Thermal Line Rating Research Articles

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

Abstract: 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

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

Abstract: 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.

Optimizing High-Voltage Direct Current Transmission Corridors: Dynamic Thermal Line Rating for Enhanced Renewable Generation and Greenhouse Gas Emission Reductions

Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution.

Interactive demand response and dynamic thermal line rating for minimizing the wind power spillage and carbon emissions

Spilling has already occurred as a result of rising the penetration of intermittent renewable generation, and it is anticipated that the level of renewable energy curtailment will continue to soar. This leads to an increase in operating costs, CO2 emissions, and not good utilization of renewable energy resources. A bi-level multi-objective optimization model is proposed in this paper to reduce wind power spillage (WPS) based on demand response (DR) and dynamic thermal line rating (DTLR). In the upper level, multiple objectives will be satisfied based on the optimal allocation and time of DR programs considering DTLR obtained in the lower level. The minimization of WPS, load shedding, power losses, and CO2 emissions are the objectives of this level. While the lower-level aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level problems that represent the market clearing conditions is used to constrain the upper-level. The effectiveness of the proposed algorithm is examined on a modified two-area IEEE 24-bus test system. Results depict that the suggested bi-level model enables considerable reductions in the WPS by up to 32.7 %. Also, there is an enhancement in load shedding, power losses, and CO2 emissions by 28.93 %, 23.07 %, and 13.9 % respectively. Finally, the social welfare increased by up to 36.6 %.

Coordinated integration of wind energy in microgrids: A dual strategy approach leveraging dynamic thermal line rating and electric vehicle scheduling

As the world shifts towards sustainable energy, integrating wind energy as a key renewable resource is crucial, despite the challenge of its intermittent output. This paper proposes an integrated approach to enhance wind energy utilization within microgrids, introducing a novel synergy of Dynamic Thermal Line Rating (DTLR) and Electric Vehicle (EV) scheduling. This synergy dynamically adjusts power transmission capacities and harnesses EVs as mobile energy storage units. Notably, case study simulations reveal that compared to other approaches, our proposed solution reduces total operational costs by 24.6% (STLR only) and 11.6% (STLR+EV), and increases wind power absorption by 41.7% (STLR only) and 12.1% (STLR+EV). This marks a significant stride towards sustainable microgrid operation amidst renewable energy integration challenges.

On the issue of applying the dynamic thermal rating in electric power distribution system

The article considers separate studies and review articles on dynamic thermal rating system in power transmission lines and electrical equipment. Available researches of foreign authors are mainly devoted to forecasting algorithms, model-ing methodologies and implementation of dynamic thermal rating in order to increase the capacity of power transmis-sion lines. It is shown that for the tasks of planning and management of the electric power system in Russia, the need to study and coordinate domestic and foreign developments on the assessment of the capacity of power transmission lines and power distribution is currently relevant. The expansion of residential areas increases the load on electric distribution network due to the shift of maximum power consumption. The currently dynamically changing conditions of electricity consumption direct attention to the development of new technological concepts and the applicability of the existing approaches to assessing the capacity of electricity transmission lines to distribution networks. Available research on dynamic thermal rating in electric distribution networks has focused much attention on both the potential problems and benefits of its implementation in the integration of distributed generation based on the renewable energy sources, in particular wind generation. The ambiguous impact of dynamic thermal rating systems on the reliability of the electric distribution network and the need for their reasonable use, as well as a significant lack of research in relation to cable distribution networks and consistency in assessing their capacity depending on the cable insulation material are being not-ed. Few studies have verified the adaptation of the state-of-the-art approach of dynamic thermal rating of overhead transmission lines to cable lines for predicting parameters corresponding to operational objectives. The research results can be useful in the development and modernization of metering systems, in which new functions of power loss moni-toring and automated determination of distribution network reliability indicators are implemented, which will allow, within the framework of “smart grids”, to ensure the required level of reliability of power supply to consumers.

