Operation Of Power Transmission Research Articles

ID-Det: Insulator Burst Defect Detection from UAV Inspection Imagery of Power Transmission Facilities

The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.

Enhancing Automation with Label Defect Detection and Content Parsing Algorithms

The stable operation of power transmission and distribution is closely related to the overall performance and construction quality of circuit breakers. Focusing on circuit breakers as the research subject, we propose a machine vision method for automated defect detection, which can be applied in intelligent robots to improve detection efficiency, reduce costs, and address the issues related to performance and assembly quality. Based on the LeNet-5 convolutional neural network, a method for the detection of character defects on labels is proposed. This method is then combined with squeezing and excitation networks to achieve more precise classification with a feature graph mechanism. The experimental results show the accuracy of the LeNet-CB model can reach up to 99.75%, while the average time for single character detection is 17.9 milliseconds. Although the LeNet-SE model demonstrates certain limitations in handling some easily confused characters, it maintains an average accuracy of 98.95%. Through further optimization, a label content detection method based on the LSTM framework is constructed, with an average accuracy of 99.57%, and an average detection time of 84 milliseconds. Overall, the system meets the detection accuracy requirements and delivers a rapid response. making the results of this research a meaningful contribution to the practical foundation for ongoing improvements in robot intelligence and machine vision.

Development of a simulation model for assessing the technical condition of the magnetic circuit of oil power transformers by measuring the temperature of the tank and the external environment

In the article, the authors analyzed how oil-filled power transformers ensure reliable and stable operation of power transmission systems by providing efficient transmission of electricity of different voltages. In the article, the authors found that the main elements of oil-filled power transformers - the magnetic circuit, the coil and the technical condition of the oil - directly determine the performance of oil-filled power transformers. In this work, a simulation model was developed to evaluate the magnetic circuit of power oil transformers based on the measured temperature of the tank and the external environment during express diagnosis. A simulation model has been developed that allows to calculate the technical condition of oil power transformers operating in city power networks. It is shown that the developed model allows to calculate the value of the technical condition of the oil power transformer with the help of diagnostic parameters. In order to represent the technical state of the magnetic circuit of the oil power transformer, the functions of changing the state value of the output variable in the Mamdani fuzzy logic method are shown and the block diagrams are drawn up. Author’s using the Mamdani fuzzy logic method, express diagnosis of the technical condition of the “Magnetic core of the oil power transformer” was carried out in the Matlab simulink program. During the evaluation, the value of the output parameter is taken from 0 to 100, and each step is divided by 10, the smallest value is 0, and the largest is 100.

Reinforcement-Learning-Based Proactive Control for Enabling Power Grid Resilience to Wildfire

Industrial electric power grid operation subject to an extreme event requires decision-making by human operators under stressful conditions. Decision making using system data informatics under adverse dynamic events, especially if forecasted, should be supplemented by intelligent proactive control. Power transmission system operation during wildfires requires resiliency-driven proactive control for load shedding, line switching, and resource allocation considering the dynamics of the wildfire and failure propagation to minimize the impact on the system. However, the possible number of line and load switching in an extensive industrial system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use pre-planned or greedy algorithms. In this work, we model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. Our approach allows the controller to provide setpoints for all generation fleets in the power grid. We evaluate our approach utilizing the IEEE test system mapped onto a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load outage during an extreme event. It reduces power flow through lines that are to be de-energized, and adjusts the load demand by increasing power flow through other lines.

Study on vegetation restoration technology in ecologically sensitive area of power transmission and transformation project in Sichuan-Chongqing area

In the rapid development of urban construction, China's power transmission and transformation engineering construction scope is more and more wide, in order to better cope with the opportunities and challenges proposed by the development of The Times, the National Grid Co., Ltd. technical research team in the proposed a number of key technologies at the same time, mastered a lot of theoretical knowledge and practical experience, which for the new period of ecological environmental protection laid the foundation. Especially for the Sichuan-Chongqing area, the vegetation restoration technology must be considered in the construction management of power transmission and transformation projects during the construction of ecologically sensitive areas, only in this way can the construction requirements of safe production and green environmental protection be met. On the basis of understanding the construction management requirements of power transmission and transformation projects in Sichuan-Chongqing area, and according to the construction status of ecologically sensitive areas during the construction period, I mainly study the construction identification system and construction evaluation system of projects in ecologically sensitive areas, and propose effective vegetation restoration technology according to the requirements of construction sites, so as to ensure the normal operation of power transmission and transformation projects in Sichuan-Chongqing area.

