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With the implementation of intelligent unmanned aerial vehicles (UAVs) in power equipment inspection, managing the obtained inspection results through information technology is increasingly crucial. This paper collected insulator images, including images of standard and self-exploding insulators, during the inspection process using intelligent UAVs. Then, an optimized you only look once version 5 (YOLOv5) model was developed by incorporating the convolutional block attention module and utilizing the efficient intersection-over-union loss function. The detection performance of the designed algorithm was analyzed. It was found that among different models, the YOLOv5s model exhibited the smallest size and the highest detection speed. Moreover, the optimized YOLOv5 model showed a significant improvement in speed and accuracy for insulator detection, surpassing other methods with a mean average precision of 93.81% and 145.64 frames per second. These results demonstrate the reliability of the improved YOLOv5 model and its practical applicability. [ABSTRACT FROM AUTHOR]

Aiming at the low-frequency line spectrum noise characteristics of power equipment noise, based on the principle of energy degradation, this paper combines the energy degradation sound insulation structure with the dynamic vibration absorption technology for the first time and applies it to the research field of noise control of power equipment in substations. Dynamic vibration absorption technology is used to effectively control low-frequency vibration and noise. Considering that there is an upper limit to the capacity of DVA, the sound-vibration energy degradation design of the transformer is completed by setting a sound insulation structure on the outside of the original transformer housing. It is analyzed that the vibration energy of the sound insulation structure in the specific frequency band is significantly reduced compared to the transformer housing, realizing efficient degradation of the vibration energy of the transformer housing and effective isolation of sound radiation. Through the optimized design of dynamic vibration absorption for the sound insulation structure, the structural sound isolation ability at the target frequency is further strengthened, and the system noise radiation level is greatly reduced under the action of multiple mechanisms at the target frequency, verifying the feasibility and high efficiency of the optimal DVAs energy degradation design of the transformer. [ABSTRACT FROM AUTHOR]

Accurate identification and classification of equipment defects are essential for assessing the health of power equipment and making informed maintenance decisions. Traditional defect classification methods, which rely on subjective manual records and intricate defect descriptions, have proven to be inefficient. Existing approaches often evaluate equipment status solely based on defect grade. To enhance the precision of power equipment defect identification, we developed a multi-label classification dataset by compiling historical defect records. Furthermore, we assessed the performance of 11 established multi-label classification methods on this dataset, encompassing both traditional machine learning and deep learning methods. Experimental results reveal that methods considering label correlations exhibit significant performance advantages. By employing balanced loss functions, we effectively address the challenge of sample imbalance across various categories, thereby enhancing classification accuracy. Additionally, segmenting the power equipment defect classification task, which involves numerous labels, into label recall and ranking stages can substantially improve classification performance. The dataset we have created is available for further research by other scholars in the field. [ABSTRACT FROM AUTHOR]

Polyurethane (PU) materials are extensively utilized in power equipment. This paper introduces a comprehensive evaluation method that combines electromagnetics and computational chemistry based on the Density Functional Theory (DFT) to elucidate the impact of external electric fields on the molecular structure of PU during electrical contact. The study focuses on the microstructural and molecular energy changes in the hard (HS) and soft (SS) segments of PU under the influence of an electric field of uniform intensity. Findings indicate that the total energy of HS molecules decreases markedly as the electric field intensity increases, accompanied by a significant rise in both the dipole moment and polarizability. Conversely, the total energy and polarizability of the SS molecules decrease, while the dipole moment experiences a slight increase. Under the influence of a strong electric field, HS molecules tend to stretch towards the extremities of the main chain, leading to structural instability and the cleavage of hydroxyl O-H bonds. Meanwhile, the carbon chain of the SS molecules twists towards the center under the electric field, with no chemical bond rupture observed. At an electric field intensity of 8.227 V/nm, the HOMO-LUMO gap of the HS molecule narrows sharply, signifying a rapid decline in the molecular structure stability, corroborated by infrared spectroscopy analysis. These findings offer theoretical insights and guidance for the modification of PU materials in power equipment applications. [ABSTRACT FROM AUTHOR]

