Transmission Line Failures Research Articles

Proactive Prevention Strategies for Grid Risk Scenarios Based on Pumped Storage Participation

Given the increasing importance of cascading trip prevention, this study presents the pumped storage characteristics to cooperate with thermal units and further ensure the safety of grid operation. The proposed model comprises two phases: the first phase is the day-ahead decision-making phase, which synergistically optimizes thermal units and pumped storage to improve the flexibility of grid operation; the second phase is the intra-day adjustment phase considering the uncertainty of the wind-photovoltaic and transmission line failure outage to enhance the robustness. Furthermore, this model employs a column and constraint generation algorithm to solve the two-phase, three-layer optimization problem, thereby minimizing the adjustment cost under the riskiest scenarios. The experimental results show that adding pumped storage reduces the total system operating cost by 3.99%, the renewable energy abandonment rate to 0.65%, and the thermal power unit output standard deviation to 74.13 MW.

Resilience Improvement of Microgrid Cluster Systems Based on Two-Stage Robust Optimization

For microgrids in deep and remote sea areas, ocean currents are widely used as an emerging power generation resource. Implementing ES systems is crucial for smooth power output and grid stability. The stability of power output from sea current energy generation faces challenges due to speed fluctuations. Enhancing resilience requires addressing transmission line failures caused by extreme seabed conditions, and ensuring operational security. An ES configuration method considering line faults based on two-stage robust optimization is presented in this paper. First, in order to simultaneously consider planning and operation, a defender–attacker–defender (DAD) model was established. Additionally, the capacity, rated power, and charging/discharging power of ES during operation were jointly optimized through the column-and-constraint generation (C&CG) algorithm. In addition, the rationality and effectiveness of the proposed method were demonstrated through experiments on modified IEEE six-bus and fifty-seven-bus systems. The results show that a distributed ES configuration increased system resilience by 54.60% and reduced abandoned power rate by 57.06% compared with the situation without ES configuration.

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.

Dynamic response of conductors during transmission tower failure under downburst loads

The frequency of thunderstorm-related failures of transmission lines has increased globally as a result of climate change. Downbursts are one of the High Intensity Wind (HIW) events associated with thunderstorms that have a detrimental impact on transmission towers due to their localized and non-stationary nature. When one tower fails, the conductors are subjected to dynamic excitation induced by the movement of the attached points to the towers which increases the tension forces in the conductors. If the adjacent towers are unable to sustain the increased tension forces, a cascade failure of multiple towers along the line is formed. The study aims to identify the conductor response to a tower failure under downburst loads using nonlinear dynamic analysis. The reactions transferred to the adjacent towers due to the tower failure are reported as well. The effect of varying the downburst jet velocity and the failure location within the tower height are investigated. The results show that the conductor response to the dynamic excitation of the tower failure depends on the velocity of the tower movement, the magnitude of the transverse load on the conductors, and the failure location along the tower height. The tension forces in the conductors and the reactions at the adjacent towers increase considerably due to the tower failure that can cause the failure of the adjacent towers.

A novel superhydrophobic Al conductor with excellent anti-icing performance and its mechanism

Overhead conductor icing is one of the main factors causing transmission line failures. Superhydrophobic surface (SHS) with composite pore structure exhibits excellent anti-icing effectiveness and durability. Herein, this novel SHS was prepared on complex aluminum conductors by two-step anodic oxidation technology, and the motion behavior of droplets on the SHS conductor in the glaze icing environment was studied experimentally to analyze the anti-icing mechanism. The simulated glaze icing experiment results show that the SHS conductor significantly reduces the ice accumulation, and the icing weight is only 23 % of the untreated conductor. Compared with the flat surface, the groove structure of the SHS conductor enhances the ability of millimeter-sized droplets to bounce, and the contact time is less than 9 ms. Also, micron-sized spraying droplets exhibit excellent mobility on the SHS conductor surface, and the contact time is further reduced. Moreover, the SHS conductor has a good ability to delay the freezing of spraying droplets. The above factors endow the SHS conductor excellent anti-icing performance. This work lays a foundation for the anti-icing application of SHS on transmission lines.

Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines

The Texas power grid on the Gulf Coast of the United States is frequently hit by tropical cyclones (TCs) causing widespread power outages, a risk that is expected to substantially increase under global warming. Here we introduce a new approach that combines a probabilistic line failure model with a network model of the Texas grid to simulate the spatio-temporal co-evolution of wind-induced failures of high-voltage transmission lines and the resulting cascading power outages from seven major historical TCs. The approach allows reproducing observed supply failures. In addition, compared to existing static approaches, it provides a notable advantage in identifying critical lines whose failure can trigger large supply shortages. We show that hardening only 1% of total lines can reduce the likelihood of the most destructive type of outage by a factor of between 5 and 20. The proposed modelling approach could represent a so far missing tool for identifying effective options to strengthen power grids against future TC strikes, even under limited knowledge.

Flexible resource optimal allocation strategy for distribution system resilience improvement under failure uncertainty

Extreme disasters have led to huge economical losses to the power system and posed a huge threat to the stable supply of electric energy. Especially for traditional passive distribution network systems, failures of transmission lines, towers, and other equipment will cause large-scale power outages and huge economic losses. In order to improve the ability of the power system to deal with extreme weather disasters, this paper proposes an optimal configuration strategy for the distribution network backup power resource oriented to resilience improvement, considering the uncertainty of equipment failure caused by disasters in the case of a known disaster range. The location and capacity of backup resources can be properly configured to reduce the load shedding of the power distribution network during the disaster process, thereby improving the resilience of the distribution network. The method proposed in this paper is modeled as a two-stage stochastic programming, which includes mix-integer constraints. In the first stage, the optimization problem confirms the capacity and location of the flexible resources. In the second stage, the operational constraints and costs of load shedding are considered. The resilience enhancement method is verified on the improved IEEE-33 node system, and the result shows that the method proposed in this paper can effectively enhance the resilience of the urban power network.

Evaluation and Improvement of Power System Security with the Application of Machine Learning

The electricity grid has added many renewable and non-renewable energy sources to meet expanding demand. Sudden load variations exacerbate generator, transmission line, and distribution network issues. Load modelling choices are crucial for system prediction. This study indicates that ZIP load models with contingency criteria can accurately forecast load behaviour over time. The NR method predicts the contingency ranking with the High Bride Line Stability Ranking Index (HLSRI) under single line outage conditions, and an artificial neural network (ANN) is trained to predict the severity of the line outage and the system's behaviour. A mathematical model was utilised to analyse stability and cost with and without the UPFC and IPFC. Machine learning (ML) is used to rapidly predict the most affected transmission line during a contingency by clustering data using the J48 algorithm for the location of compensating devices. The PSO algorithm is used to develop an objective function to minimise fuel costs by maximising generating capacity. A transmission line failure and load variation might damage the electrical system. This study prioritises transmission line breakdowns and load changes. Power system security analysis provides power system status.

Predicting dynamic stability from static features in power grid models using machine learning.

A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronization events after line failures with an average precision greater than 0.996 when averaging over all datasets. Learning transfer between different datasets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronization is essentially governed by only a few network metrics that quantify the networks' ability to reroute the flow without creating exceedingly high static line loadings.

Review on the Application of Machine Learning Algorithms on Smart Grid Optimization

With the increasing challenge of distributed and renewable energy sources, maintaining the stability of the power grid is becoming increasingly difficult. By incorporating information and communication technologies, along with machine intelligence, the conventional power grid has the potential to evolve into a smart grid. The integration of machine learning equips the smart grid to make its decisions and efficiently handle generation, power outages, transmission line failures, unforeseen shifts in customer demands, overall fluctuations in renewable energy production, or any unexpected catastrophic events. These diverse machine-learning algorithms play a crucial role in enhancing the functionality of smart grids, contributing to their optimization. This article examines various machine learning algorithms and approaches designed to optimize the responsiveness of each facet of smart grid optimization.

