Data-Driven Modeling of Microgrid Transient Dynamics Through Modularized Sparse Identification

Nandakumar, Apoorva; Li, Yan; Zheng, Honghao; Zhao, Junhui; Zhao, Dongbo; Zhang, Yichen; Hong, Tianqi; Chen, Bo

doi:10.1109/tste.2023.3273127

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…In Ref. [13], data-driven modeling of the nonlinear transient dynamics of microgrid systems is presented. On this basis, a control synthesis was performed, and the proposed method can simultaneously calculate the control equations of different subsystems and is verified as robust to measure noises and disturbances.…”

Section: Short Term Long Termmentioning

confidence: 99%

Evaluation of Voltage Stability in Microgrid-Tied Photovoltaic Systems

2023

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These days, with the significant increase in the use of renewable energy sources as additional energy sources connected to the distribution network, many challenges and difficulties arise in ensuring sustainability and reliability. The generation, transmission and distribution, in the current state of the electricity system, are facing quite dynamic changes. They are the result of the liberalization of the energy market, the increased use of renewable energy sources such as photovoltaic systems, wind turbines and the charging stations for hybrid and electric vehicles. The most important factors are related to the balancing of the energy system, the analysis of voltage stability, overcoming the consequences of the increase in short-circuit currents, increasing the transmission capacities of the system forming and distribution networks, as well as the accurate forecasting of the development of loads and consumption over the coming years. This article presents an analysis of the voltage stability in a smart microgrid for two different scenarios. The studied cases describe a linear low-voltage p-type microgrid with loads connected to it at different nodes. Data on the type and cross-section of the conductors of the studied power line are presented. Simulation studies were carried out to determine the limits of grid voltage stability when connecting photovoltaic plants with a set power. The simulation results are commented on and an analysis of the optimal operating mode of the system is realized. The model studies were implemented in the NEPLAN program environment. The research carried out allows an evaluation of the permissible limits for network stability when connecting photovoltaic plants. Through this evaluation, it can be determined how many and at which node the loads should be connected without causing an imbalance in the network. This is useful from the point of view of ensuring the sustainability and reliability of electrical energy in a microgrid.

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Section: Short Term Long Termmentioning

confidence: 99%

Evaluation of Voltage Stability in Microgrid-Tied Photovoltaic Systems

2023

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Online stability assessment for isolated microgrid via LASSO based neural network algorithm

Lasheen,

Sindi,

Zeineldin

et al. 2024

Energy Conversion and Management: X

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Fast stability enhancement of inverter‐based microgrids using NGO‐LSTM algorithm

Pang,

Tang

2024

IET Renewable Power Gen

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To improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG.

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Data-Driven Modeling of Microgrid Transient Dynamics Through Modularized Sparse Identification

Cited by 8 publications

References 28 publications

Evaluation of Voltage Stability in Microgrid-Tied Photovoltaic Systems

Evaluation of Voltage Stability in Microgrid-Tied Photovoltaic Systems

Online stability assessment for isolated microgrid via LASSO based neural network algorithm

Fast stability enhancement of inverter‐based microgrids using NGO‐LSTM algorithm

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