Acoustic barriers are used to reduce the noise of power equipment. To increase their efficiency, an cylindrical top edge is installed, which is an add-on on the top edge of the barrier. To study the acoustic properties of the cylindrical top edge, a mathematical model of a 3-m high barrier was built in the COMSOL Multiphysics program. The mathematical model of the barrier without an cylindrical top edge was verified using the Kurze calculation method. The acoustic characteristics of a superstructure in the form of an cylindrical top edge have been studied. It has been determined that the acoustic efficiency of the cylindrical top edge depends both on the position relative to the upper edge of the barrier and on the distance from the noise source to the barrier. The calculation results show that the greatest changes in sound pressure levels when installing an cylindrical top edge are observed at high frequencies, and the minimum at low frequencies. The acoustic efficiency of the cylindrical top edge at geometric mean frequencies corresponding to low frequencies is approximately 1–2 dB and it can reach up to 25 dB at geometric mean frequencies corresponding to high frequencies. The acoustic characteristics of an cylindrical top edge with different installation angles have been studied. It has been shown that the cylindrical top edge with an installation angle of 0° has the highest acoustic efficiency (8–10 dBA) at a distance from the noise source to the barrier of up to 2 m. At distances from 2 to 5 m, the highest acoustic efficiency (4–8 dBA) is observed when using an antidiffraction device with an installation angle of 90°. Using an cylindrical top edge with an installation angle 180° is advisable when the barrier is located next to the design point at a distance from the barrier to it of less than 5 m. When installing an antidiffraction device, a significantly greater acoustic effect is achieved than when increasing the height of the barrier. The results obtained during the research are recommended to be taken into account when implementing noise reduction measures when choosing the location of an acoustic barrier with an cylindrical top edge relative to the noise source and the design point. [ABSTRACT FROM AUTHOR]

The paper discusses the aspects of implementation of high-precision digital twins for power plants and energy systems based on the approaches used by General Electric (GE), which has an extensive experience in this field. To create such twins, the company uses laws of physics, knowledge and experience in engineering design, methods of measuring and controlling various parameters, as well as artificial intelligence technologies. The paper offers a description of models, methods, and approaches used by GE when developing digital twins. The company's experience in the use of artificial intelligence technologies, such as pattern recognition, unstructured (multimodal) data analysis, and expert networks, as well as sensor technologies is presented. The most important component of GE's digital twin is the Predix simulation platform. It was specifically developed by GE by using industrial Internet technologies to perform analytical processing of huge amounts of machine data in real time. The basic capabilities and specifications of the Predix platform are provided. [ABSTRACT FROM AUTHOR]

Abstract Accurate identification and classification of equipment defects are essential for assessing the health of power equipment and making informed maintenance decisions. Traditional defect classification methods, which rely on subjective manual records and intricate defect descriptions, have proven to be inefficient. Existing approaches often evaluate equipment status solely based on defect grade. To enhance the precision of power equipment defect identification, we developed a multi-label classification dataset by compiling historical defect records. Furthermore, we assessed the performance of 11 established multi-label classification methods on this dataset, encompassing both traditional machine learning and deep learning methods. Experimental results reveal that methods considering label correlations exhibit significant performance advantages. By employing balanced loss functions, we effectively address the challenge of sample imbalance across various categories, thereby enhancing classification accuracy. Additionally, segmenting the power equipment defect classification task, which involves numerous labels, into label recall and ranking stages can substantially improve classification performance. The dataset we have created is available for further research by other scholars in the field.

Objective With the increase in urban rail transit operational lines and operating hours, electrical energy consumption rises annually. To reduce energy consumption and operational costs, it is essential to conduct in-depth research on energy-saving and consumption reduction strategies for urban rail transit. Method The composition of electrical energy consumption in urban rail transit is analyzed, and influencing factors of train traction energy consumption, power equipment energy consumption, and lighting energy consumption are summarized. The strategies and practical applications of Nanning rail transit in energy-saving and consumption reduction are reviewed. Strategies and suggestions for urban rail transit deep energy-saving and consumption reduction in areas such as train traction, power equipment, and lighting. Result & Conclusion In 2022 to 2023, Nanning rail transit significantly reduced energy consumption by implementing strategies such as ATO (automatic train operation) traction and braking curve optimization, train air-conditioning control logic optimization, BAS (building automation system) optimization for enthalpy value, ′wind and water balance′ commissioning, lighting brightness adjustment. These strategies resulted in an annual energy saving of approximately 16 GWh per line. It is recommended that urban rail transit operating companies across various regions develop specific energy consumption and saving management measures tailored to their operational characteristics in aspects like line design, equipment and material selection, construction quality, operational organization, and management. This approach aims to achieve maximum energy savings and consumption reduction.

