15][16], this paper selects constant power load and compares the strategy in this paper with a traditional hysteresis voltage stabilization strategy. In the traditional hysteresis voltage stabilization strategy, the voltage stabilization target of the battery is 750 V. When bus voltage is lower than 730 V or higher than 760 V, operations of the discharging voltage stabilization and charging voltage stabilization are triggered respectively to achieve hysteresis voltage stabilization and avoid frequent charging and discharging of the battery.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Energy management strategy of field power station based on differential hysteresis voltage stabilization

Wang¹

2023

J. Phys.: Conf. Ser.

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The existing field power stations mainly use diesel generator sets for power supply, with poor power supply stability and no uninterrupted power supply capability. Therefore, a hybrid energy storage device is introduced into the field power station to improve it. According to the working characteristics of different energy sources, an energy management strategy based on differential hysteresis voltage stabilization is proposed. Hysteresis voltage stabilization control makes it true that the diesel generator set as the main energy source works for a long time, and the battery as a backup energy source works when necessary. Differential voltage stabilization control realizes coordination control of the two, which allows the battery to exit work smoothly. The strategy can get transient stability and stable economy, and extend the life of the battery. Simulation results show that this strategy is superior to the traditional hysteresis voltage stabilization strategy.

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Section: Simulation Experimentsmentioning

confidence: 99%

Energy management strategy of field power station based on differential hysteresis voltage stabilization

Wang¹

2023

J. Phys.: Conf. Ser.

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“…To summarize, in the case of safety-critical applications in the industry, functional safety and HIL simulation are the most significant methods for reliable production. The following text will discuss the historical overview of different HIL simulation fields through particular examples within the automotive industry [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], electric drive control [37][38][39][40], power electronics converters [41][42][43][44][45][46][47][48][49], electric grid applications [50][51][52][53][54][55][56][57][58][59], railway traction systems [60][61][62], and education [63][64][65][66]…”

Section: V-models For the Development Procedures And Functional Safetymentioning

confidence: 99%

“…The performance and effectiveness of the proposed hybrid scheme for the stability improvement and phase curve reshaping were verified through time-domain simulation results. In [56], an adaptive continuous control set model predictive control (CCS-MPC) is proposed to solve the instability problem caused by the constant power load (CPL) in a DC-DC buck converter in DC microgrids. Wide application of new efficient energy systems, DC micro-networks, appear in data centers, telecommunications power systems, and electric transport (such as EVs, ships, and aircraft).…”

Section: Grid Applications and Power Hil Simulation Examplesmentioning

confidence: 99%

Hardware-in-the-Loop Simulations: A Historical Overview of Engineering Challenges

2022

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The design of modern industrial products is further improved through the hardware-in-the-loop (HIL) simulation. Realistic simulation is enabled by the closed loop between the hardware under test (HUT) and real-time simulation. Such a system involves a field programmable gate array (FPGA) and digital signal processor (DSP). An HIL model can bypass serious damage to the real object, reduce debugging cost, and, finally, reduce the comprehensive effort during the testing. This paper provides a historical overview of HIL simulations through different engineering challenges, i.e., within automotive, power electronics systems, and different industrial drives. Various platforms, such as National Instruments, dSPACE, Typhoon HIL, or MATLAB Simulink Real-Time toolboxes and Speedgoat hardware systems, offer a powerful tool for efficient and successful investigations in different fields. Therefore, HIL simulation practice must begin already during the university’s education process to prepare the students for professional engagements in the industry, which was also verified experimentally at the end of the paper.

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“…To ensure the stability and resilience of buck power converters in DC microgrids, Zhou et al devised a unique continuous control model for predictive control. This approach works adequately in terms of load and input voltage fluctuations, settling time, and overshoot [49]. Ceron et al suggested a generalized predictive controller for wind turbines that offers microgrid frequency support.…”

Section: Figure 6a Block Diagram Of Ccs Mpcmentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

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The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified.INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems

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A novel continuous control set model predictive control to guarantee stability and robustness for buck power converter in DC microgrids

Cited by 11 publications

References 24 publications

Energy management strategy of field power station based on differential hysteresis voltage stabilization

Energy management strategy of field power station based on differential hysteresis voltage stabilization

Hardware-in-the-Loop Simulations: A Historical Overview of Engineering Challenges

Model Predictive Control Strategies in Microgrids: A Concise Revisit

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