Study on ecological environment impact and soil and water conservation technology of power transmission and transformation projects in typical ecologically fragile areas in western China

In the steady development of social and economic, the field of power transmission and transformation engineering construction in our country gradually forms a new electric power development pattern, providing a solid foundation for the innovative development of electric power enterprises in a new era. Since power transmission and transformation projects will cause serious soil and water loss, which will have a direct impact on the stability of ecological environment in various regions, which is not conducive to the construction and operation of power transmission and transformation projects and the long-term development of social economy, how to construct a safe and standardized construction environment, scientifically deal with the ecological environment problems of power transmission and transformation projects, and rationally use soil and water conservation technology? It is the main problem that Chinese scholars study and discuss at present. On the basis of understanding the environmental status of typical ecologically fragile areas in western China, and according to the development of soil and water conservation technology in power transmission and transformation projects, this paper mainly studies the online monitoring system and application technology of soil and water conservation for power transmission and transformation projects in a certain area, so as to provide effective basis for the construction management of power transmission and transformation projects in the new era.

Multidimensional heterogeneous data clustering algorithm for power transmission and transformation equipment

Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times.

An improved rigid rod method for wind-induced swing response prediction of heavily iced transmission lines

Wind-induced swing of overhead transmission lines may cause flashover and line tripping, which will greatly jeopardize the normal operation of power transmission system and give rise to great economy loss. This paper analyzed a swing flashover accident on a practical high-voltage (HV) transmission line and calculated the swing angle of the suspension insulator string using the rigid rod method recommended by the Chinese standard. The aerodynamic coefficients of the iced HV transmission lines were obtained through high frequency force balance wind tunnel tests. An improved rigid rod method was then developed for predicting wind-induced swing response of heavily iced transmission lines in mountainous terrain under severe icy weather conditions. Finally, the improved rigid rod method was also extended to deal with general transmission lines with light ice accretion or regular ice shapes.

WACC for Electric Power Transmission System Operators: The Case of Colombia

In emerging countries, energy service users generally pay high rates of return to transmission system operators (TSOs). One of the causes of this situation is the application of the CAPM with the comparable beta method when estimating the cost of equity in the WACC. The purpose of this article is to present a new methodology for calculating the cost of equity of TSOs in Colombia. To achieve this objective, a multifactor model has been built to explain the variation in returns on the electric utilities portfolio in Colombia between April 2008 and March 2022 and then recalculate the WACC approved by the country’s regulatory commission. It was found that, in addition to the estimated market risk in the CAPM, there is a risk due to the regulatory framework and changes in interest rates, which helps to explain 73% of the variations in the electric utilities portfolio, resulting in a lower cost of equity, and therefore a lower WACC of 5.28% compared to the WACC estimated by the regulator of 11.79% in 2019. These results can support regulatory commissions in emerging countries in establishing a more accurate rate of return for users.

Sustainable Supply Chain Management of Electric Grid Power Consumption Load for Smart Cities Based on Second-order Exponential Smoothing Algorithm

Electric grid power consumption load is one of the fundamental areas that need to be faced to provide a sustainable and green ecosystem in smart cities. Consumption load as well as supply and availability of electricity to suppliers and customers is a major issue to be faced to have a balanced smart city power grid infrastructure. Balancing in this case is assumed as a well-designed supply chain management system to be applied in the Smart City (SC) of Athens, Greece. Core of such a system is the knowledge of electric power consumption load per weekly basis of a year, that is the granularity of the proposed system is one week of the system’s operation. In this paper, focus is given on the electric load forecast component of an Energy Management System (EMS) such as the Independent Power Transmission Operator (ITPO) of Greece. Concretely, stochastic data of electric energy consumption load are used to predict the demand or offering of electric power in the future. This is achieved by incorporating a machine learning second-order exponential smoothing algorithm. Such an algorithm is able to speculate near or far in the future power consumption load thus providing a promising parameter to predict smart city needs for electric power in the future. Adopted system is evaluated by the evaluation metric of Normalized Root Mean Square Error (NRMSE), which assures that the system can be used for future predictions of electric power consumption load in smart cities.

Robust disturbance classification in power transmission systems with denoising recurrent autoencoders

The automated classification of grid disturbances based on phasor measurements is a key technology for the reliable operation of power transmission systems. The predominant use of simulated training data limits the applicability of existing classification approaches due to the missing consideration of measurement errors or data quality issues. To mitigate these shortcomings, this study presents a robust disturbance classification procedure incorporating denoising recurrent autoencoders within a novel two-stage training approach. The developed disturbance classification procedure is evaluated for different noise characteristics and dataset combinations created with an optimization based error model. Experimental results based on a generic power transmission system show superior performance of the proposed two-stage design compared to a conventional, one-stage model training.

Analysis and improvement of measures for lightning protection of 35kV transmission line in a mountainous area

Lightning is an important factor affecting the safe and reliable operation of power transmission and distribution lines. As most power transmission and distribution lines are built in mountainous areas with complex terrain and changeable weather, the lightning accidents in mountainous areas are much higher than those in plain areas. Firstly, the mechanism of lightning damage and common measures of lightning protection are analyzed. Then, taking Lufeng line in Yunnan province as the object, taking the topographic and geomorphic characteristics of the line, the activity intensity of lightning strike and the line structure into consideration, three measures are compared by increasing the number of insulator string pieces, reducing the grounding resistance of poles and towers, and erecting lightning conductor across the line. Finally, erecting lightning conductor across the line is adopted to rectify the line. The feasibility and practicability of the rectification scheme are proved by calculation.

Self-powered sensing of power transmission lines galloping based on piezoelectric energy harvesting

Online monitoring sensors are a feasible solution for conductor galloping that greatly harms the stable operation of power transmission systems. However, power supply that drives sensors has become one of the bottlenecks restricting the development of distributed sensing systems. This work initially proposes harvesting the energy of conductor galloping and joint utilization to assess conductor galloping degree. A swinging piezoelectric energy harvester based on frequency boost conversion is also proposed. The output characteristics of harvester and the physical validation of the scale model of power transmission line galloping are further explored. Experiment results showed a maximum output voltage and current of 29.6 V and 29 µA, respectively, under the characteristic conditions of conductor galloping. The corresponding load capacity of the harvester reached a maximum power output of 155.58 µW under minimum resistance of 70 kΩ at 35 cm vibration amplitude and 1.3 Hz frequency. The conductor galloping testing platform indicated that the frequency boost conversion effect was weakened due to the occurrence of torsion movement during conductor galloping, and the output presented a nonlinear variation with the vibration amplitude and frequency. The degree and direction of conductor galloping can be preliminarily judged according to the output trend, and the maximum output power of load capacity of the harvester reached 101.5 μW under 107 MΩ resistance at 1.3 Hz vibration frequency, validating that the proposed energy harvesting system is promising for self-powered sensing applications in low-power monitoring sensors for conductor galloping.© 2017 Elsevier Inc. All rights reserved.

Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method

A key component of the design and operation of power transmission systems is the optimal power flow (OPF) problem. To solve this problem, several optimization algorithms have been developed. The primary objectives of the program are to minimize fuel costs, reduce emissions, improve voltage profiles, and reduce power losses. OPF is considered one of the most challenging optimization problems due to its nonconvexity and significant computational difficulty. Teaching–learning-based optimization (TLBO) is an optimization algorithm that can be used to solve engineering problems. Although the method has certain advantages, it does have one significant disadvantage: after several iterations, it becomes stuck in the local optimum. The purpose of this paper is to present a novel adaptive Gaussian TLBO (AGTLBO) that solves the problem and improves the performance of conventional TLBO. Validating the performance of the proposed algorithm is undertaken using test systems for IEEE standards 30-bus, 57-bus, and 118-bus. Twelve different scenarios have been tested to evaluate the algorithm. The results show that the proposed AGTLBO is evidently more efficient and effective when compared to other optimization algorithms published in the literature.

Siamese Sigmoid Networks for the open classification of grid disturbances in power transmission systems

AbstractThe online classification of grid disturbances is an important prerequisite for an automated and reliable operation of power transmission systems. Most of the state‐of‐the‐art approaches assume that all classes are already known in the training phase and cannot handle new disturbance events, which appear in the application phase and lead to severe misclassifications. To mitigate this shortcoming, the disturbance detection is investigated as an open classification task and a novel recurrent Siamese neural network architecture is introduced to identify and locate known and unknown disturbance events from phasor measurements. Extending preliminary work, a probabilistic distance‐based classification approach with an integrated rejection mechanism is presented, which enables to learn class‐dependent decision boundaries and margins to reduce the open‐set risk. A detailed performance analysis is presented including multiple benchmark methods in different closed‐set and open‐set classification tasks for a simulated power transmission system. Additionally, a limited and full observability of the grid with phasor measurements are addressed in the experiments.