Main characteristics of cast polymer insulation are analyzed. The factors affecting its electrical strength are identified, and recommendations to optimize insulation systems to enhance their reliability and durability are presented. The influence of the main structural and operational factors on the electrical insulation strength is considered. Regularities in the influence of the main operational factors on the characteristics of epoxy compounds used in high-voltage power and measuring transformers, switchgears, and current conductors are analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy C -means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to high greenhouse gas emissions, countries worldwide are stepping up their emission reduction efforts, and the global demand for new, carbon-free fuels is growing. Ammonia (NH3) fuels are popular due to their high production volume, high energy efficiency, ease of storage and transportation, and increased application in power equipment. However, their physical characteristics (e.g., unstable combustion, slow flame speed, and difficult ignition) limit their use in power equipment. Based on the structural properties of the power equipment, NH3 fuel application and emissions characteristics were analyzed in detail. Combustion of NH3 fuels and reduction measures for NOx emissions (spark plug ignition, compression ignition, and gas turbines) were analyzed from various aspects of operating conditions (e.g., mixed fuel, fuel-to-exhaust ratio, and equivalence ratio), structure and strategy (e.g., number of spark plugs, compression ratio (CR), fuel injection, and ignition mode), and auxiliary combustion techniques (e.g., preheating, humidification, exhaust gas recirculation, and secondary air supply). The performance of various NH3 fuel cell (FC) types was analyzed, with a focus on the maximum power achievable for different electrolyte systems. Additionally, the application and NOx emissions of indirect NH3 FCs were evaluated under flame and catalytic combustion conditions. The system efficiency of providing heat sources by burning pure NH3, anode tail gas, and NH3 decomposition gas was also compared. Based on a comprehensive literature review, the key factors influencing the performance and emissions of NH3-powered equipment were identified. The challenges and limitations of NH3-powered equipment were summarized, and potential strategies for improving efficiency and reducing emissions were proposed. These findings provide valuable insights for the future development and application of NH3 FCs. [ABSTRACT FROM AUTHOR]

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency–response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor's sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hardware and software monitoring complex is designed to study the relationship between changes in the patterns of the magnetic field intensity distribution with power equipment failures. In experimental studies with predetermined technical faults artificially introduced into the object the complex will provide useful and reliable results by registering changes in the magnetic field induction in the spatial, temporal and frequency domains. The choice and flexible adjust-ment of data collection tools by the researcher will ensure the use of the most appropriate method for measuring, con-verting and displaying information about the investigated faults. The article presents the main operating modes and graphical user interfaces of a personal computer and a mobile data acquisition system that jointly convert, store, ana-lyze and display measurement information received from various magnetic sensors. The generalized functional specification of the complex software, the design of the graphical user interface (GUI) as parts of the real-time operating system (RTOS) of the mobile data acquisition system (DAQ) and PC software will also be used in the development of problem-oriented computer diagnostic systems for electric power equipment. Ref. 12, fig. 3.

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification. Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior. Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor, this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal. A vibration measurement test platform is constructed, and feasibility and effectiveness tests are performed for the vibration motor and other power equipment. The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range. Furthermore, the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency. The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.

The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model's extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model's universal applicability. [ABSTRACT FROM AUTHOR]

In order to detect abnormal heat generation in time, a reversible color-changing coating temperature measurement method is proposed for self-detection of temperature by power equipment, and its adhesion characteristics and aging performance were analyzed. The results showed that the reversible color-changing coating prepared with crystalline violet lactone as the colorant, bisphenol A as the color developer, octadecanol as the solvent, and RTV-II as the base paint can meet the requirements of self-detection of temperature by power equipment with its adhesion performance. The accelerated aging tests using high temperature, light and humidity were carried out in the laboratory, and we concluded that the deterioration degree of the color-changing coating was positively correlated with the temperature. Light can accelerate the aging rate of reversible color-changing coatings, and the degradation process of the coating was significantly accelerated under UV light. The effect of humidity on the coating was not significant. The degradation of the coating after aging for 288 h under indoor conditions was small, while it was accelerated under outdoor natural conditions. This research can provide a reference for the on-site application of reversible color-changing coatings for self-detection of temperature by power equipment. [ABSTRACT FROM AUTHOR]

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Power equipment is an important component of the whole power system, so that it is obvious that it is required to develop a correct method for accurate analysis of the infrared image features of the equipment in the field of detection and recognition. This study proposes a troubleshooting strategy for the power equipment based on the improved AlexNet neural network. Multi-scale images based on the Pan model are used to determine the equipment features, and to determine the shortcomings of AlexNet neural network, such as slower recognition speed and easy overfitting. After knowing these shortcomings, it would become possible to improve the specific recognition model performance by adding a pooling layer, modifying the activation function, replacing the LRN with BN layer, and optimizing the parameters of the improved WOA algorithm, and other measures. In the simulation experiments, this paper's algorithm was compared with AlexNet, YOLO v3, and Faster R-CNN algorithms in the lightning arrester fault detection, circuit breaker fault detection, mutual transformer fault detection, and insulator fault detection improved by an average of 5.47 %, 4.69 %, and 3.42 %, which showed that the algorithm had a better recognition effect. [ABSTRACT FROM AUTHOR]

