When Digital Twin Meets Network Softwarization in the Industrial IoT: Real-Time Requirements Case Study

The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System.

Research on New Rail Transit Power Supply System Scheme

The tourism rail transit project has broad development prospects in China. The new type of rail transit represented by trams and monorails is suitable for the demand of the project of the rail transit, and the traction power supply system is great significance for the normal operation of the new type rail transit. This paper introduces the external power supply, medium-voltage loop network and traction power supply of new type of rail transit project. Through the comparison of technical schemes, the selection for several important engineering schemes in the new rail transit power supply system engineering design are obtained.

Simulasi Modifikasi Konfigurasi Jaringan Spindel Menjadi Loop Tertutup Untuk Meningkatkan Keandalan Sistem Distribusi Menggunakan Software ETAP

Distribution reliability network rely on to any factor such as material quality, maintenance, operational pattern, protection device and also network configuration. In the spindle network the level of network reliability is level 3 (SPLN 52-3, 1983: 5). In order to leveling up the network reliability from level 3 to level 5 (zero down time)[2][3] we need to modify the protection system from overcurrent relay and ground fault relay to line differential relay in each distribution substation. Beside that Load Break Switch in each customer cubicle substation and in the connection substation should replaced by circuit breaker. Spindle network which operated open loop in the connection substation switch to normally close operated, so it can be called as closed loop network. This modification purpose is ther is no down time in case off ground fault or phase to phase sort circuit on the network cable. Before this kind of modification and the setting applied into real network, we make a simulation using an application called ETAP and no missmatch trip from 7 time experiment consist of ground fault and phase to phase short circuit in 7 cable

Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning

Because deep learning networks can 'learn' the complex mapping function between the labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we propose a closed-loop fully convolutional residual network (FCRN) combined with transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pre-training on the Marmousi2 model and fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. The proposed work flow can be used for velocity, density, and impedance inversion from post-stack seismic data. This paper takes velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that the closed-loop FCRN with transfer learning is superior than the open-loop FCRN with better lateral continuity and velocity details. The closed-loop FCRN can effectively predict the velocity with high accuracy on the synthetic data, has good anti-noise performance, and also can be effectively used for the field data with spatial heterogeneity.

Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

Aiming at the problem of insufficient accuracy and timeliness of transmission line parameters in the grid energy management system (EMS) parameter library, a dynamic optimization method of transmission line parameters based on grey support vector regression is proposed. Firstly, the influence of operating conditions and meteorological factors on the changes of parameters is analyzed. Based on this, the correlation quantification method of transmission line parameters is designed based on Pearson coefficient, and the influence coefficient value is obtained. Then, with the influence coefficient as the constraint condition, a method for selecting strong influence characteristics of line parameters based on improved Elastic Net is proposed. Finally, based on the grey prediction theory, a grey support vector regression (GM-SVR) parameter optimization model is constructed to realize the dynamic optimization of line parameter values under the power grid operation state. The effectiveness and feasibility of the proposed method is verified through the commissioning of the reactance parameters of the actual local loop network transmission line.