Power Substation Equipment Research Articles

Seismic Assessment of Electrical Equipment in Power Substations: A Case Study for Circuit Breakers

Electric power is essential in post-earthquake periods for the continuous functionality of disaster management and emergency services. In addition, interruption of electric power can cause significant economic losses due to downtime of critical facilities. Therefore, it is very important to maintain seismic safety of electric power systems and components. There are existing seismic regulations and standards regarding electric power systems, especially in the United States of America (USA) and Europe. A similar regulation has been prepared recently in Türkiye, which is a country in a seismically active region. This study focuses on the current state of practice regarding the seismic assessment of electrical equipment in power stations and implementation of the regulations on seismic qualification of these systems. Among many electrical equipment, circuit breakers have been selected for case study. The seismic assessment of the selected high voltage equipment has been performed according to the new regulation under the seismic hazard specifically defined for Türkiye. The case study experiment presents the new methodology in evaluating and classifying the seismic response of high voltage electrical equipment and provides insight to the expected behaviour of circuit breakers under earthquake induced action.

A Method for Identifying Power Equipment in Foggy Substation Based on Improved YOLOv4

Abstract In foggy weather, inspection robots detect low visibility and unclear targets in substation power equipment, and the commonly used target detection algorithms have too many parameters, making it difficult to achieve real-time requirements when deployed on inspection robots. To this end, a lightweight substation power equipment identification method combining Cycle Dehaze defogging algorithm is proposed. Pre process the collected fog map of the substation to improve the clarity and visibility of the target power equipment; Introducing MobileNetv3 and deep separable convolution in YOLOv4 network to achieve model lightweight; To increase the precision of power equipment identification, incorporate the SE attention mechanism into the target detection network. Based on the testing results, it is possible to reach a recognition mAP of 92.5% by combining Cycle Dehaze and improved YOLOv4, as the improved YOLOv4 has a detection speed that is 15.59 frames/second faster than the YOLOv4 method. The model parameter quantity has been reduced by 80.21%, which is more conducive to deployment on substation inspection robots.

Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

Abstract Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule’s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure.

Эко-НИОКР – 18 лет для охраны птиц на объектах электросетевого комплекса России

Эко-НИОКР – 18 лет для охраны птиц на объектах электросетевого комплекса России

Analysis of Substation Joint Safety Control System and Model Based on Multi-Source Heterogeneous Data Fusion

As the number of substations continues to increase globally and the market demand continues to rise, the current workload of maintenance and daily operation of substations in power grids cannot meet the current demand if only relying on manual work, and the design and implementation of intelligent safety control solutions for substations is imperative. Therefore, this paper proposes a joint safety control system and model analysis for substations based on multi-source heterogeneous data fusion. Firstly, a three-dimensional visualization substation efficient interactive operation platform is realized, which realizes the functions of substation scene roaming, system login, information management, equipment parameters, status viewing and operation ticket pushing; after that, a variety of intelligent hardware devices for data collection, such as multi-dimensional terminal sensors, intelligent wearable devices, intelligent pre-built positioning installation measure rod, and substation intelligent inspection robots are designed to greatly improve the substation inspection efficiency and realize real-time monitoring and data interaction in the inspection process. Finally, we propose an Attention-LSTM-based prediction model for substation multidimensional data, which can predict power equipment spatio-temporal data in the short term, and the prediction results can be combined with intelligent devices for joint diagnosis. The Attention-LSTM prediction model is well-trained in transformer oil temperature experiments, and the experimental results show that this model can provide early warning for the abnormal state of substation power equipment. In summary, this thesis describes a set of complete and practically feasible intelligent safety control methods for substations. The joint safety control system and model analysis of the substation based on multi-source heterogeneous data fusion designed in this paper is mainly oriented to the substation as an electric power workplace, which has quite a vast application prospect for energy equipment.

