Safe Operation Of System Research Articles

On-Line Insulation Monitoring Method of Substation Power Cable Based on Distributed Current Principal Component Analysis

Monitoring the insulation condition of power cables is essential for ensuring the safe and stable operation of the substation power supply system. Leakage current is an important indicator of insulation performance of power cables. However, the application of leakage current monitoring methods in substations is limited due to issues such as neutral line shunting on the load side and the spatial isolation of the phase-to-neutral line in the power cabinet. This paper proposes an insulation monitoring method based on distributed current principal component analysis for power cables in substations. Firstly, the leakage current of substation power cable is measured by a distributed current extraction method, and the cable insulation condition is preliminarily judged. Then, considering the problem of measurement error interference in the process of distributed current synthesis, an evaluation method of power cable insulation state based on principal component analysis of distributed current is proposed. To verify the feasibility of the proposed method, both simulation and laboratory tests were conducted. The results indicate that the proposed method can effectively measure the leakage current of power cables in substations and realize the accurate distinction between measurement error and cable insulation degradation characteristics. The method offers a novel idea for insulation monitoring of substation power cables.

PictureGuard: Enhancing Software-Defined Networking–Internet of Things Security with Novel Image-Based Authentication and Artificial Intelligence-Powered Two-Stage Intrusion Detection

Software-defined networking (SDN) represents a transformative approach to network management, enabling the centralized and programmable control of network infrastructure. This paradigm facilitates enhanced scalability, flexibility, and security in managing complex systems. When integrated with the Internet of Things (IoT), SDN addresses critical challenges such as security and efficient network management, positioning the SDN-IoT paradigm as an emerging and impactful technology in modern networking. The rapid proliferation of IoT applications has led to a significant increase in security threats, posing challenges to the safe operation of IoT systems. Consequently, SDN-IoT-based applications and services have been widely adopted to address these issues and challenges. However, this platform faces critical limitations in ensuring scalability, optimizing energy consumption, and addressing persistent security vulnerabilities. To overcome these issues, we proposed a secure SDN-IoT environment for intrusion detection and prevention using virtual blockchain (V-Block). Initially, IoT users are registered and authenticated to the shadow blockchain nodes using a picture-based authentication mechanism. After that, authenticated user flows validation was provided by considering effective metrics utilizing the Trading-based Evolutionary Game Theory (TEGT) approach. Then, we performed a local risk assessment based on evaluated malicious flows severity and then the attack graph was constructed using an Isomorphism-based Graph Neural Network (IGNN) model. Further, multi-controllers were placed optimally using fox optimization algorithm. The generated global paths were securely stored in the virtual blockchain Finally, the two agents in the multi-controllers were responsible for validating and classifying the incoming suspicious flow packets into normal and malicious packets by considering the operative metrics using the Dueling Deep Q Network (DDQN) algorithm. The presented work was conducted by Network Simulator-3.26 and the different performance matrices were used to itemize the suggested V-Block model based on its malicious traffic, attack detection rate, link failure rate, anomaly detection rate, and scalability.

Network Submission and Monitoring of Power System Operation Safety Monitoring Data using Safe Power Hybrid Classifier (SPHC)

Network Submission and Monitoring of Power System Operation Safety Monitoring Data using Safe Power Hybrid Classifier (SPHC)

A novel arc detection and identification method in pantograph-catenary system based on deep learning

Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accuracy and high computational complexity, especially in complex operational environments. This limitation is particularly problematic in real-time monitoring and the efficient operation of power systems. In order to solve these problems, this paper proposes a method of arc detection based on deep learning, called arc multi-scene detection (ArcMSD), which leverages deep learning techniques to address these challenges. The primary distinction of this method lies in its enhancement of the Inception V3 model. This paper has redesigned the original Inception module by incorporating a guided anchor mechanism, an attention mechanism, and upsampling techniques to optimize detection performance. The improved Inception V3 network uses an attention mechanism to allow the model to focus on arc features in complex backgrounds, which can also prevent the model from overfitting. It performs upsampling and fusion with low-level features in the model. The fused features have better arc discrimination capabilities than the original input features, which better improves the accuracy of the model. In order to adapt to arcs with large size differences and improve detection efficiency, the guided anchor is selected to adjust the anchor generation algorithm. In terms of dataset, continuous frame images are intercepted from the video of Integrated Supervision and Control System (ISCS), and image preprocessing operations are performed to improve the model’s detection accuracy of pantograph arcs. Experimental results show that the mean Average Precision (mAP) of the deep learning model proposed in this article is 95.4%, which is far better than other models, thus verify the method’s efficacy.

