Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

In order to support the inertia of a microgrid, virtual synchronous generator control is a suitable control method. However, the use of the virtual synchronous generator control leads to unacceptable transient active power sharing, active power oscillations, and the inverter output power oscillation in the event of a disturbance. This study aims to propose a deep neural network controller which combines the features of a restricted Boltzmann machine and a multilayer neural network. To initialize a multilayer neural network in the unsupervised pretraining method, the restricted Boltzmann machine is applied as a very important part of the deep learning controller. The Lyapunov stability method is used to update the weight of the deep neural network controller. The proposed method performs power oscillation damping and frequency stabilization. The experimental and simulation results are presented to assess the usefulness of the suggested method in damping oscillations and frequency stabilization.

This study suggests a novel deep neural network for active power oscillation damping and for improving voltage and frequency support. The suggested deep neural network controller combines the features of a restricted Boltzmann machine and a neural network. The Lyapunov stability theory is used to obtain the updating law of the deep neural network controller parameters. It is shown that the suggested deep neural network controller can efficiently damp out oscillations, support the frequency, and considerably improve the dynamic of the microgrid. The suggested DNNC can be implemented for microgrids having a higher number of DGs. In this article, a comparative study is performed between the conventional virtual synchronous generator and the proposed method in terms of power quality aspects that can facilitate a better understanding of the two favorable controller schemes for integrating renewable energy resources. The simulation and experimental results confirm the effectiveness of the suggested deep neural network controller control in damping active power oscillations and grid frequency support. Also, these results confirm that DNNC can adjust the grid frequency to within 0.01 Hz for high RES penetration. In future work, the frequency and voltage stability will be studied in a hybrid DC/AC microgrid.

مرجع:

Mohammad Reza Ghodsi, Alireza Tavakoli, Amin Samanfar, "Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG", International Transactions on Electrical Energy Systems, vol. 2022, Article ID 7539173, 17 pages, 2022. https://doi.org/10.1155/2022/7539173

https://onlinelibrary.wiley.com/doi/full/10.1155/2022/7539173