Preface

The International Conference on Power Electronics and Electrical Technology (ICPEET) has confirmed once again this year the enthusiasm and determination of students and young researchers to play a major role in the scientific progress. Since 2022, we have regularly gathered to discuss the most recent developments and achievement on power electronics and electrical technology, and we firmly convince that sharing our expertise and experience is the foundation of research activity.The 2023 2nd International Conference on Power Electronics and Electrical Technology (ICPEET 2023) took place on July 7th to 9th, 2023 in Chongqing, China (virtual event), attracting about 80 leading delegates around the world. The format we chose is a formal meeting primarily aimed at graduate students and leading researchers, who were encouraged to present their work through oral or poster presentations that provide genuine engagement of the audience and cross-pollination of ideas. One of the main purposes of the Conference was to create an international network of researchers, both experimentalists and theorists, and fruitful collaborations across different branches of power electronics and electrical technology.After previous meeting that strengthened it, the International Conference on Power Electronics and Electrical Technology was held for the second time. It was a three-day Conference, where there was an extremely rich and interactive keynote speech, oral and poster session that covered several areas of power electronics and electrical technology.With the intent of broadening the contents and stimuli adopting academic communication, the keynote speech part was held. This choice reflected the growing importance of the activity performed by scientists and researchers, especially at the earliest stages of their career, as a way of increasing their expertise and developing soft skills. Prof. Jiashen Teh (Universiti Sains Malaysia, Malaysia) addressed a speech on Flexible Thermal Line Rating for Reliable Power Systems, Prof. Zhenbin Zhang (Shandong University, China) on Bias-Free Fast Start-Up Strategy and High-Performance Predictive Control Method for DC Energy, and Prof. Ali Arefi (Murdoch University, Australia) on Reliability of Renewable-Rich Microgrids: Evaluation and Planning.In this volume, we collected part of the contributions that have been presented at the Conference. They cover topics on Cogeneration and Distributed Generation, High Voltage Technology, Wireless Power Transmission, Electromagnetic Compatibility, Advanced and Sensorless Machine Control, etc. Given the recent experimental achievements in power electronics and electrical technology, several contributions were focused on the latest results obtained in these fields, presenting the impact of experiments to the community of young researchers and forecasting the future goals in these areas of research.The organizers of ICPEET 2023 would like to thank all the keynote speakers, authors, peer reviewers, and everyone who contributed to the Conference. We owe gratitude to all the Committee members for providing necessary help and support throughout the meeting. The publication of the Proceedings of this Conference is supported by Journal of Physics: Conference Series.The Committee of ICPEET 2023List of Committee Member is available in this Pdf

Dynamic thermal line rating model of conductor based on prediction of meteorological parameters

This paper developed a new algorithm to predict dynamic thermal line rating to increase the capacity of transmission lines, which can enhance the capacity of wind power integrated to the grid and reduce the curtailment. The proposed dynamic thermal line rating prediction model was trained by analyzing historical meteorological data and conductor physical parameters, and used deep learning with parameters optimized by an optimized algorithm. The prediction accuracy of the model is verified by Mean Absolute Error, R2 and comparison with other models. The simulation results show that the proposed prediction model has a good performance. The suggested dynamic thermal line rating algorithm, which bears resemblance to the actual value, boosts the static thermal line rating by varying degrees of 23% to 75% at different instances throughout the sample. At the same time, this paper designs an optimal power flow economic dispatch objective function. By comparing the economic dispatch of the power grid calculated by adding static thermal line rating and the prediction models, the method proposed in this paper can effectively increase the amount of wind power integration and reduce power generation costs.

Methods for estimation of OHL conductor temperature based on ANN and regression analysis

Due to the trend of increased production, consumption and interstate exchange of electricity, transmission system operators are faced with the problem of allowable thermal rating of overhead lines (OHLs) in the transmission power network. Transmission system operators often use static thermal rating for maximum allowable thermal rating of OHL conductor. Such static thermal limits are usually defined for operation in extreme weather conditions which are rarely achieved in real-world operation. Nowadays transmission system operators calculate the thermal limits based on current weather conditions and line ampacity, dynamically in real-time. Such techniques allow higher exploitation of existing OHL enabling safe and stable transmission of electrical energy. In this paper, based on the weather parameters collected from an automated weather station installed on a transmission tower, the conductor temperature is estimated using newly developed methods based on artificial neural network (ANN) and regression analysis. Estimation results are compared with ones obtained by existing CIGRE 601 and IEEE 738-2012 methodologies. Calculated temperatures are compared with measured temperatures from Overhead Transmission Line Monitoring (OTLM) device. Both methodologies, regression analysis and ANN are discussed, and their error of conductor temperature calculation is analyzed. DTR is calculated based on the maximum allowable conductor temperature.