A practical and online trajectory planner for autonomous ships’ berthing, incorporating speed control

Abstract Autonomous ships are designed and equipped to perceive their internal and external environments and subsequently take appropriate actions based on predefined objective(s) without human intervention. Consequently, trajectory-planning algorithms for autonomous berthing must consider factors such as system dynamics, ship actuators, environmental disturbances, and operational safety. In this study, trajectory planning for an autonomous ship was modeled as an optimal control problem (OCP), which was transcribed into a nonlinear programming problem (NLP) using direct multiple shooting. To enhance berthing safety, wind disturbances, speed control guidelines, actuator limitations, and collision avoidance features were incorporated as constraints in the NLP, which was then solved using the sequential quadratic programming (SQP) algorithm in MATLAB. Finally, the performance of the proposed planner was evaluated through (i) comparison with an existing method, (ii) trajectory planning for different harbor entry and berth approach scenarios, and (iii) a feasibility study using predefined as well as stochastically generated initial conditions. The simulation results indicate improved berthing safety as well as practical and computational feasibility.

Ultra-short-term Forecasting Study of Power Load in Mega Steel Industry Based on Multi-stage Modeling

Background: In the large-scale steel industry, significant power load variability, especially during processes like steel smelting, poses challenges to power system safety. Although there is an abundance of research and patents related to load forecasting, studies and patents specifically addressing large industrial load forecasting are sparse. Hence, accurate ultra-short-term load forecasting becomes particularly crucial. Objective: This study proposes an innovative method for ultra-short-term load forecasting to improve prediction accuracy during peak periods and mitigate risks in high-load conditions. Methods: We introduce an LSTM-XGBoost model enhanced by a random forest network and an improved grey wolf optimization algorithm (IGWO) for feature selection and parameter optimization, respectively. Results: Compared to other advanced models, our method demonstrates superior performance across key indicators such as MAPE (1.93%), RMSE (220.81), and R2 coefficient (0.99), and the prediction error is lower during both peak and off-peak periods. For instance, the proposed model achieved a MAPE improvement of over 25% compared to traditional models. Validation with data from multiple time periods confirms the model's accuracy and robustness. Conclusion: The proposed forecasting method effectively tackles load fluctuations in the steel industry, supporting safe and economical power system operations. Future research will aim to further improve peak identification accuracy and enable continuous adaptive learning.

Design of Power Economic Dispatch Method Integrating Differential Evolution and PSO

As energy demand continues to grow and environmental problems become increasingly serious, optimizing the economic dispatch of the power system is crucial to ensuring the sustainability and economic benefits of energy supply. To ensure the safe operation of the power system, reduce power generation costs as much as possible, and develop a method that can adapt to the needs of different power systems, the experiment combines the differential evolution algorithm with the power economic dispatch problem and proposes a method based on improved differential evolution. Electric power economic dispatch method with particle swarm optimization algorithm. The experiment first introduces a moderate interference strategy to appropriately adjust the position of the particles; then combines the local mutation strategy to enhance the searchability of the differential evolution algorithm in the solution space and achieve good economic dispatch. The results show that when running on the F11 test set and F21 test set, when the system iterates to the 26th and 32nd times respectively, the loss function of the method constructed in the experiment begins to have a minimum value and remains stable thereafter. In addition, on the F11 test set, when the number of iterations is 150, this method has a minimum time of 0.153s. While running the loop for the first time on System 1, the total cost of this approach was only $1.01× 104. Through the actual operation of power generation equipment, under the operation of this method, the power system can ensure optimal operating power of each power generation equipment unit based on ensuring optimal cost. It can be seen from the above results that this method provides power system operators and decision-makers with a new tool to help them maximize cost-effectiveness while ensuring system stability and meeting power demand. In addition, the method's superior convergence and stability can effectively improve the solution's accuracy and speed and has strong practicality and promotion value.

