NDLI: Parameter tuning for dynamic digital twins in inverter-dominated distribution grid

Content Provider	IET Digital Library
Author	Song, Xinya Jiang, Teng Schlegel, Steffen Westermann, Dirk
Abstract	This research proposed the methods based on the neural network (NN) to build the digital twins (DT) of the inverter model. The proposed methods can be divided into two groups: firstly, an online tuner for the proportional–integral (PI) controller is formulated through backpropagation (BP) algorithm; and secondly an NN-based identifier is used to approximate the non-linear functional dynamics of the targeted control loop of the inverter. The design of PI tuner is based on the deviation between the output of the model and the reference output from measurement data. Then, according to the difference, the tuner can calculate the appropriate parameter of the current and voltage controller to track the dynamic behaviour of the reference model. The NN identifier is, however, to replicate the dynamic character of the reference model by NN which is initially trained offline with extensive test data and afterwards is applied to online tuning. In order to compare the advantages of the methods by NN with the traditional ones, the system identification and parameter estimation are also utilised to build the DT of inverter model. The performance of these methods will contribute to illustrating that NN identifier is effective in the building of DT.
Starting Page	811
Ending Page	821
Page Count	11
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0163
Journal	IET Renewable Power Generation
Publisher Date	2019-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Appropriate Parameter Back Propagation Algorithm Backpropagation Control of Electric Power System Control System Analysis Control System Synthesis Current Controller Distribution Network Dynamic Behaviour Dynamic Digital Twins Inverter Model Inverter-dominated Distribution Grid Invertors Measurement Data Neural Network Neurocontrol Neurocontrollers NN-based Identifier Nonlinear Control System Nonlinear Functional Dynamics Online Tuner Parameter Estimation Parameter Tuning PI Control PI Tuner Power Distribution Control Power System Control Proportional-integral Controller Reference Output Self Adjusting Control System Synthesis Method System Identification Targeted Control Loop Tuning Voltage Controller
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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