How do Transformer-based models accurately predict EV charging demand?5 answersTransformer-based models accurately predict EV charging demand by leveraging time series data and incorporating spatial and temporal information. These models, such as BERTtery, have been designed specifically for battery systems and use time-resolved battery data (current, voltage, and temperature) as input to estimate the state-of-charge (SOC) of the battery. The Transformer architecture, with its self-attention mechanism, allows the model to capture complex multiphysics and multiscale behaviors of the battery system, enabling accurate SOC estimation under dynamic operating and aging conditions. Additionally, the Transformer model has been shown to outperform other traditional and deep learning approaches, such as RNN, LSTM, and ARIMA, in predicting EV charging demand. By effectively capturing both spatial and temporal dependencies, Transformer-based models can provide reliable forecasts of EV charging loads, aiding in the efficient management of electricity grids.
How to calculate parasitic parameters in high voltage high frequency transformer?3 answersParasitic parameters in high voltage high frequency transformers can be calculated using various methods proposed in the literature. One approach is to use an optimization method to extract the parasitic parameters of the transformer. This method involves substituting the traditional equivalent circuit with a five capacitance network equivalent circuit model that considers the characteristics of the electric field energy transformation and the common mode electromagnetic interference. Another analytical approach is based on the principle of electrostatic energy and calculates the self-capacitance of power transformers by considering the capacitance between the turns and between the layers. Additionally, a novel analytical method has been proposed for calculating turn-to-turn, turn-to-core, and stray capacitances in each winding, which can be used to calculate the equivalent parasitic capacitance in high-voltage switching transformers. These methods provide efficient ways to accurately calculate the parasitic parameters in high voltage high frequency transformers.
What is the transformer model?5 answersThe transformer model is a deep neural network that utilizes a self-attention mechanism to understand the contextual relationships within sequential data. It excels in handling long dependencies between input sequence elements and enables parallel processing, making it suitable for a wide range of domains such as natural language processing, computer vision, audio and speech processing, healthcare, and the Internet of Things. Several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations. However, there is still a need for a comprehensive survey paper that encompasses its major applications across various domains. Elmekki et al. conducted an extensive survey of proposed transformer models from 2017 to 2022, identifying the top five application domains and analyzing the impact of influential transformer-based models in these domains. Lomurno and Matteucci further investigated the effectiveness of transformer-based models in time series forecasting, demonstrating their limitations and proposing alternative models that are better performing and less complex.
How to calculate the stray capacitance of a high frequency transformer?4 answersThe stray capacitance of a high-frequency transformer can be calculated using various methods. One method is to use an analytical approach based on the dimensions of the winding and core. Another method involves measuring the magnetic flux density of the transformer under high-frequency switching conditions and observing the oscillations caused by stray capacitances. Additionally, an experimental measuring method can be used, which involves constructing a circuit for measuring stray capacitance and calculating the value of the stray capacitance using a charge conservation formula. Another approach is to calculate the capacitance of a multi-layer foil winding transformer by considering the turn-to-turn and layer-to-layer capacitances based on conductor and insulation geometry. Finally, a capacitance voltage transformer stray capacitance calculation method involves dividing a capacitive voltage divider into units and obtaining the capacitance coefficient matrix to accurately determine the stray capacitance.
How can the parasitic capacitance of transformers be calculated?5 answersThe parasitic capacitance of transformers can be calculated using various analytical methods. One approach is based on the energy-conservation law, where the equivalent capacitances of inductors are determined by considering the floating voltage potential of the magnetic core. Another method involves calculating the electrical energy within the winding window using Schwarz-Christoffel transformations and considering the potential distribution between different turns. A novel analytical method has been proposed to calculate turn-to-turn, turn-to-core, and stray capacitances in each winding, which can be used for high-voltage switching transformers. Additionally, rethinking the basic assumptions used in analytical modeling can lead to more accurate results, with the energy-conservation method being a reasonable solution for modeling the equivalent parasitic capacitance at the first resonant frequency. A general physics-based model has also been proposed for identifying the parasitic capacitance in medium-voltage filter inductors, providing analytical calculations without empirical equations.
Has Transformer Models been used for predictions?5 answersTransformer models have been used for predictions in various domains. For example, in the field of natural language processing and social media analysis, a transformer-based approach using BERT has been proposed for emoji prediction, achieving high accuracy. In the analysis of Transformer language models, the prediction head, including bias parameters, has been investigated, revealing their significant role in reflecting word frequency and improving text generation. In the healthcare domain, transformer models have been utilized for real-time prediction of acute brain dysfunctions using Electronic Health Record data, achieving high classification performance. Additionally, transformer models have been employed for explaining predictions of generative transformer models, providing relevance maps for input-output relationships in a computationally efficient manner. Furthermore, transformer models have been used for irradiance prediction in photovoltaic renewable energy systems, successfully fitting and extrapolating irradiance trends.