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Study on forecasting model of transmission line failure rate and outage time

Given the complex problem of transmission line failure and outage and the difficulty of predicting the failure rate and outage time of the transmission line with certainty, the accident prediction information is provided for power grid managers. This paper proposes a three-order fundamental Fourier function fault rate prediction model based on a monthly time scale. This method does not rely on any strict assumptions and is driven by the real transmission line fault data in a province. The fitting results reflect the monthly bimodal fault characteristic distribution law of the transmission line fault rate. On this basis, the historical outage time is analyzed and counted, the probability density distribution characteristics of forced outage time are proposed, and the ExpGro function is used to fit it. The results show that the prediction results of the model proposed in this paper have good sharpness and reliability, which has a guiding significance for actual maintenance work.

Information Monitoring of Transmission Lines Based on Internet of Things Technology

To solve the problem of difficult real-time monitoring of current transmission lines, this article proposes an information-based monitoring system for transmission lines based on Internet of Things technology. The system utilizes the characteristics of strong scalability, good fault tolerance, low power consumption, and low cost of the Internet of Things. Taking the ultra-low power consumption MSP430 microcontroller and CC2430 radio frequency module as the core, a line monitoring system based on the Internet of Things is designed. The proposed design uses ZigBee wireless sensor network technology which is powered by solar energy. The collection, transmission, processing and judgment of various environmental parameters of the line are realized. The data information is transferred to the monitoring center of the upper computer through GPRS. When there is an abnormality, it can send a mobile phone short message to the person in charge to feedback the abnormal content in time. The distribution network's load symmetry allowed for the development of several locating procedures. For the three-phase symmetric scheme, the fault location approach based on line supply characteristics was employed, and for the three-phase asymmetric scheme, the fault location technique based on line impedance is proposed. One of the most vital uses for the Internet of Things is in the mitigation of power transmission line failures and disasters. Improved power transmission dependability, less financial loss, and fewer power outages are all possible thanks to the Internet of Things' cutting-edge sensing and communication technology. This research introduced the use of IoT in online monitoring system of electricity transmission line with a focus on the characteristics of the construction and development of smart grid. The results indicated that the system's highest temperature difference is 0.31 C, while the maximum humidity difference is 1.38%. The system increases the safety and manageability of electricity transmission while also fostering the widespread adoption and technical integration of the smart grid and the Internet of Things.

Reliability improvement of power systems using shunt reactive compensation and distributed generation

Due to the increased demand on electric power, power systems have become highly stressed. This has caused the frequent occurrence of cascading failures, where the failure of one line leads to a series of failures causing a system blackout. Adding high speed control of different electrical parameters of the power system can help improve the reliability of the power system and relieve some of that stress. In this research, the effects of adding static VAR compensators (SVCs) and distributed generation units has been studied from a reliability perspective. Since installing this equipment can be expensive, an algorithm has been developed to obtain the optimal bus location to install such devices, such that reliability is improved. Furthermore, Monte Carlo simulation is used to provide a measure for the improvement in system reliability in the presence of transmission line failures. Results show that injecting real and reactive power generally improves system reliability. However, increasing the amount of injection and increasing the number of buses injected to indefinitely does not necessarily enhance the reliability of the system any further. As such, caution must be exercised when deploying SVCs or distributed generation sources when the goal is to improve system reliability.

A DRL-Based Load Shedding Strategy Considering Communication Delay for Mitigating Power Grid Cascading Failure

Successive failures in power transmission lines can cause cascading failures in the power grid, which may eventually affect large parts of the power grid and even cause the power grid system to go down. Collecting and transmitting primary equipment information and issuing load-shedding action commands in the power grid depend on the power communication network. With the help of the power communication network, we can better observe the situation of the power grid in real time and provide a guarantee for the regular working of the power grid. However, the communication network also has the problem of communication delay causing latency in load-shedding action. On the premise of preserving the key physical properties and operational characteristics of the power grid, this paper uses the IEEE 14 and 30 bus systems as examples to establish a direct current (DC) power flow simulation environment. We establish a communication network model based on the power grid topology and the corresponding communication channels. For the problem of cascading failures occurring in the power grid after transmission line failures, a load-shedding strategy using soft actor-critic (SAC) based on deep reinforcement learning (DRL) was developed to effectively mitigate cascading failures in the power grid while considering the impact of communication delay. The corresponding communication delay is obtained by calculating the shortest communication path using the Dijkstra algorithm. The simulation verifies the feasibility and effectiveness of the SAC algorithm to mitigate cascading failures. The trained network can decide on actions and give commands quickly when a specific initial failure is encountered, reducing the scale of cascading failures.