Impacts of Measurement Errors on Real-Time Thermal Rating Estimation for Overhead Lines

To exploit additional capacity on legacy transmission assets, network owners have developed a variety of techniques to support real-time thermal rating (RTTR) of overhead lines so as to get the maximum possible capacity within their operating range. Most RTTR techniques are inferred from effective weather conditions (EWCs) that are used to assess the thermal equilibrium of conductors indicated by the measured temperature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> , thus making RTTR estimates sensitive to measurement errors (MEs). To highlight the impacts that MEs have on RTTRs, this paper describes two EWC-based approaches to RTTR estimation and assesses the accuracy of their resulting estimates under steady state conditions given different levels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> and weather variables. Furthermore, the paper develops a Monte Carlo-based approach to model the propagation of measurement uncertainties through to RTTR estimates. Numerous rating scenarios for a particular instance are generated through combining a Monte Carlo method, where possible MEs are sampled within the sensor specification based limits, with an enhanced EWC-based approach that models transient changes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${T}_c$</tex-math></inline-formula> in each scenario. The lower RTTR percentiles extracted from the rating samples can not only mitigate the RTTR overestimation due to MEs, but also inform system operators of the risk associated with RTTR decisions.

Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

Transmission system operators (TSOs) in recent years have faced challenges in order to ensure maximum transmission capacity of the system to satisfy market needs, while maintaining operational safety and permissible impact on the environment. A great help in the decision-making process was introduced with the Dynamic Thermal Rating (DTR) - an instrument to monitor and predict the maximal allowed ampacity of the power grid based on weather measurements and forecast. However, the introduction of DTR raises a number of questions related to the accuracy and uncertainty of the results of thermal assessment and the level of acceptable risk and its management. In this paper, we present a solution for estimating DTR uncertainty, appropriate for operational use at TSOs. With the help of conductor surface temperature measurements, weather measurements and predicted weather data, we also estimate the error of the weather forecast and the DTR itself. Following the results of the data analyses, we build an operative solution for estimating the ampacity uncertainty based on Monte Carlo random simulations and integrate it into the operational environment of ELES - the operator of the Slovenian electric power transmission network.

Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating

Wind power generations as renewable and sustainable energies are widely integrated into electric power systems due to their technical, environmental, and economic advantages. This integration affects the power system operation and planning studies. Network-constrained unit commitment (NCUC) is one of important operation studies of electric power systems in which the aim is to schedule the generating units for the next 24-h period with the minimum operating cost considering the constraints of generating units and transmission network. One of the essential constraints influencing the results of NCUC is thermal rating of lines and transformers. In most of the previous studies, static ratings have been used for this equipment. The static ratings are conservative values leading to non-optimal use of real capacity of lines and transformers and increasing the system's operational costs. This issue is highlighted when there exist wind energy generations in system. A new NCUC problem is addressed in this paper that considers dynamic line rating (DLR) and dynamic transformer rating (DTR) in the presence of wind power generations. The proposed approach has been implemented on the 24-bus IEEE-RTS system in the GAMS environment in different experiments. The simulation results demonstrate effectiveness of the conducted model in reducing the operational costs and maximizing the usage of wind energy.

A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines

The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and line conditions measured by several sensors. This manuscript shows that estimation accuracy can be improved by adopting a data-driven Digital Twin approach. The proposed method exploits machine learning by learning the input–output relation between the physical sensors data and the actual conductor temperature, serving as a digital equivalent to physics-based standards. An experimental assessment on real data, comparing the proposed approach with the IEEE 738 standard, shows a reduction of 60% of the Root Mean Squared Error and a decrease in the maximum estimation error from above 10 °C to below 7 °C. These preliminary results suggest that the Digital Twin provides more accurate and robust estimations, serving as a complement, or a potential alternative, to traditional methods.