Power system static and dynamic security studies for the 1st phase of Crete Island Interconnection

The island of Crete is currently served by an autonomous electrical system being fed by oil-fired (Heavy fuel or light Diesel oil) thermal power plants and renewables (wind and PVs). The peak load and annual electric energy consumption are approximately 600 MW and 3 TWh respectively; wind and photovoltaic parks contribute approximately 20% of the electricity needs of the island. Due to the expensive fuel used, the Cretan power system has very high electric energy generation cost compared to the Greek mainland. On the other side the limited size of the system poses severe limitations to the penetration of renewable energy sources, not allowing to further exploit the high wind and solar potential of the island. According to the Ten Year Network Development Plan (TYNDP) of the Greek TSO (Independent Power Transmission Operator S.A. IPTO S.A.), the interconnection of Crete to the mainland Transmission System of Greece will be realized through two links: A 150 kV HVAC link between the Peloponnese and the Crete (Phase I) and a HVDC link connecting the metropolitan area of Athens with Crete (Phase II). The total length of submarine and underground cable of the HVAC link will be approximately 174km; it is at the limits of the AC technology and the longest and deepest worldwide at 150 kV level. A number of studies have been conducted by a joint group of IPTO and Hellenic Electricity Distribution Network Operator (HEDNO) for the design of this interconnection. This paper presents briefly the power system static and dynamic studies conducted for the design of the AC link and its operation. Firstly, the paper presents the main results of the static security study regarding the calculation of the maximum power transfer capability of the link and the selection of the reactive power compensation scheme of the cable. Results from dynamic security analysis studies are also presented. The small-signal stability analysis concludes that a new (intra-area) electromechanical oscillation is introduced to the National System after the interconnection. The damping of the electromechanical oscillations is sufficient; however the operation of power system stabilizers at power plants located both at the mainland and at Crete power system can increase significantly the damping of important oscillation modes. Finally with respect to the risk of loss of synchronism after a significant disturbance in the system of Crete, such as a three-phase fault (“transient stability”)- enough safety margin is estimated by means of Critical Clearing Time calculations.

Combined Network Intrusion and Phasor Data Anomaly Detection for Secure Dynamic Control Centers

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions.

Application of an improved k‐means clustering algorithm in power user grouping

AbstractThe illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional k‐means clustering method has the characteristics of randomly selecting the initial cluster center, which leads to the instability of clustering results. Aiming at the shortcomings of traditional k‐means clustering algorithm, this paper proposes a density based k‐means clustering algorithm (Dk‐means clustering) to optimize the initial center selection. According to the characteristics of users' power load, the method of determining the number of clusters and the evaluation method of clustering effect are selected. Then, the traditional k‐means clustering algorithm and the improved Dk‐means clustering algorithm are used to analyze the power load data of a residential area, and six groups of characteristic curves are obtained. Based on the analysis of these curves, the power consumption characteristics of each group of users were evaluated. Finally, through the comparative analysis of Euclidean distance, Manhattan distance and correlation coefficient r, it is proved that Dk‐means algorithm has better clustering effect and is more accurate for the selection of suspicious power stealing users.

Research on Operation Safety of Power Transmission and Transformation Equipment in Wind Power Plant

Wind power generation is clean renewable power without environmental pollution, so wind power plant has been paid more and more attention, and has been popularized in recent years. As an unstable energy, wind power has high requirements for the operation, management and protection of facilities to ensure the safe operation of the system. In order to improve the power efficiency, it is necessary to conduct in-depth research and Discussion on the operation management and guarantee countermeasures of the current wind power plant equipment. Based on this, this paper deeply discusses the key factors restricting the operation safety of the power transmission and transformation equipment of the wind power plant, and puts forward the safety technical means to enhance the operation safety of the power transmission and transformation equipment. It is expected to have a certain reference significance for the development of the power transmission and transformation equipment of the wind power plant, Thus, it has a positive reference for the management and guarantee measures of the current operation of power transmission and transformation equipment in wind power plants.

Based on the combination prediction method for the characteristic parameters prediction of power transmission and transformation equipment

The safe and stable operation of power transmission and transformation equipment is the foundation of power grid safety, so the prediction of power transmission and transformation equipment fault is particularly important, and the prediction of the characteristic parameters that affect the equipment fault is the core of fault prediction. In this paper, a method for predicting the characteristic parameters of power transmission and transformation equipment based on combination prediction is presented. According to the correlation analysis results of various parameters, the key prediction input data with high correlation degree with the predicted parameters are extracted automatically, and the method library including multiple single-factor prediction models, multi-factor prediction models and combined prediction models is established. The error checking method is used to automatically select the optimal prediction method, and then the characteristic parameters of the parameter are automatically predicted. The test results show that the multi-factor power transmission and transformation equipment fault feature parameter prediction method can improve the accuracy of prediction, and is of great significance to improve the scientific evaluation of equipment health status and the prediction of equipment fault.