Enhancing power system security using soft computing and machine learning

Purpose. To guarantee proper operation of the system, the suggested method infers the loss of a single transmission line in order to calculate a contingency rating. Methods. The proposed mathematical model with the machine learning with particle swarm optimization algorithm has been used to observe the stability analysis with and without the unified power flow controller and interline power flow controller, as well as the associated costs. This allows for rapid prediction of the most affected transmission line and the location for compensation. Results. Many contingency conditions, such as the failure of a single transmission line and change in the load, are built into the power system. The single transmission line outage and load fluctuation used to determine the contingency ranking are the primary emphasis of this work. Practical value. In order to set up a safe transmission power system, the suggested stability analysis has been quite helpful.

Production and an advanced model for electrical conductivity of silicone rubber/carbon nanotube nanocomposites as outdoor insulator housing material

Considerable cause of the condensation mechanism of moisture and fog on the contaminated high-voltage insulator surface is the temperature difference between the insulator surface and the dew point which eventuate owing to the mechanism of radiative cooling. Condensation of moisture leads to creating a conducting path on the insulator surface and make the insulator to be a conductor and results in the electrical transmission line failure. In order to cover the above-mentioned temperature difference, carbon nanotubes (CNTs) are being applied to the silicone rubber which is the insulator housing material. This study is based on joule heating generation which decrease the electrical resistance of the high-voltage insulator surface. An advanced mathematical model which is based on Pukanszky formulation for estimation of composite tensile strength was presented for the effectual electrical conduction of silicone rubber - carbon nanotube nanocomposite (SCNT). SCNT samples were produced by the addition of dissimilar CNT concentrations to a high temperature vulcanizing (HTV) silicone rubber by solution-blending method. Comparison between experimental results and theoretical forecasts showed an excellent agreement. The obtained model disclosed that the lower ranks of tunneling resistivity and distance, the higher magnitudes of tunneling diameter, the higher concentration of longer and thinner CNTs, lower CNT curliness, higher fraction of networked CNTs, and deeper interphase can produce the maximum level of SCNT conductivity.

A virtual synchronous generator control method for remote island power system considering dynamic voltage stability

Remote island power systems are difficult to install renewable energy sources (RES) because of the small system capacity and the large impact of intermittency of RES. Virtual synchronous generators (VSG) can equip inverters with the inertia of synchronous generators and can suppress frequency and voltage fluctuations. This paper proposes a VSG control method for dynamic voltage stability in remote island power systems. In conventional VSG control methods, the command of active and reactive power is determined by the Droop characteristics of frequency and transmission line voltage. However, the command are not based on dynamic voltage stability indices. Therefore, the compensation amount of active and reactive power is inappropriate. The proposed method determines the command of active and reactive power based on dynamic voltage stability indices. The dynamic voltage stability index considers the critical boundary index (CBI). The CBI is defined as the distance between the operating point of active and reactive power flowing through a transmission line and the stability limit curve calculated from the power flow between two bus lines. The proposed method improves dynamic voltage stability by determining power command so that the operating points of active and reactive power are closer to the origin direction. Simulation verification is performed for load increase and three-phase ground fault. In the three-phase ground-fault simulation, FRT requirements are considered to verify continued operation and the case of gate blocking. The conventional Droop control and the proposed method are compared for the case of continued operation with FRT requirements during load increase and ground fault. Simulation verification results show that the proposed method improves the dynamic voltage stability compared to the conventional method. Furthermore, it is shown that the proposed method is capable of stable continuous operation of the system even after a transmission line failure considering the FRT requirement.