State Of Transmission Line Research Articles

Convolutional neural networks-based multi-sensor data fusion analysis for transmission lines

Abstract With the development of the power system, accurate assessment and fault diagnosis of the health status of transmission lines have become increasingly important. However, the standard data fusion algorithms do not fully exploit the correlation and weight disparity among multiple sensors. This study proposes a technique for analyzing transmission lines using a convolutional neural network-based approach to address this issue. By fusing data collected from multiple sensors in different directions, it can more accurately capture key information about transmission lines and conduct comprehensive analysis to evaluate their health status.

Research on monitoring method for ice-covered state of transmission lines based on conductor end displacement

To efficiently and economically monitor the status of transmission lines in frozen rain areas, we propose a method, which can monitor the status of ice- covered transmission lines based on conductor end displacement. Firstly, we delve into the factors that contribute to icing on transmission lines and the subsequent mechanical implications. Subsequently, we propose an equal-height double-node conductor model that aptly captures the stress characteristics of transmission lines in frozen rain environments. This model utilizes the horizontal displacement of the mid-span endpoint and the wire's load as proxies for the variations in horizontal tension and sag. Furthermore, we establish the overall static balance equation for continuous overhead conductors, grounded in the principle of nodal force equilibrium. We then elaborate on the operational framework of our monitoring model. A comparative analysis with existing models reveals the superior accuracy and computational efficiency of our proposed model. To validate our approach, we construct a real-world transmission line setup and assess the economic viability of various monitoring strategies. Additionally, we examine the impact of varying icing thicknesses and locations on the transmission line's status. Our findings indicate that our model outperforms traditional counterparts in terms of accuracy, computational speed, and cost-effectiveness. Specifically, at ice-covered sections, the maximum sag and tension of the conductor exhibit a direct correlation with the ice thickness. Conversely, in non-ice-covered areas, the maximum sag inversely correlates with ice thickness, while the tension maintains a direct relationship.

A hybrid energy harvesting approach for transmission lines based on triboelectric nanogenerator and micro thermoelectric generator

To effectively detect faults in transmission lines, monitoring the operating status of these lines is imperative. However, providing power to monitoring devices for transmission line status presents a significant challenge. In this research, a hybrid energy harvesting approach based on micro thermoelectric generator (MTEG) and triboelectric nanogenerator (TENG) is proposed, and a theoretical model for MTEG-TENG hybrid energy harvesting is established. This study develops an integrated energy harvesting prototype, which incorporates oscillating-TENG (O-TENGs), MTEGs, and a power management control unit. This prototype not only harvests energy from the vibrations of transmission lines but also converts the lines thermal energy into electricity. The Experiment results show that the maximum open-circuit voltages of O-TENG and MTEG reach 80.3 V and 1.094 V, respectively. Compared to a single MTEG energy harvesting device, the prototype of the MTEG-TENG hybrid energy harvesting device demonstrates a 5.36% improvement in energy harvesting and battery charging performance. Consequently, this approach achieves self-powered monitoring with excellent stability and lower manufacturing costs. It provides an efficient and durable power approach for transmission line status monitoring devices.

Search strategy and line association analysis of cascading failure accident chain in new energy power systems

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies.

A Dynamic Load Increasing Method for 220 kV Overhead Transmission Lines and Application

With the continuous development of China’s economy, the demand for electricity in society is increasing year by year, and it is necessary to continuously strengthen the construction of transmission capacity. This article aims to develop a terminal monitoring device that can monitor the status of transmission lines and the environment, analyze the monitoring data of overhead line status and micrometeorological data, and, based on the prediction model of the current carrying capacity of transmission lines, fully tap the power supply potential of transmission lines, which is of great significance for improving the transmission capacity of transmission channels.

