AI for capacity estimation in overhead transmission lines - A new model proposal for deploying AI to do dynamic line rating
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Tydén, Ludvig
Nordin, Jonas
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
Dynamic line rating (DLR) for overhead transmission lines, which estimates the current-carrying capacity based on environmental and operational conditions, presents an opportunity to enhance the efficiency and reliability of electrical grids. This thesis explores the feasibility and application of artificial intelligence (AI) to improve the accuracy and scalability of DLR systems, while also lowering the cost of installation. By leveraging machine learning models, particularly physics-informed neural networks (PINNs), this research aims to lay the foundation of the development of an advanced DLR solution capable of real-time and forecast estimation of the capacity of a transmission line. The initial phase of the thesis focuses on implementing a weather-based model, based on the IEEE-738 standard, to estimate line ratings based on weather parameters such as temperature, wind speed, wind angle and solar radiation, but also parameters such
as the electrical current and conductor specific metrics. This model serves as both a practical tool for immediate deployment and a foundational step towards more complex AI models. Following the development of the weather-based model, the research transitions to the integration neural networks, with a perspective of utilizing physics informed neural networks. These models combine the data-driven capabilities of traditional machine learning with the robustness of physical laws governing power transmission. The objective is to enhance the precision and reliability of the DLR system, accommodating non-linear relationships and interactions within the data. The thesis proposes a new model of a DLR system based around the weather model and machine learning. The system consists of several modules that each serve a purpose, data collection, ML model, ML training, weather model and real-time capacity estimation. The findings demonstrate that AI-based DLR systems is a new, interesting approach that can significantly improve the operational efficiency of electrical grids by providing more accurate and adaptive line ratings. This research contributes to the field of power systems by offering a scalable and innovative approach to dynamic line rating, supporting the transition towards a smarter infrastructure. The project has gained interest from key stakeholders such as EON, Vattenfall, and Svenska kraftnät, and has been formed in close contact with their respective specialists in DLR systems. The involvement of these industry leaders underscores the practical relevance and potential impact of this type of research in this domain.