YOLOv7-CWFD for real time detection of bolt defects on transmission lines

Detecting bolt defects on transmission lines is crucial for ensuring the safe operation of the electrical power system. However, existing methods for detecting bolt defects on transmission lines require higher detection accuracy and smaller model sizes. To address these challenges, this paper proposes a real-time bolt defect detection model based on YOLOv7, named YOLOv7-CWFD. The model integrates the Channel Shuffle Diverse Path Aggregation Network (CSDPAN), significantly reducing computational and parameter complexity while maintaining high detection accuracy. Additionally, weighted Efficient Intersection over Union (EIoU) and Normalized Wasserstein Distance (NWD) loss functions are designed to reduce the network’s sensitivity to object size variations and enhance model convergence in regression tasks. The Fast Fourier Channel Attention Mechanism (FFCAM) is introduced between the backbone and neck fusion networks to mitigate excessive smoothing of detailed information and improve the network’s sensitivity to objects. The DySample upsampling operator is implemented to replace the upsampling module in the neck fusion network, minimizing information loss during the upsampling process. Experiments conducted on the custom Transmission Line Bolt Defect Dataset (TLBDD) demonstrate a reduction of 10.30MB in model parameter size, along with a 2.30% increase in mean Average Precision (mAP) compared with the original YOLOV7 and a detection speed of 51.15 frames per second (FPS). Experiments on the public dataset CCTSDB further confirm the model’s robust generalization capability. These experiments validate the effectiveness of the proposed algorithm.

Flexible switching method of control strategy for MMC-HVDC converter based on AC power grid strength

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) has been widely used in the large-scale transmission of renewable energy sources (RESs). However, the operating conditions of MMC-HVDC system with RESs are complex and variable, making it challenging to apply a single converter control strategy to different operating conditions, which affects the safe and stable operation of the whole system. In this paper, a flexible switching method of control strategies is proposed for MMC-HVDC converter. Firstly, the state-space model of MMC-HVDC under different control strategies is established, and the small signal stability and stable operation ranges under these control strategies are analyzed. Then, according to the proposed control strategy switching principle, a flexible control switching method for MMC-HVDC converter is proposed. This flexible control switching method ensures a smooth switching between different control strategies and causes a minimal disturbance to the overall system. Finally, a simulation model is established on the PSCAD/EMTDC platform, and the feasibility of the flexible control switching strategy for the MMC-HVDC system is verified. The simulation results show that very small disturbance is observed during the control switching and the flexible control switching strategy can effectively maintain the stable operation of MMC-HVDC system under different operating conditions. The proposed flexible control switching method can be activated according to the change of AC grid strength and it will help improve the stable operation of MMC converter.

A Novel Fault Location Method Based on ICEEMDAN-NTEO and Ghost-Asf-YOLOv8

Background: The rapid growth of distribution grids and the increase in load demand have made distribution grids play a crucial role in urban development. However, distribution networks are prone to failures due to multiple events. These faults not only incur high maintenance costs, but also result in reduced productivity as well as huge economic losses. Therefore, accurate and fast fault localization methods are very important for the safe and stable operation of distribution systems. Methods: Firstly, the Ghost-Asf-YOLOv8 network is employed to assess the three-phase fault voltage travelling waveforms at both ends of the line, determine the temporal range of the fault occurrence, and differentiate its line mode components. Subsequently, the ICCEMDAN algorithm is employed to decompose the line mode components, thereby yielding the IMF1 components. The key feature information is then enhanced through the application of NTEO. Finally, the Ghost-Asf-YOLOv8 network is employed to further narrow down the time range of the initial traveling wave head, thereby enabling the calculation of the fault location and the determination of the traveling wave arrival time. Results: Experiments are conducted based on the simulation data of the constructed hybrid line model, and the comparison experiments between the TEO algorithm and the NTEO algorithm are conducted, which show that the NTEO has good noise immunity when applied to fault localization. In addition, the proposed ICCEMDAN-NTEO method is also compared with the fault localization methods based on DWT and HHT, and the results show that the method has high accuracy. Finally, the light weighted YOLOv8 model captures the traveling wave time quickly and accurately to compensate for the shortcomings of the visualization data. Conclusion: This work presents a novel fault localization method that integrates traditional and artificial intelligence techniques, offering rapid detection and minimal localization error.

