Dynamic stability analysis of power grid in high proportion new energy access scenario based on deep learning

Tian, Zengyao; Shao, Yangdi; Sun, Mingze; Zhang, Qiang; Ye, Peng; Zhang, Hongpeng

doi:10.1016/j.egyr.2022.03.055

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Moreover, an improvement of transient stability was carried out, cultivating the critical clearance of three-phase fault on power system. Tian et al [33] have clarified dual-stream CNN algorithm on deep learning, take the power of every node and line as input, quickly recognize the key oscillation modes of power system, and make qualitative assessment. D. Rakesh Chandra et al [34] have postulated that the effect of DFIG on TS of grid-connected power system has been examined, to improve the TS of system.…”

Section: Recent Research Workmentioning

confidence: 99%

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

Thanigaivel

Ramesh

Karunanithi

2023

IRASE

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In this paper, a novel hybrid technique is proposed for transient stability analysis on grid connected Wind-Diesel-PV hybrid system. The proposed hybrid methodology is combination of the dwarf mongoose optimization algorithm (DMO) and the recalling enhanced recurrent neural network (RERNN) named DMO-RERNN. The main purpose of this work is to consider various elements on hybrid system for the analysis of transient stability according to different conditions. The voltage profile of hybrid system is enhanced using the proposed unified power flow controller (UPFC), which also has higher performance improving transient performance compared to the conventional ANN, PI and fuzzy-sliding mode controller. Considering the proposed technique, DMO is used to find the optimal global solution for the fault predicted by the RERNN approach. The proposed system is executed on MATLAB work platform; its performance with existing systems is analyzed. The result proves that the proposed hybrid technique based UPFC controller provides better results compared with other existing technique. The efficiency of the PI is 82.136, ANN is 77, Fuzzy Sliding Mode is 65.097% and proposed technique is 97.99038%.

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Section: Recent Research Workmentioning

confidence: 99%

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

Thanigaivel

Ramesh

Karunanithi

2023

IRASE

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“…To address this issue, data-driven methods utilizing artificial intelligence technology are being developed, which primarily aim to classify prediction models or fit stable regions in various scenarios. Decision trees (Vanfretti and Narasimham Arava, 2020), support vector (Gomez et al, 2011), deep learning (Tian et al, 2022), and artificial neural network (ANN) (Tan et al, 2019) are relevant techniques. In references (Gomez et al, 2011;Tan et al, 2019;Vanfretti and Narasimham Arava, 2020;Tian et al, 2022), classical stability analysis methods are combined with intelligent technologies to achieve intelligent stability monitoring via offline training and online testing using massive datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Decision trees (Vanfretti and Narasimham Arava, 2020), support vector (Gomez et al, 2011), deep learning (Tian et al, 2022), and artificial neural network (ANN) (Tan et al, 2019) are relevant techniques. In references (Gomez et al, 2011;Tan et al, 2019;Vanfretti and Narasimham Arava, 2020;Tian et al, 2022), classical stability analysis methods are combined with intelligent technologies to achieve intelligent stability monitoring via offline training and online testing using massive datasets. Existing research focuses mostly on the traditional power system, and the intelligent stability monitoring approaches to DC microgrids are relatively rare.…”

Section: Introductionmentioning

confidence: 99%

Voltage monitoring based on ANN-aided nonlinear stability analysis for DC microgrids

Sun

Tang²,

Hailati³

et al. 2023

Front. Energy Res.

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Due to the low inertia of the DC microgrid, the DC bus voltage is prone to drop or oscillate under disturbance. It is also challenging to supervise the stability of a DC microgrid since it is a highly nonlinear dynamic system with high dimensionality and randomness. To tackle this problem, this paper proposes a new method using ANN-aided nonlinear dynamic stability analysis for monitoring the DC bus voltage, which is combined with two steps. The first step is to establish six corresponding nonlinear accurate discrete iterative models of six switching modes of the PV-battery-load-based DC microgrid system, based on the Poincaré map theory, in order to judge the stability quantitatively with a promoted stability margin index. The second step is to use artificial neural networks (ANNs) to forecast the operating mode of the system when random changes occur in environmental circumstances and load power; this will aid the first step in being efficient and adaptable while determining stability cases. And the employed ANNs are trained with the datasets, including the circuit data, ambient temperature, irradiance, and load power, which are generated by MATLAB/Simulink simulation. Theoretical and simulation analyses are carried out under different operating conditions to validate the proposed method’s efficacy in judging the DC microgrid’s destabilizing oscillation and stable running.

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RETRACTED ARTICLE: Hierarchical classification method of electricity consumption behaviour of power users based on combination model

et al. 2023

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Dynamic stability analysis of power grid in high proportion new energy access scenario based on deep learning

Cited by 9 publications

References 5 publications

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

Voltage monitoring based on ANN-aided nonlinear stability analysis for DC microgrids

RETRACTED ARTICLE: Hierarchical classification method of electricity consumption behaviour of power users based on combination model

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