Infrared Thermal Image Instance Segmentation Method for Power Substation Equipment Based on Visual Feature Reasoning

Infrared Thermal Image Instance Segmentation Method for Power Substation Equipment Based on Visual Feature Reasoning

Predictive Maintenance of Power Substation Equipment by Infrared Thermography

Predictive Maintenance of Power Substation Equipment by Infrared Thermography

The impact of fusion welding in corrosion diagnosis of joint systems using ultrasonic technique

Nowadays the ultrasonic guide wave technique is the most accurate method in defect detection. Defect detection in the joined and continues systems make some measurement problems due to the structure and propagation of ultrasonic waves in these systems that must be considered for obtained accurate results. In this paper we analysis the manner and the effect of welded points in joint systems. For this aim we specially considered the grounding grids as a joint system and study the problem of the ultrasonic waves in corrosion diagnosis of this system. The grounding grids are the most important systems that increasing safety of power equipment in substations. The conductors of grounding grids usually connected together by fusion welding or bolts and nuts. But fusion welding make some problems for corrosion diagnosis of grounding grids and also pipelines while using ultrasonic technique. The main problem is incorrect results that obtained due to the interference of ultrasonic waves. This paper presented a new method to eliminating the effect of fusion welding points by placing the ultrasonic transducers in right locations. The influence of the soil is considered. Computer simulation results have shown the effect of this interference in accuracy of the result and effectiveness of the new method.

Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map

A fuzzy cognitive map based behavior planning method for mobile robot is proposed in this paper. Aiming at the intelligentialize advanced application problems of power substation equipment inspection robot, a logical structure of inspection tasks planning based on hierarchical hybrid complex system is presented, and the operating principle of task decomposition and task planning are given in detail. The dependent events in inspecting tasks are considered as concept nodes to present the connection weights between nodes and establish the fuzzy cognitive map model of task and behavior planning, so as to realize task decomposition and generate the action logic sequences. Meanwhile, the action sequences can be adjusted in real time with the sensor information. The proposed method is simple without introducing complicated calculations and much training. Simulation results verify the feasibility and effectiveness.

Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method.

Intelligent Diagnosis Method of Power Equipment Faults Based on Single‐Stage Infrared Image Target Detection

With the rapid expansion of the scale of the power grid, the efficiency of fault diagnosis has been severely challenged by the large amount of inspection image data generated by intelligent devices such as drones and inspection robots. In order to improve the efficiency of fault diagnosis for power equipment in substations, a new method for intelligently diagnosing different types of faults in power equipment is proposed. For circuit breakers and insulators, YOLOv4 is selected as the target detection model. To improve the detection performance of the YOLOv4 model, this paper improves it: the Cross Stage Partial (CSP) structure is introduced in the Spatial Pyramid Pooling (SPP) module of the neck of the YOLOv4 model. The experimental results show that after using the optimal learning rate decay strategy, the mAP and frames per second (FPS) of the improved YOLOv4 model are better than the original YOLOv4 and PP‐YOLO model. Finally, an intelligent diagnosis terminal system for power equipment faults is developed. Through the target recognition and rapid extraction of equipment temperature, the intelligent diagnosis of thermal faults of equipment is realized. This method is especially suitable for accurate fault diagnosis of more power equipment, and has potential huge applicability in the field of power equipment diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Novel Spiral-Gap Shielding Shell for a Rogowski Current Sensor

Rogowski current sensors play an important role in detecting the condition of electrical power substation equipment. An electromagnetic field shielding shell is necessary for some of these sensors to operate correctly, especially those with large diameters and small heights. These are used for frequency-response analysis in power transformers. In this study, the magnetic-shielding effectiveness of a normal C-type metal shell was tested and simulated. The distribution and direction of the magnetic field were analyzed using finite-element simulation calculations to reveal the reasons for its poor shielding effectiveness. It was found that the magnetic field induced by an interference current crosses the shell and enters its cavity through the gap in its inner wall. Shells with larger inner-diameter-to-height ratios have worse magnetic-shielding effectiveness. Therefore, a novel shell structure with a spiral gap is proposed here. The magnetic-shielding effectiveness of this new type of shell was analyzed by further simulations and verified in experimental tests. The results showed that a shell with a spiral gap has better shielding effectiveness when it has a long gap. In addition, the spiral gap was found not to inhibit the current-measurement function of the sensor. This provides a new approach to the design of electromagnetic shielding shells for Rogowski sensors.