Abnormal temperature rise and discharge characteristics of decay-like composite insulators

Abstract In recent years, there have been multiple incidents of fracture faults caused by the deterioration of composite insulators in China, seriously threatening the safe operation of the power system. This article takes a 500 kV decay-like composite insulator collected from the operation site as the research object, conducts visual inspection and analysis of its shed and core rod degradation status, and tests its infrared temperature rise and ultraviolet discharge distribution under power frequency operating voltage and related electrical parameters. Finally, an electric thermal coupling finite element model is established to analyze its discharge and tem-perature rise mechanism. The research results found that the infrared temperature rise characteristics and ultraviolet discharge characteristics of decay-like composite insulators can reflect their degradation status. As the testing distance increases, the measured infrared temperature rise gradually decreases. The distortion of dielectric parameters and the decrease in insulation resistance have caused partial discharge and abnormal temperature rise of decay-like insulators. In addition, compared to ultraviolet imaging detection, infrared temperature measurement can better reflect the internal defects of decay-like composite insulators and is less susceptible to interference from unrelated discharges around them.

Research on the spatial state and electrical distance variation of overhead transmission lines with changes in foundation

This article conducts a thorough investigation into the spatial state variations of overhead lines in response to foundational changes, as well as alterations in the electrical distance between the conductor and both the ground and tower structures. These changes are crucial for maintaining the stability and safety of the power system. By establishing an appropriate coordinate system and analyzing how tower sinking affects the position of overhead lines, this study examines the combined effects of multiple factors on the position of overhead lines. These factors include the degree of tower sinking, the elasticity and tension of the overhead lines, the positions and heights of adjacent towers, wind force, temperature, and other environmental elements. A mathematical model was developed to describe the positional changes of overhead lines following tower sinkage, and formulas were used to calculate the new suspension point positions and electrical distance variations of the wires. In addition, the effects of wire oscillations due to wind forces on the electrical clearance at tower heads and the impact of insulator string deviation on wire stability and safety were analyzed. Finally, a series of measures were proposed to ensure the safe operation of the power system.

Preparation and mechanical-electrical characterization of new complex-phase conductive concrete

The transmission line tower foundation is the key to determining the safe and stable operation of transmission lines under high current impact, and conductive concrete plays a vital role in the safe and stable operation of transmission lines. However, the mechanical and electrical properties of conductive concrete have a competitive relationship, and balancing these properties simultaneously is a current research problem. This paper proposes a new method for the preparation of novel multiphase electrically conductive concrete fiber composites and elucidates the mechanism of its electrical resistivity and compressive and flexural properties. The effects of concrete, graphite and stainless steel fibers as conductive phase materials on the changing law of physical properties of conductive concrete were compared, and the optimal ratio of the content of conductive phase materials was explored. A numerical calculation model of multi-field coupling of a new type of conductive concrete under high current impact was established. The results demonstrate that when the graphite content is 20% and the stainless steel fiber content is 2.75% ± 0.1%, the mechanical and electrical properties ratio is the best. The resistivity was reduced by 54%, the compressive strength was increased by 47.4%, and the flexural strength was increased by 151.4% compared to graphite conductive concrete. The new conductive concrete has promising frontiers of steady-state electricity and transient heat under 20 kA high current impingement, with its current density value dropping to 3280A/m3 and the maximum module temperature dropping to 2270 K. Relevant research provides a theoretical reference for guaranteeing the safe operation of power systems.

Research on reachable set boundary of neutral system with various types of disturbances.

This study delves into neutral-type systems (NTSs), emphasizing the critical role of defining precise reachable set (RS) boundaries for safe and efficient system design and operation. The investigation notably addresses the challenges posed by time-varying delays, applying Lyapunov's direct method alongside advanced matrix inequality techniques to identify minimized and more accurate ellipsoidal boundaries of the RS in NTSs influenced by bounded and nonlinear disturbances. Our findings, verified through numerical simulations and comparisons with existing literature, demonstrate enhanced control and management capabilities for complex systems, thus underscoring the substantial theoretical and practical value of incorporating delay elements in NTSs.