Fault detection through discrete wavelet transform in overhead power transmission lines

AbstractTransmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

Intelligent Sensing and Monitoring System for High-Voltage Transmission Line Status of Smart Grid Based on IoT Technology

This paper integrates the Internet of Things (IoT) technology and a smart grid to build an electric power IoT architecture and analyzes the intelligent sensing technology and wireless communication technology in this electric power IoT. Through the multi-channel data collection technology in power IoT technology and an orthogonal discrete multiwavelet transform algorithm of edge computing technology, the high-voltage transmission line status data of the smart grid are collected and processed. Then, the high-voltage transmission line condition monitoring system is designed using the node design of the high-voltage transmission line condition monitoring sensing network and the optimal sensor configuration for droop monitoring. The performance of the monitoring system is simulated and examined. The experimental results show that as the number of burst data nodes increases, the acceptance rate of the ODMT algorithm decreases from 99% to 98%, and the network survival time is over 2000. When the current exceeds 20% of the rated current, the overall measurement error is controlled at approx. 3%. At a height of 4 m, the ratio of the difference between the input voltage and output voltage sensing monitoring is approx. 5%. The error range of temperature sensing monitoring is within ±1 °C. The error rate of communication distance within 200 m is 0, and over 200 m, the error rate is approx. 7%. This system can monitor the transmission status of high-voltage lines very well.

Transmission Defense Hardening Against Typhoon Disasters Under Decision-Dependent Uncertainty

Transmission defense hardening (TDH) is an effective practice to ensure the safe operation of the power system during and after natural disasters, such as typhoons. However, the coupling among the typhoon disaster, hardening decision, and transmission line status is not addressed in the previous research works. Besides, the enhancement measures for different types of transmission line failures have not yet been assessed. This paper proposes a comprehensive framework to improve power transmission system resilience against typhoon disasters. Firstly, the motion path and the wind field of typhoons are simulated using Monte Carlo sampling to quantify the spatiotemporal impacts of wind speed on the transmission line status. The decision-dependent uncertainty (DDU), which reflects the effects of hardening decisions on the transmission lines, is addressed by sampling according to the corresponding failure probabilities. Then, the scenario simulation is integrated into the proposed two-stage stochastic mixed-integer programming (SMIP) model considering various enhancement measures for different failures. Since the way to handle the DDU needs a large number of scenarios, it leads to high computation complexity. Hence, the sample average approximation (SAA) algorithm is introduced to cope with it. Finally, the proposed framework and its solution algorithm are carried out on the modified IEEE RTS-79 system and the modified IEEE 118-Bus system. The significant cost saving and resilience improvement demonstrate the effectiveness of the proposed framework.

Strain Monitoring of Overhead Transmission Line Based on Brillouin Scattering

The change in the optical fiber state and the status of transmission line often leads to the change of strain in its composite optical fiber. This paper attempts to use a commercial BOTDR (Brillouin optical time-domain reflectometry) to monitor the strain of optical fiber composited in transmission line. The Brillouin spectra of typical optical fiber used in the transmission line are measured by a commercial BOTDR at room temperature, the spectra are fitted and the key parameters are calculated, and then the strain along whole optical fiber is obtained by demodulation. The results show that BOTDR can effectively monitor the optical fiber strain, and the mean standard deviation of strain measured for multiple measurements under the same condition is only about 8 με, which can stastify the requirements of practical cases. The work of this paper lays a foundation for the strain measurement of transmission line.

Research on online monitoring system of fusion transmission line based on multi-source data

With the rapid development of China’s national economy, the length of transmission lines is increasing steadily, and the operating status of high-voltage transmission lines is directly related to the operational safety and efficiency of the entire power grid, but the sensors of transmission lines can only monitor one type of fault, and the fault analysis has limitations, which makes it impossible to comprehensively judge the operating status of transmission lines. This paper designs a transmission line online monitoring system based on multi-source data fusion, which adopts the improved Kalman filter algorithm. The data collected by the front-end system is fused, analyzed and shared in real time through the edge computing fusion terminal, so as to achieve a comprehensive judgment on the operation of transmission lines and improve the safe and stable operation level of transmission lines.

Transmission Line Sag Measurement and Simulation Research Based on Non-Contact Electric Field Sensing.

Sag is an important indicator of the operational health of a transmission line, and its timely measurement is of great significance to maintain the stability and reliability of power systems. However, traditional contact measurements may be affected by the electromagnetic interference of conductors. In contrast, measurement methods without direct electrical contact with the subject provide greater portability and flexibility. This paper presents a study of a transmission line sag measurement and simulation based on non-contact electric field sensing. The finite element method was used to analyze the conductor distribution, establish the coupling relationships among the electric field, transmission line, and measurement point, propose a sag inverse calculation model, and assess the impact of the transmission line parameter on the curved drooping measurement. Simultaneously, sag measurement schemes for single-round and dual-circuit lines were designed for multi-conductive lines, and measurement array studies were conducted. The vertical component of the electric field in space measured by the array was obtained, which could be used to perform conductor sag measurement simply and efficiently. The proposed method will facilitate the monitoring of the overhead transmission line status, which is conducive to the effective operation of the entire system.