Fault diagnosis method of substation equipment based on You Only Look Once algorithm and infrared imaging

Aiming at the problems of local temperature increase of cable, heating of insulator porcelain disc, local heating of lightning arresters and transformers in the process of abnormal heat generation fault diagnosis of power equipment. This study proposes a target detection method that YOLOv4 (You Only Look Once version 4), combined with the infrared image data of power equipment to locate and diagnose the faulty heating area of power equipment. A dataset is established, and image annotation is made by detecting infrared image data of typical electrical equipment collected by the inspection department using a thermal imaging camera. Then this study established a target detection model to calculate the degree of overlap between the power equipment area and the fault area, which is used to determine whether the power equipment to be measured has abnormal heat generation. Finally, the optimal detection model was established by analyzing 6000 test samples in the four kinds of power equipment image dataset. The detection results are compared with Faster Region Convolutional Neural Network algorithms, Single-Shot multibox Detectors, and You Only Look Once version 3. The results show that YOLOv4 has an average accuracy rate of 92.2%. The YOLOv4 model established in this study can effectively detect anomalies in the infrared image of power equipment can be effectively applied to infrared detection of power equipment in substations.

A Data-Driven Approach for Electric Energy Equipment Using Wireless Sensing Technology in the Context of Carbon Neutrality

In this paper, we use wireless sensing technology to conduct an in-depth study and analysis of data-driven power energy equipment in the context of carbon neutrality. For the high-order uncertainty of renewable energy power generation and the nonlinearity of the tidal equation, a set of orthogonal bases under arbitrary probability space can be constructed by itself using the high-order information of renewable energy power generation statistics, and then, polynomials fit the state variables such as voltage in the tidal equation using the orthogonal bases and calculate the fitting parameters using the stochastic Gallatin integration method. Based on the analysis of the online monitoring project of the main power equipment in the substation, a substation power equipment condition monitoring system is designed to realize the real-time monitoring of the temperature status of power equipment, substation smoke and temperature, and humidity environment, and the feasibility and advanced of the system are verified by elaborating the analysis. Using wireless transmission to send real-time temperature information to the monitoring background, the infrared thermal imaging online monitoring system is designed around the front-end data acquisition system, transmission network, background data processing, and display module. Saving-investment equilibrium means that total investment equals total savings; government budget balance can be achieved through government savings or deficits. When both supply chain approaches enter into the same environmental competition, the added value that consumers value the product because of the carbon-neutral approach becomes smaller in the coefficient constraint of green effort investment cost due to the competition between the two supply chains, which in turn increases the green investment cost of the supply chain with the green effort carbon-neutral approach, indicating that in the case of competition, consumers, when faced with two products produced by two supply chains, are interested in the product with the carbon-neutral approach has less value-added, and some of the demand is shifted to the product not produced with the carbon-neutral approach.

Design of Remote Monitoring System for Substation DC Power Supply under the Background of Big Data