Polyurethane (PU) materials are extensively utilized in power equipment. This paper introduces a comprehensive evaluation method that combines electromagnetics and computational chemistry based on the Density Functional Theory (DFT) to elucidate the impact of external electric fields on the molecular structure of PU during electrical contact. The study focuses on the microstructural and molecular energy changes in the hard (HS) and soft (SS) segments of PU under the influence of an electric field of uniform intensity. Findings indicate that the total energy of HS molecules decreases markedly as the electric field intensity increases, accompanied by a significant rise in both the dipole moment and polarizability. Conversely, the total energy and polarizability of the SS molecules decrease, while the dipole moment experiences a slight increase. Under the influence of a strong electric field, HS molecules tend to stretch towards the extremities of the main chain, leading to structural instability and the cleavage of hydroxyl O-H bonds. Meanwhile, the carbon chain of the SS molecules twists towards the center under the electric field, with no chemical bond rupture observed. At an electric field intensity of 8.227 V/nm, the HOMO-LUMO gap of the HS molecule narrows sharply, signifying a rapid decline in the molecular structure stability, corroborated by infrared spectroscopy analysis. These findings offer theoretical insights and guidance for the modification of PU materials in power equipment applications.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value. [ABSTRACT FROM AUTHOR]

The study considers approaches to ensuring energy management for the safe operation of facilities and their equipment and ways to improve it. It has been established that to ensure effective safety management of industrial enterprises, one of the critical areas is the technical diagnostics of power equipment during operation. An assessment of the actual technical condition of power equipment of VVER-1000 power units is proposed based on establishing the aging mechanisms and determining the relative evaluation coefficients for the characteristics of individual equipment elements. The results of the calculations allowed us to conclude that the obtained results correspond to the coefficients of relative assessment Ki of the technical characteristics of the power equipment that determine its degradation. Studies indicates that when assessing the state of power equipment, it is necessary to consider the presence and impact of the following operational factors that are not considered in the design calculations: loads, high levels of mechanical stress, fatigue damage, and metal defects, which primarily indicate the presence of degradation changes. To assess the technical condition of the equipment, considering the degree of mechanical wear, 17 technical characteristics were selected to determine the aging mechanisms by signs of degradation. A mathematical model of the dependence of the relative evaluation coefficient K on changes in the operating parameters is presented, and it is noted that the most significant influence on the value of the coefficient is the temperature of the coolant at the inlet (K = 0.56). The developed approach makes it possible to improve the safety management system of power facilities by introducing the proposed model to assess the technical conditions of power equipment by defining the parameters in the overall safety management system. [ABSTRACT FROM AUTHOR]

In this work, a 1550-nm full-fiber Doppler lidar via software-defined platform is built to realize flexible and low-cost contactless vibration measurement of high-voltage power equipment. A 1550-nm fiber layout is designed to generate optical interference between vibration signal and carrier wave. The reflected vibration signal is collected by an optical transceiver and the carrier wave is generated by an acousto-optic modulator (AOM). The optical beat signal is collected by a balanced detector (BD) then sent into a general software defined radio (SDR) receiver. By GNU developing platform, the target mechanical vibration signal is demodulated and several flexible functions such as speed-acceleration trans, harmonic component analysis and fault diagnosis is realized. Performance of Doppler lidar is first verified on mechanical vibration source by PZT vibration actuator, results show that the designed lidar could retrieve 50 Hz–20 kHz mechanical vibration signals within the working distance is up to 20 m. Further case application scenarios on the power transformer and gas-insulated switchgear (GIS) are also conducted to verify the feasibility of proposed lidar.

In order to realize the energy supply-side reform and promote the “double carbon” goal, the power supply structure of China is being optimized and the proportion of renewable energy sources (RES) is gradually increasing, resulting in the traditional mechanism model analysis methods can hardly satisfy the requirements of monitoring and operation optimization of new power systems. Digital twin technology utilizes sensors, big data, artificial intelligence and other technologies to establish the mirroring from physical space to virtual space, and it is an effective way to analyze the evolution law of dynamic behavior of key equipment and power systems, and propose optimal control strategies. Based on the technical connotation of digital twin, this paper proposes a framework for the construction of digital twin microgrids with high percentage of RES. A real-time digital simulator (RTDS) is used to build a grid-level digital twin microgrid to digitally reproduce the equipment, environment and other key aspects of the physical grid. A digital twin framework for power equipment is proposed to provide a systematic structural support for the digital management of microgrid power equipment. Finally, the advanced application module of digital twin microgrid is prospected to provide lessons and references for the construction of digital twin microgrid.