A CNN‐LSTM Method Based on Voltage Deviation for Predicting the State of Health of Lithium‐Ion Batteries

ABSTRACTEnsuring the accurate estimation of the state of health (SOH) of lithium‐ion batteries (LIBs) is essential for the reliability and safe operation of battery management systems. The prediction of SOH has witnessed significant advancements recently, largely propelled by the powerful nonlinear modeling capabilities of deep learning. Despite these advancements, the intricate nature of the battery degradation process poses a challenge in accurately simulating it using measurement data. In this paper, we introduce a novel approach by focusing on the charging voltage deviation, which is defined as the discrepancy between the charging voltage and its average value over each charge/discharge cycle. This deviation is rooted in the electrochemical reactions that lead to capacity decay and voltage fluctuations. We propose a convolutional neural network‐long short‐term memory (CNN‐LSTM) hybrid framework aimed at estimating the SOH of the battery. For each charge/discharge cycle, a conventional CNN is employed to extract key capacity features from sequential charging data, encompassing voltage deviation, current, and charging duration. Following this, an LSTM network is leveraged to build the long‐term dependencies of battery capacities, facilitating the SOH prediction process. The experimental results indicate that our model not only simplifies the computational complexity but also significantly enhances the precision of SOH predictions. This innovative approach holds promise for the advancement of battery management systems, ensuring their continued reliability and safety.

Wildfire Risk Assessment to Overhead Transmission‐Line Based on Improved Analytic Hierarchy Process

ABSTRACTThe occurrence of wildfire disasters can easily trigger tripping in overhead transmission‐line, thereby posing a significant threat to the safe and stable operation of the power system. In order to enhance the wildfire prevention and control capability of overhead transmission‐line, a wildfire risk assessment method based on the improved analytic hierarchy process (AHP) is proposed. First, the main factors of wildfire occurrence are explored, and the wildfire risk indicator system for overhead transmission‐line is constructed. We propose a novel runaway coefficient of fire for assessing the impact of fire sources on wildfire disaster. Secondly, the mutual information method is used to avoid the subjective arbitrariness of AHP to improve the reliability of each index weight. The results show that about 82.14% of new fire events of 2023 in Fujian (China) are located in medium‐, high‐, and very‐high‐risk areas, demonstrating the effectiveness of the proposed method. This methodology offers a foundation for the power system to mitigate the risk of wildfire.

Renovation of JGC-30 Coal Feeding Belt in Thermal Power Plant

The coal feeder of our company's pulverizing system adopts JGC-30 weighing metering coal feeder. The raw coal in the raw coal hopper falls onto the coal feeder belt through the coal drop port by its own gravity. The existing design of high belt skirt on both sides is 40mm. A small amount of coal blocks falls off on both sides of the belt during the falling process. Due to the wind force of the coal feeder sealing wind and the operation of the belt, a small amount of coal blocks are scattered and stuck at the coal feeder weighing device. The inaccurate coal quantity measurement leads to large fluctuations in the coal feeding quantity of the pulverizing system, that will makes inducing abnormal vibration of the coal mill, insufficient coal powder output, large amount of slag discharge from the coal mill and other problems, which seriously affect the safe and stable operation of the pulverizing system. In this regard, the coal feeding belt was modified, and the skirts on both sides of the coal feeder were increased to 60mm. After the modification, it can be ensured that the raw coal does not fall from the skirts on both sides during the operation of the coal feeder, thereby solving the problem of inaccurate coal quantity measurement caused by coal blocks stuck in the weighing device. This study aims to address the problem by modifying the coal feeder belt, namely by increasing the skirt height on both sides of the belt from 40mm to 60mm. The method applied was to adjust the belt skirt design directly on the operating coal feeder, followed by operational testing to evaluate the effectiveness of the modification in preventing coal falling from the belt side. Results show that after increasing the height of the skirt belt, there is no more coal falling during coal feeder operation. Coal measurement became more accurate, which had a positive impact on coal feeding stability, reduced vibration in the coal mill, as well as increased fine coal output and reduced the amount of slag produced.

