AC Loss Prediction Model of a 150 kJ HTS SMES Based on Multi-Scale Model and Artificial Neural Networks

Sadeghi

Supercond. Sci. Technol.

et al. 2022

More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take the advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, health care, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing related issues. In the current century, massive advancements have been achieved on artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for the technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. The topical review are presented in three main categories, AI for large-scale superconducting applications, AI for superconducting materials, and AI for physics of superconductors. In addition, the challenges to apply AI techniques to the superconductivity and its applications are given. Eventually, future trends on how to integrate AI techniques with superconductivity towards the commercialization are discussed.

Section: Ai For Smesmentioning

confidence: 99%

“…In this process, the inputs are operating current and load parameters, while the output is AC loss. Also, the supervised training is used to increase the accuracy of the model, which was reduced due to the highly non-linear relation between current and AC loss [156].…”

Section: Ai For Smesmentioning

confidence: 99%

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Sadeghi

Supercond. Sci. Technol.

et al. 2022

“…An alternative to FE models is using empirical equivalent circuits, with intrinsically reduced calculation time It must be pointed out, however, that these advantages come at the cost of a lower accuracy of the results. To achieve real-time computation capability in the wide variety of 3D problems occurring in practical HTS applications, while maintaining accuracy, substantial innovation still need to be introduced in the calculation approach and AI can play a role in this [12,17,29], especially when system level analysis must be carried out, as it is discussed next.…”

Section: Current and Future Challengesmentioning

confidence: 99%

“…However, such a calculation involves a large number of state variables, it is time-consuming, and it cannot be executed in real-time. To achieve real-time computation, an AI layer is added to the FE layer where the solution of the problem for a specified operating condition is obtained by regression on a set of pre-calculated FE solutions [17,29]. Overall, the power grid controller generates the power versus time curve to be supplied (or absorbed) by the HTS SMES asset and the AI layer, implemented on the digital real-time simulators, evaluates the evolution of the SMES status following the service input received and checks the compatibility with safe SMES operation.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

Roadmap on artificial intelligence and big data techniques for superconductivity

Supercond. Sci. Technol.

Morandi

et al. 2023

This paper presents a roadmap to the application of AI techniques and big data for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and big data techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 years time-frame.

“…It makes this method slow and not robust enough, because any bug in the estimator side would feed the wrong information to the slow solving FE model. In [16], ANN was used to estimate AC losses of an HTS SMES for thermal stability studies. Firstly, the AC loss were calculated with a multi-scale model in FE software, and then back propagation ANN was implemented in MATLAB to estimate the loss.…”

mentioning

confidence: 99%

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Taghipour-Gorjikolaie

et al. 2020

IEEE Access

Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil's U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance.