Reduced-Order Aggregate Model for Large-Scale Converters With Inhomogeneous Initial Conditions in DC Microgrids

Wang, Rui; Sun, Qiuye; Tu, Pengfei; Xiao, Jianfang; Gui, Yonghao; Wang, Peng

doi:10.1109/tec.2021.3050434

Cited by 93 publications

(51 citation statements)

References 31 publications

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“…The input -output mappings error between the original system and the reduced-order system is significant when typical model order reduction procedures are used. To resolve this concern, the authors [61] offers a reduced-order aggregate model based on balanced truncation as a preparatory strategy for real-time modelling of large-scale converters in DC microgrids with inhomogeneous initial conditions. To begin, the non-leader multiagents idea is used to build a conventional linear time-invariant model with inhomogeneous initial conditions which make it simple to construct complicated system modelling using switched topology.…”

Section: Overview Of Control Aspect Of Microgridsmentioning

confidence: 99%

“…In addition, the entire system is split into two parts: an unforced component with nontrivial initial circumstances, and a forced component with null initial circumstances. An aggregated strategy is presented in [61], which includes independent reducing component responses as well as merging lowering component responses. The error in the input-output mappings is decreased as a result of this.…”

Section: Overview Of Control Aspect Of Microgridsmentioning

confidence: 99%

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A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration

et al. 2021

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This paper describes a comprehensive review of microgrid control mechanism and impact assessment for hybrid grid. Building the model of sustained energy growth is one of the actions to achieve the Sustainable Development Objective (SDO) and change the global fossil fuel system. For co-operation in the development of one independent supplier of renewable energy, the microgrid is essential. Hybrid solar energy microgrid is also a solution for reducing fossil fuel consumption and providing an environmentally sustainable solution to rising rural electricity demand. The most common renewable energy options in the microgrid are solar photovoltaics, wind turbines and biomass. The environmentally sustainable and technologically innovative installation is convenient everywhere. Hybrid microgrid-based renewable energy, however, is confronted, given its intermittent and variable source efficiency, by challenges such as voltage instability, frequency instability, charge malfunction and power quality problems. The paper thus offers a critical overview of the micro grid growth, economic analysis and control strategy.

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Section: Overview Of Control Aspect Of Microgridsmentioning

confidence: 99%

Section: Overview Of Control Aspect Of Microgridsmentioning

confidence: 99%

A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration

et al. 2021

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“…In comparison, S-transform has higher time resolution and frequency resolution, and is more suitable for analyzing nonlinear, non-stationary, and transient power quality disturbances (Wang et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

Liu

Deng-ke

et al. 2021

Front. Energy Res.

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The accurate classification of power quality disturbance (PQD) signals is of great significance for the establishment of a real-time monitoring system of modern power grids, ensuring the safe and stable operation of the power system and ensuring the electricity safety of users. Traditional power quality disturbance signal classification methods are susceptible to noise interference, feature selection, etc. In order to further improve the accuracy of power quality disturbance signal classification methods, this paper proposes a power quality disturbance classification method based on S-transform and Convolutional Neural Network (CNN). Firstly, S-transform is used to extract disturbance signals to obtain the time-frequency matrix with characteristics of the disturbance signals. As an extension of wavelet transform and Fourier transform, S-transform can avoid the disadvantages of difficult window function selection and fixed window width. At the same time, the feature extracted by S-transform has better noise immunity. Secondly, CNN is used to perform secondary feature extraction on the obtained high-dimensional time-frequency modulus matrix to reduce data dimensions and obtain the main features of the disturbance signal, then the main features extracted are classified by using the SoftMax classifier. Finally, after a series of simulation experiments, the results show that the proposed algorithm can accurately classify single disturbance signals with different signal-to-noise ratios and composite disturbance signals composed of single disturbance signals, and it also has good noise immunity. Compared with other classification methods, the algorithm proposed in this paper has better timeliness and higher accuracy, and it is an efficient and feasible power quality disturbance signal classification method.

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“…On the other hand, DC microgrids can be seen as a complex system consist of several subsystems (wind and solar energy generators). According to this point, the function model of DC microgrids can be built with the style of multi-agent systems, which was investigated in many existing results (Zhou et al, 2020) (Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Output Consensus Problem of DC Microgrids With Dynamic Event-Triggered Control Scheme

Geng

2021

Front. Energy Res.

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In this paper, the output consensus problem of DC microgrids with dynamic event-triggered control scheme is investigated. According to the properties of DC microgrids and multi-agent systems, the multi-agent systems function model for DC microgrids is provided. For making the multi-agent systems achieve output consensus, the non-periodic and periodic dynamic event-triggered control schemes are provided, respectively, which are classified according to the style of receiving information. By using a series of analysis, it can be proved that these two control schemes not only can make systems achieve output consensus, but also can avoid the Zeno-behavior successfully. Moreover, the periodic dynamic event-triggered control scheme does not need the continuous information transfer. Finally, a numerical example is provided to support our conclusions.

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Reduced-Order Aggregate Model for Large-Scale Converters With Inhomogeneous Initial Conditions in DC Microgrids

Cited by 93 publications

References 31 publications

A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration

A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration

Classification of Power Quality Disturbance Based on S-Transform and Convolution Neural Network

The Output Consensus Problem of DC Microgrids With Dynamic Event-Triggered Control Scheme

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