Analysis and Enlightenment of Power System Massive Blackout Accidents

The occurrence of massive blackouts has had a severe impact on both the social-economic fabric and the daily lives of the public. This paper analyzes massive blackout accidents worldwide to explore their primary causes, including natural disasters, equipment issues, imperfect management mechanisms, and human factors. The research indicates that natural disasters are the leading cause of massive blackouts, accounting for over 52% of cases. Equipment issues and human factors also contribute to a significant proportion. To ensure the safe and stable operation of power systems, this paper proposes targeted recommendations, including enhancing the resilience of power systems, improving management mechanisms, strengthening protections against human-induced incidents, and bolstering cybersecurity defenses. These measures aim to draw on lessons from past accidents while improving the overall risk resistance ability of power systems to better address potential future power crises.

Strategy Deduction for Improving Aviation Emergency Rescue Capability from the Perspective of Public Safety

Aviation emergency rescue plays a crucial role in the emergency management of various emergency events. Consequently, the processes of identifying relevant constraints of aviation emergency rescue ability, analyzing the interactive relationship among factors, and putting forward an economical and effective improvement strategy for aviation emergency rescue capabilities have become widely popular among scholars. To greatly improve the economic input efficiency of aviation emergency rescue capabilities, 18 relevant influencing factors were screened and identified from four perspectives: system institutionalization, technological advancement, rescuer professionalism, and operation safety. Then, the causal relationship among the influencing factors and the feedback mechanism were analyzed via the system dynamics (SD) method, and the influencing factor weights were calculated via the entropy weight method. Then, the equations of state variables and constants among the influencing factors were determined, and an SD model of aviation emergency rescue was built. Finally, a case study based on a practical construction condition of an aviation emergency rescue in Henan Province, China was analyzed. The variation trend of rescue ability was predicted from the temporal dimension, and the optimal economic input strategy for effectively improving the rescue ability was determined. Results demonstrate that the efficacy level of aviation emergency rescue ability in Henan Province is 44.97. The aviation rescue ability level on the 49th month can be improved to 90 if the monthly input into the aviation emergency rescue is raised to 3 million RMB. Additionally, the aviation rescue project can be significantly improved if the input proportions of operation safety, rescuer professionalism, system institutionalization, and technological advancement are 0.4, 0.2, 0.2, and 0.2, respectively. This work can provide an economic decision reference for building and perfecting public safety systems.

How Will Concrete Piles for Offshore Wind Power Be Damaged Under Seawater Erosion? Insights from a Chemical-Damage Coupling Meshless Method.

Based on the background of the continuously rising global demand for clean energy, offshore wind power, as an important form of renewable energy utilization, is booming. However, the pile foundations of offshore wind turbines are subject to long-term erosion in the harsh marine environment, and the problem of corrosion damage is prominent, which seriously threatens the safe and stable operation of the wind power system. In view of this, a meshless numerical simulation method based on smoothed particle hydrodynamics (SPH) and a method for generating the concrete meso-structures are developed. Concrete pile foundation models with different aggregate contents, particle sizes, and ion concentration diffusion coefficients are established to simulate the corrosion damage processes under various conditions. The rationality of the numerical algorithm is verified by a typical example. The results show that the increase in the aggregate percentage gradually reduces the diffusion rate of chemical ions, and the early damage development also slows down. However, as time goes, the damage will still accumulate continuously; when the aggregate particle size increases, the ion diffusion becomes more difficult, the damage initiation is delayed, and the early damage is concentrated around the large aggregates. The increase in the ion diffusion coefficient significantly accelerates the ion diffusion process, promotes the earlier and faster development of damage, and significantly deepens the damage degree. The research results contribute to a deeper understanding of the corrosion damage mechanisms of pile foundations and providing important theoretical support for optimizing the durability design of pile foundations. It is of great significance for ensuring the safe operation of offshore wind power facilities, prolonging the service life, reducing maintenance costs, and promoting the sustainable development of offshore wind power.