Transmission line status portrait and assessment based on bidirectional long short-time memory networks

Transmission line status portrait and assessment based on bidirectional long short-time memory networks

Probability prediction method of transmission line icing fault based on adaptive relevance vector machine

To precisely forecast the operation status of transmission line during an ice storm and achieve early warning, a method based on adaptive relevance vector machine (ARVM) is proposed for fault probability prediction of transmission line icing. According to the basic theory of RVM, this paper establishes a forecasting model, which consists of selection and preprocessing of data, initial parameter optimization, icing prediction with adaptive optimization and fault probability prediction of transmission line. The quantum particle swarm algorithm, together with K-fold Cross-validation is applied to optimize model parameters. The weight vector of the icing prediction model is corrected by repeating training to get the precise prediction result of ice thickness with its probability distribution. The case study with practical data from Zhejiang province shows that the proposed method can effectively improve the accuracy of icing prediction and further realize fault probability prediction of transmission line, which can provide early warning for the anti-icing and mitigation work of the electric power department.

Comprehensive evaluation method of transmission line operating status based on improved combination weighting evaluation model

The transmission line is an important channel for the electric energy transmission in the power system. Whether the stable transmission of electric energy is related to key issues such as people’s livelihood and industry, so it is of great significance to evaluate the operation status of the transmission line. Aiming at the shortcomings of the current evaluation methods for the operation status of transmission lines, this paper proposes a comprehensive evaluation method for the operation status of transmission lines based on the improved combination weighting evaluation model. By constructing a transmission line status indication system based on transmission line standards and specifications, comprehensively considering subjective weights and objective weights, the operational health status of the 6 transmission lines in actual operation is calculated, and the calculation results are compared with the evaluation results of the national grid industry standards. In contrast, this method fully combines the advantages of the improved AHP method and the CRITIC method. While retaining the subjective opinions of experts, it can well avoid the phenomenon of unbalanced weights. Compared with the traditional standard evaluation method, it is more in line with objective reality. The evaluation and early warning of the operation status of transmission lines are of great guiding significance.

Phi-OTDR Based On-Line Monitoring of Overhead Power Transmission Line

The overhead power transmission line, as an important component of equipment for long-distance power transmission, is threatened by icing and galloping, which will lead to equipment troubles and cause huge economic loss. Thus, there is a great need for on-line monitoring for transmission lines. Because the photoelectric composite cable, such as optical fiber composite overhead ground wire (OPGW) is widely used, it is possible to introduce distributed optical fiber sensors (DOFS) into transmission line status monitoring. Common schemes based on DOFS usually offer low sensing density and are hard to realize global awareness, while phase sensitive optical time domain reflectometry (Φ-OTDR), as a DOFS that has the characteristic of distributed sensing, is hoping to address the limitation. In this paper, by establishing mathematical models, proposing analytical method, and building demonstration devices to analyze the dynamic strain characteristics of overhead power transmission line, an Φ-OTDR based on-line monitoring scheme of transmission line status is presented and experimentally proved for the first time. The estimation error of sag is less than 5.8% on centimeter scale, and the estimation error of ice thickness is no more than 10.84% on sub-millimeter scale, which gives an accurate description of transmission line status and provides strong support for early warning of transmission line failures.

Identification of line status changes using phasor measurements through deep learning networks

THE PURPOSE. To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. Develop the algorithm for detecting changes in the status of transmission lines in real time by using voltage and current phasors captured by phasor measurement units (PMUs) are placed on buses. Carry out experimental research on IEEE 14-bus test system. METHODS. This paper proposes a method from the field of artificial intelligence such as machine learning in particular "Deep Learning" to solve the problem. Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. One of the means to effectively extract the inherent hidden features in data are Convolutional Neural Networks (CNNs). RESULTS. The article describes the topic relevance, offers to apply the method for detecting status of lines using a CNN classifier. The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. The effectiveness of the joint use of PMUs and CNN in solving this problem has been proven. CONCLUSION. A solution for the line status change detection in the transient states using a CNN classifier is proposed. A high accuracy of the line status detection was obtained despite the influence of noise on measurement data. A change in the network topology is detected at the very beginning of the transient state almost instantly. It will allow the operator several times during the first seconds to identify the line state in order to make sure that the decisions made are correct.

