Dynamic Thermal Rating Forecasting Methods: A Systematic Survey

Lawal, Olatunji Ahmed; Teh, Jiashen

doi:10.1109/access.2022.3183606

Cited by 12 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forecasting allows the system operator to foresee and manage potential issues before they happen. Forecasting methods can be classified into stochastic processes [173] and deep learning [174].…”

Section: Computing and Forecasting (Analytical Engine)mentioning

confidence: 99%

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Abas,

Ab Kadir,

Azis

et al. 2024

IEEE Access

View full text Add to dashboard Cite

As the world is pledged towards net zero carbon by 2050, the need for clean and efficient energy transitions is more critical than ever. Optimizing the power grid transfer capacity is crucial for maintaining grid stability and reliability. Ageing infrastructure, population growth, and revolutionary technological developments increase the demand for grid modernization and resilience investments. Climate change and natural disasters highlight the need for adaptive load-shedding schemes. The two possible ways to optimize the grid are an ampacity increase or a voltage increase. While increasing voltage provides the most significant rise in rating, it comes with high investment costs. Out of all the options available, dynamic line rating (DLR) is the most efficient and cost-effective solution. This paper provides a comprehensive review of the optimization of the grid transfer capacity using DLR. The review critically examines different line rating methods, the DLR system, factors that need to be considered before DLR implementation, and its advantages and disadvantages. Also, the review presents the real-world applications and case studies, standards and regulations involved, and current approaches and challenges for implementing DLR in Malaysia. Additionally, we highlight the most commonly used standards to calculate the conductor's ampacity for the steady-state and dynamic state. Moreover, this review work presents how DLR can advance the grid's flexibility, considering its significance for cleaner energy production in the future, challenges related to wind energy power generation, and their mitigations. This work provides a shortcut path for researchers and utilities to understand DLR and as a reference for future research to advance clean energy in response to changing energy needs and climate conditions.

show abstract

Section: Computing and Forecasting (Analytical Engine)mentioning

confidence: 99%

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Abas,

Ab Kadir,

Azis

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…While STR establishes a fixed maximum capacity based on limited environmental assumptions, DTR revolutionizes this approach. By integrating real-time weather data and advanced algorithms, DTR forecasts the actual capacity of transmission lines, enhancing operational efficiency and optimizing power system management [2]. This dynamic approach not only increases line capacity but also enables proactive planning and control, ushering in a new era of reliability and sustainability in energy transmission.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Thermal Rating in Diverse Climatic Conditions: Uncertainty Analysis and Fuzzy Logic Approach

Baishya,

Patowary

2024

IJRASET

View full text Add to dashboard Cite

In recent times, there has been a significant rise in the need for electricity, a trend expected to continue. This surge in demand has resulted in transmission lines operating at much higher capacities, which in turn exposes them to increased thermal and mechanical stress, ultimately impacting the reliability of the transmission network. In this paper presents a comprehensive analysis of the static thermal rating (STR) and dynamic thermal rating (DTR) of power lines across varied climatic conditions. The study encompasses a thorough investigation into the thermal behavior of power lines during both summer and winter seasons over a 24-hour period. Through rigorous computational simulations and empirical data collection, the STR and DTR profiles are determined to assess the real-time capacity of power lines under changing environmental conditions. Furthermore, the research introduces a novel approach utilizing fuzzy logic to model the dynamic thermal rating (FDTR) based on the IEEE 738 standard, in regions characterized by diverse climatic patterns. By integrating fuzzy logic with meteorological data, the FDTR model offers enhanced accuracy in predicting the thermal performance of power lines, considering factors such as ambient temperature, wind speed, and solar radiation. The proposed methodology aims to provide utilities and grid operators with a reliable tool for optimizing power transmission capacity, mitigating the risk of overheating, and ensuring grid reliability in regions with complex and fluctuating weather conditions. This study contributes to the advancement of smart grid technology and facilitates efficient energy management in diverse climate zones

show abstract

“…Electric power transmission systems rely on two primary methods to determine transmission line capacity: Static Thermal Rating (STR) and Dynamic Thermal Rating (DTR). While STR establishes a fixed maximum capacity based on limited environmental assumptions, DTR introduces a dynamic approach by integrating real-time weather data and advanced algorithms [2]. This dynamic method not only boosts line capacity but also facilitates proactive planning and control, ushering in an era of enhanced reliability and sustainability in energy transmission.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Dynamic Thermal Rating Assessment for Adaptive Grid Operations

Baishya,

Patowary

2024

IJRASET

View full text Add to dashboard Cite

In recent times, there has been a notable increase in electricity demand, which is expected to continue rising. This uptick in demand has led to transmission lines operating at higher capacities, exposing them to greater thermal and mechanical stress and affecting the reliability of the transmission network. This paper presents an in-depth analysis of the static thermal rating (STR) and dynamic thermal rating (DTR) of power lines across various climatic conditions. The study investigates the thermal behavior of power lines during both summer and winter seasons over a 24-hour period. Through computational simulations and empirical data collection, the study determines the STR and DTR profiles to evaluate the real-time capacity of power lines under changing environmental conditions. Additionally, the research introduces a novel approach using fuzzy logic to model the dynamic thermal rating (FDTR) based on the IEEE 738 standard, particularly in regions with diverse climatic patterns. By integrating fuzzy logic with meteorological data, the FDTR model improves accuracy in predicting the thermal performance of power lines, considering factors like ambient temperature, wind speed, and solar radiation. This methodology aims to offer utilities and grid operators a reliable tool for optimizing power transmission capacity, reducing the risk of overheating, and ensuring grid reliability in areas with complex and fluctuating weather conditions. The study contributes to the advancement of smart grid technology and facilitates efficient energy management in diverse climate zones.

show abstract

Dynamic Thermal Rating Forecasting Methods: A Systematic Survey

Cited by 12 publications

References 67 publications

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Optimizing Grid With Dynamic Line Rating of Conductors: A Comprehensive Review

Dynamic Thermal Rating in Diverse Climatic Conditions: Uncertainty Analysis and Fuzzy Logic Approach

Fuzzy Logic-Based Dynamic Thermal Rating Assessment for Adaptive Grid Operations

Contact Info

Product

Resources

About