Real-time monitoring of distribution systems has be-come necessary, due to the deregulation of electricity markets and the wide deployment of distributed energy resources. To monitor voltage and current at sub-cycle detail, requires, typically, major investment undertaking and disruptions to the operation of the grid. In this work, measurements of the low voltage (LV) side of distribution transformers (T/F) are used to calculate in real time the waveforms of their medium voltage (MV) sides, based on a mathematical model of said T/F. This model is, essentially, the dig-ital twin of the MV side of the T/F. The method calculates T/F MV waveforms of voltage and current, and active and reactive power as accurately as an instrument T/F, captures all harmonics con-tent, is unaffected by asymmetrical loading and identifies most sys-tem faults on the MV side of the T/F. The digital twin method en-ables monitoring of distribution T/F that avoids MV instrumenta-tion, does not suffer in accuracy and may be readily deployable. Field data from an actual MV-LV T/F, agree with simulation re-sults showcasing the efficacy of the digital twin method.
With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel vo…
In recent times, non-intrusive load monitoring (NILM) has emerged as an important tool for distribution-level energy management systems owing to its potential for energy conservation and management. However, load monitoring in smart building environments is challenging due to high variability of real-time load and varied load composition. Furthermore, as the volume and dimensionality of smart meters data increases, accuracy and computational time are key concerning factors. In view of these challenges, this paper proposes an improved NILM technique using multi-head (Mh-Net) convolutional neura…
Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate large quantities of data to train DNNs which would otherwise be difficult to obtain from the real-life system. However, such models can exhibit parametric uncertainty that propagates to the generated data. In addition, DNNs exhibit uncertainty in the parameters learnt during training. In such a scenario, the performance of the DNN model will be influenced by the…
Due to increased penetration of renewable resources in the distribution grid, the distribution system operator (DSO) faces increased challenges to maintain security and quality of supply. Since, a large proportion of renewables are intermittent generations, maintaining production and consumption balance of the electric system is at stake. The DSO needs to procure flexibilities not only from large resources, but also small-scale resources connected to the LV grid. Therefore, there is a need to estimate the aggregate flexibility at the secondary substation available from the LV grids. The flexib…
Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operatepower systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse and powerblackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential totake proactive control actions and minimize the impact on system. This paper presents a novel online algorithm for a) hybridperturbation analysis-based voltage stability monitoring (HPVSM), b) including Q-limit in voltage …
A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
University of Bath（巴斯大学）；Loughborough University（拉夫堡大学）
Measurement-rich power distribution networks may enable distribution system operators (DSOs) to adopt model-less and measurement-based monitoring and control of distributed energy resources (DERs) for mitigating grid issues such as over/under voltages and lines congestions. However, measurement-based monitoring and control applications may lead to inaccurate control decisions due to measurement errors. In particular, estimation models relying on regression-based schemes result in significant errors in the estimates (e.g., nodal voltages) especially for measurement devices with high Instrument …
Queen’s University Belfast（贝尔法斯特女王大学）；National Chiao Tung University (NCTU)（国立交通大学）；National Yang Ming Chiao Tung University (NYCU)（国立阳明交通大学）
While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.
Digital twin is a virtual replica of a real-world object that lives simultaneously with its physical counterpart. Since its first introduction in 2003 by Grieves, digital twin has gained momentum in a wide range of applications such as industrial manufacturing, automotive and artificial intelligence. However, many digital-twin-related approaches, found in industries as well as literature, mainly focus on modelling individual physical things with high-fidelity methods with limited scalability. In this paper, we introduce a digital-twin architecture called TiLA (Twin-in-the-Loop Architecture). T…
We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based di…
How to Integrate Digital Twin and Virtual Reality in Robotics Systems? Design and Implementation for Providing Robotics Maintenance Services in Data Centers
University of Twente（特文特大学）；Beijing Hanning Tech Co., Ltd（北京汉宁科技有限公司）
Deep Generative and Discriminative Digital Twin endowed with Variational Autoencoder for Unsupervised Predictive Thermal Condition Monitoring of Physical Robots in Industry 6.0 and Society 6.0
Department of Computer Science \& Engineering, Frankfurt University of Applied Sciences, Frankfurt a.M., Germany (e-mail: ).
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven mod…
