PreprintArticleVersion 1This version is not peer-reviewed
Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks
Version 1
: Received: 20 October 2024 / Approved: 21 October 2024 / Online: 21 October 2024 (16:24:13 CEST)
How to cite:
Neupert, S.; Yao, J.; Kowal, J. Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks. Preprints2024, 2024101629. https://doi.org/10.20944/preprints202410.1629.v1
Neupert, S.; Yao, J.; Kowal, J. Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks. Preprints 2024, 2024101629. https://doi.org/10.20944/preprints202410.1629.v1
Neupert, S.; Yao, J.; Kowal, J. Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks. Preprints2024, 2024101629. https://doi.org/10.20944/preprints202410.1629.v1
APA Style
Neupert, S., Yao, J., & Kowal, J. (2024). Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks. Preprints. https://doi.org/10.20944/preprints202410.1629.v1
Chicago/Turabian Style
Neupert, S., Jiaqi Yao and Julia Kowal. 2024 "Advanced Load Cycle Generation for Electrical Energy Storage Systems Using Gradient Random Pulse Method and Information Maximizing-Recurrent Conditional Generative Adversarial Networks" Preprints. https://doi.org/10.20944/preprints202410.1629.v1
Abstract
The paper introduces an approach to extract information from measurements and generate new load cycles for electrical energy storage systems. Load cycle analysis is performed using rainflow counting, which helps evaluate data and identify stress factors. Load cycle generation can involve clustering methods, random micro-trip methods, and machine learning techniques. The study utilises the Random Pulse Method (RPM) and presents an improved version called the Gradient Random Pulse Method (gradRPM) that allows control over stress factors such as the gradient of the State of Charge (SOC). Another method called Information Maximizing-Recurrent Conditional Generative Adversarial Network (Info-RCGAN) has been developed, and it utilises a deep learning algorithm for data-driven load profile generation with control over stress factors. The results demonstrate the effectiveness of the gradRPM and Info-RCGAN methods in generating load profiles based on the given parameters. The findings provide valuable insights into designing simulation data or testing data for electrical energy storage applications, aiding in improving and understanding system behaviour and requirements.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