Forecast of transmission line clearance using quantile regression‐based weather forecasts

This paper uses dynamic thermal line rating methods and a probabilistic prediction methodology to forecast line clearance and conductor temperature and evaluate the risk of clearance encroachment. A transient model is used to predict conductor temperature at different prediction levels. Clearance is estimated using a relationship model, developed based on historical measured clearance and conductor temperature data. Additional sensitivity analysis is performed to determine the applicability of ambient-adjusted predictions when considering a lightly loaded line compared to a heavily loaded line. The developed methodology enables utilities to make decisions on line loading in advance of real-time operation with information on confidence associated with that decision.

Improvement and sensitivity analysis of equivalent heat transfer model for dynamic thermal rating of overhead lines

With the rapid growth of power demand, the conductor ampacity calculation method based on the dynamic thermal rating (DTR) becomes more and more important. Based on the shortcomings of the current DTR models, a new DTR model that does not need to measure the wind speed and the operating state of conductor is proposed, which is called the equivalent heat transfer (EHT) model. However, there are still shortcomings in the accuracy of the EHT model in application. In this paper, the EHT model is improved at first according to the consideration of the effect of air physical parameters and the redetermination of the experimental parameters of the EHT equipment. Then, the operation of EHT equipment is simulated through the established experimental platform. The improved EHT model is verified by the IEEE standard. Finally, the sensitivity analysis of the improved EHT model is carried out. The results show that the improved EHT model is greatly improved on application accuracy compared to the original EHT model. Moreover, the improved EHT model can choose the steady-state temperature at any position on the surface of the aluminum ball to calculate the conductor ampacity, and the relative error does not exceed 6%. The improved EHT model is reliable and can meet the safe operation requirements of the power system in practical engineering applications.

Secure Probabilistic Prediction of Dynamic Thermal Line Rating

Accurate short-term prediction of overhead line (OHL) transmission ampacity can directly affect the efficiency of power system operation and planning. Any overestimation of the dynamic thermal line rating (DTLR) can lead to the life-time degradation and failure of OHLs, safety hazards, etc. This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR. The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR. The model is based on an augmented deep learning architecture that makes use of a wide range of predictors, including historical climatology data and latent variables obtained during DTLR calculation. Furthermore, by introducing a customized cost function, the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values. The proposed probabilistic DTLR is developed and verified using recorded experimental data. The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.

Analysis of a Smart Sensor Based Solution for Smart Grids Real-Time Dynamic Thermal Line Rating

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor’s location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.

Probabilistic forecasting of dynamic thermal line rating with temporal correlations

Dynamic Thermal Line Rating is a technology that optimizes the utility of overhead power transmission lines by dynamically adjusting the rating according to current ambient conditions. It is often applied in discrete time intervals (i.e., the rating is updated hourly or daily) based on a rating forecast for the next period. This paper examines the effects of temporal discretization on rating prediction, discusses the importance of temporal correlations, and proposes an approach to include these correlations in a rating prediction system. It demonstrates that predictions may be biased towards extreme values if temporal correlation is not taken into account. The proposed solution to this problem is a sampling algorithm based on iterative sampling from a chain graphical model to emulate the correlations as observed in real data. This results in a set of time series samples with well-calibrated joint probability between neighbouring variables. Therefore, these samples are usable in a Monte Carlo optimization of an objective function and yield good calibration overall. The experimental evaluation shows that the dynamic rating calculated through this sampling method permits the usage of the result at its face value without the need for further calibration. It also increases the average daily rating compared to the basic quantile selection method.

An Enhanced Algorithm for Conventional Protection Devices of Transmission Lines With DTLR

As the installed capacity of renewable generation continues to increase nowadays to satisfy both emissions and economic requirements, the nature of intermittent generation results in a need to further increase transmission capacity. Dynamic thermal line rating may be used to increase the capacity of transmission lines without upgrading infrastructure. However, existing transmission line protection devices may mal-operate due to the increased capacity. So, it is required to evaluate the performance of existing protection devices. This paper proposes an algorithm that enhances the performance of existing conventional relays such as distance and overcurrent relays. The proposed algorithm is based on a combination of dynamic thermal rating, current ratio of negative and positive sequences and voltage criterion to detect faults whether symmetrical or asymmetrical and unsafe overload. In addition, it aims at restraining the existing relays during safe overloads. A model of a system under investigation is simulated, with studies are performed in MATLAB/Simulink environment. The results demonstrate the ability of the proposed scheme to detect all types of faults and unsafe overload dependably and restrain the conventional relays securely during safe overloads.