Topology-Cognizant Optimal Power Flow in Multi-Terminal DC Grids

In this paper, we propose a topology-cognizant optimal power flow (OPF) paradigm with additional safety constraints for multi-terminal direct current (MTDC) grids. The resulting formulation is concerned with the optimization of controller set-points, i.e., voltage and power levels at each bus, and the switching status of transmission lines that collectively referred to as grid topology. A pair of additional safety constraints are integrated into the problem formulation to prevent voltage violations caused by power fluctuations in between two controller set-point updates. Searching for a grid topology that offers more efficient operation leads to a mixed-integer nonlinear program (MINLP) which is computationally challenging due to: i) Non-convex power flow equations, (ii) Non-convex converter loss equations, and iii) Binary variables accounting for the operational status of transmission lines. Non-convexities of power flows and converter loss equations are tackled by means of a mixed-integer second-order cone programming (MISOCP) relaxation, while the optimal switching status of transmission lines are determined via a branch-and-bound search. Numerical results for the modified IEEE 14, 30, and 57-bus systems are used to verify the merits of the proposed method. Furthermore, this method is experimentally validated using the CIGRE B4 DC grid benchmark in a real-time hardware-in-the-loop platform.

LPR‐MLP: A Novel Health Prediction Model for Transmission Lines in Grid Sensor Networks

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR‐MLP is proposed to predict the health status of the power grid sensor network. The LPR‐MLP model consists of two parts: (1) local binary pattern (LBP), principal component analysis (PCA), and ReliefF are used to process image data and meteorological and mechanical data and (2) the multilayer perceptron (MLP) method is then applied to build the prediction model. The results obtained from extensive experiments on the real‐world data collected from the online system of China Southern Power Grid demonstrate that this new LPR‐MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods.

Resilient Unit Commitment for Day-Ahead Market Considering Probabilistic Impacts of Hurricanes

In the face of extreme events, e.g., hurricanes, the transmission systems, especially the transmission lines, are affected across time and space. To mitigate these impacts on the day-ahead market from a probabilistic perspective, a resilient unit commitment (UC) problem is formulated as a two-stage distributionally robust and robust optimization (DR&RO) problem. In the first stage, the commitment, energy, and reserves of generators are pre-scheduled to minimize the operational cost, responding to the worst load forecasting and line failure scenario in the operating day. The operating status of transmission lines are depicted by a novel uncertainty set with a distributionally chance constraint considering the repair of failed lines. This chance constraint is reformulated to its deterministic equivalence. Using both load shedding and generation curtailment, recourse problems are formulated in the second stage considering the time-varying operating status of transmission lines. The formulated DR&RO problem is solved using a hybrid Benders decomposition and column-and-constraint generation scheme. Simulations are conducted on the modified IEEE reliability test system (RTS) and two-area IEEE RTS-96 under hurricanes. Results verify the effectiveness of the proposed method, in comparison with prevalent two-stage stochastic and robust optimization methods.

Data-Driven Screening of Network Constraints for Unit Commitment

The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant problems solved by independent system operators for the daily operation of power systems. Given its computational complexity, this problem is usually not solved to global optimality for real-size power systems. In this paper, we propose a data-driven method that leverages historical information to screen out network constraints in the TC-UC problem. First, past data on demand and renewable generation throughout the network are used to learn the congestion status of transmission lines. Then, we infer the lines that will not become congested for upcoming operating conditions based on such learning and disregard their capacity constraints. This way, we formulate a reduced TC-UC problem that is easier to solve. Numerical results on a medium- and a large-size power system show that the proposed approach outperforms existing ones by significantly reducing the computational time while obtaining solutions that are equal or close to the one obtained with the original TC-UC problem. Furthermore, the purely data-driven method we propose can be seamlessly complemented with a constraint generation procedure to guarantee that the optimal solution to the original TC-UC problem is eventually recovered.