The DC power supply(DPS) system is an important part of the substation. In order to adapt to the current unmanned operation and maintenance mode of the substation and the new requirements of state maintenance and management, it must develop in the general direction of digitalization, programming and intelligence. To ensure the safety and stability of the new operation and maintenance mode of the power system. Through the design and application research of the DPS remote monitoring system of the substation, the real-time data collection of the domestic DPS device is realized, the collected data information is summarized, and the cooperation of the remote control center and related operation and maintenance management teams is requested. The management software effectively analyzes the collected equipment operating parameters and real-time data, extracts fault information and hidden dangerous data required for operation and maintenance, which is helpful for the scientific management and risk prediction of DC power equipment in substations. The operation and maintenance personnel and the DPS system of the substation ensure the operation in a high-quality and healthy state and the safe and stable operation of the entire power network. This paper studies the design principle, structure and function of the DPS remote monitoring system used in the substation. On this basis, in-depth study of battery voltage equalization technology, internal resistance measurement technology and other core technologies related to DPS remote monitoring system design. In order to realize the online monitoring, analysis and diagnosis of the operation information data of DPS devices such as remote charging and discharging operation safety management strategy, DC power information collection and communication network design, the theoretical and technical foundations have been formulated.

Coupling effects of power substation equipment with multiple configurations

Power substation equipment with multiple configurations use metallic blades to switch to different service configurations. When subjected to earthquakes, a closed blade can produce great coupling effects between two interconnected pieces of the equipment, which substantially affect their dynamic properties and seismic responses. However, knowledge of the coupling effects and their production mechanism is currently insufficient. To investigate coupling effect characteristics, an ultra-high voltage (UHV) disconnect switch with different configurations was tested on a full-scale shaking table. Test results proved that the main blade was a weak connection, which leads inconsistent resonance among different pieces. Furthermore, phase traces of the blade confirm its coupling effects are produced by a nonlinear mechanism. Combined testing results and a numerical calculation were employed to quantitatively estimate the interaction forces raised by the blade. Based on a frequency-domain characteristic analysis, it was found that the nonlinearity was contributed by by the unsteady connection of the blade. Furthermore, this study proved that the blade connection cannot be modeled by a single linear element with any physical parameters.

A RSSI-AOA-Based UHF Partial Discharge Localization Method Using MUSIC Algorithm

To monitor the insulation deterioration of power equipment and realize prompt fault warning systems in air-insulated substations, in this study, we propose a multiple signal classification (MUSIC) algorithm-based partial discharge (PD) localization method with an angle of arrival (AOA) and ultrahigh frequency (UHF)-received signal strength indicator (RSSI). Compared with traditional UHF time-difference-based techniques, this RSSI-based AOA localization method is a more economical solution. In addition, by comparing the measured RSSI vector to a prebuilt reliable reference data set, the MUSIC method can effectively locate the direction of the PD source with high accuracy. Compared with the method that directly determines the smallest RSSI values by several sensors, this method can accomplish localization by fewer sensors without impeding accuracy. Furthermore, the interpolation method was adopted to improve the precision of the relationship curve of AOA/RSSI, which it did with a limited number of sensors. Laboratory tests were conducted to verify the accuracy of the proposed method, and most of the localization errors were less than 1°, which indicates its potential application in the prompt identification of faults regarding the insulation deterioration of power equipment in substations.

A Diagnostic Method for Moisture Intrusion Fault in OIP Bushing

Oil-impregnated paper (OIP) bushing is essential equipment in power substations, and moisture intrusion is one of the major faults during its operation. In this article, a method is proposed to diagnose the moisture intrusion fault by the hidden Markov model (HMM) and the shuffled frog-leaping algorithm (SFLA). First, HMM is employed to characterize the probabilistic relationship between the moisture states and frequency-domain spectroscopy (FDS). Then, SFLA is adopted to optimize the locatable results of moisture intrusion point (MIP) in the bushing. Next, extensive simulated data are used for parameter training in the HMM-SFLA model library, with cross-validation for optimal parameters' configuration of HMM and SFLA. Finally, the proposed method was validated by experiment in laboratory and field measurements. The results show that the proposed method can effectively diagnose the MIP of OIP bushings.

Seismic Analysis of a 500-kV Power Transformer of the Type Damaged in the 2008 Wenchuan Earthquake

AbstractAs indispensable electric power equipment in substations, high-voltage power transformers have experienced severe damage in past earthquakes. This paper illustrates an earthquake-damaged 50...