Due to high greenhouse gas emissions, countries worldwide are stepping up their emission reduction efforts, and the global demand for new, carbon-free fuels is growing. Ammonia (NH3) fuels are popular due to their high production volume, high energy efficiency, ease of storage and transportation, and increased application in power equipment. However, their physical characteristics (e.g., unstable combustion, slow flame speed, and difficult ignition) limit their use in power equipment. Based on the structural properties of the power equipment, NH3 fuel application and emissions characteristics were analyzed in detail. Combustion of NH3 fuels and reduction measures for NOx emissions (spark plug ignition, compression ignition, and gas turbines) were analyzed from various aspects of operating conditions (e.g., mixed fuel, fuel-to-exhaust ratio, and equivalence ratio), structure and strategy (e.g., number of spark plugs, compression ratio (CR), fuel injection, and ignition mode), and auxiliary combustion techniques (e.g., preheating, humidification, exhaust gas recirculation, and secondary air supply). The performance of various NH3 fuel cell (FC) types was analyzed, with a focus on the maximum power achievable for different electrolyte systems. Additionally, the application and NOx emissions of indirect NH3 FCs were evaluated under flame and catalytic combustion conditions. The system efficiency of providing heat sources by burning pure NH3, anode tail gas, and NH3 decomposition gas was also compared. Based on a comprehensive literature review, the key factors influencing the performance and emissions of NH3-powered equipment were identified. The challenges and limitations of NH3-powered equipment were summarized, and potential strategies for improving efficiency and reducing emissions were proposed. These findings provide valuable insights for the future development and application of NH3 FCs.

The strength of the welded joint of the connecting pipe and the collector made of 12Kh1MF steel is investigated within the framework of the model of elastic solid, taking into account their real geometry and the dependence of the material characteristics on temperature. Zones of the maximum operating stresses in the joint for different types of welding are identified. Design and technological solutions aimed at significant reduction of the maximum operational stresses are proposed. [ABSTRACT FROM AUTHOR]

The study considers ways to ensure the quality of the functioning of power equipment as one of the directions for ensuring the safety of energy complexes in the period of "green transformation". Based on the analysis, it is established that for an effective "green" transformation, it is necessary to ensure the energy safety of equipment operation, which is possible by developing effective science-based approaches to technical diagnostics of the state of power systems and individual equipment. The main objective of the study is to develop a Safety Management Model for Life Term Operation of power equipment, which takes into account changes in its condition at different stages of operation and allows for the prediction of further safe operation. The paper proposes an approach to researching the technical condition of power equipment, taking into account the deformations that occur during operation. The results of the calculation of stress intensity coefficients for longitudinal and transverse-postulated cracks in different modes are presented. Based on the calculated and experimentally obtained values, an approach to predicting the operating life of power equipment with regard to changes in technical characteristics is proposed. The results of the calculations showed that by changing the allowable load on the material of the energy equipment to 35 kgf/mm2, the estimated operational lifespan could be extended until 2035, ensuring all necessary safety conditions. It has been proven that with effective management and continuous safety diagnostics, nuclear power plants have the potential to operate beyond their standard design life of up to 30–40 years. This approach allows for the quality and safe operation of power equipment in the context of the transition to sustainable energy. [ABSTRACT FROM AUTHOR]

In this work, a 1550-nm full-fiber Doppler lidar via software-defined platform is built to realize flexible and low-cost contactless vibration measurement of highvoltage power equipment. A 1550-nm fiber layout is designed to generate optical interference between vibration signal and carrier wave. The reflected vibration signal is collected by an optical transceiver and the carrier wave is generated by an acousto-optic modulator (AOM). The optical beat signal is collected by a balanced detector (BD) then sent into a general software defined radio (SDR) receiver. By GNU developing platform, the target mechanical vibration signal is demodulated and several flexible functions such as speed-acceleration trans, harmonic component analysis and fault diagnosis is realized. Performance of Doppler lidar is first verified on mechanical vibration source by PZT vibration actuator, results show that the designed lidar could retrieve 50 Hz-20 kHz mechanical vibration signals within the working distance is up to 20 m. Further case application scenarios on the power transformer and gas-insulated switchgear (GIS) are also conducted to verify the feasibility of proposed lidar. [ABSTRACT FROM AUTHOR]

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency–response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor’s sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor.

The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

With the construction and development of the new generation of power system (thereafter, it is displaced with PS), intelligent power equipment is more widely used and higher requirements for the flexibility, controllability and safety of PS emerge. This article focused on the key technologies of equipment operation and maintenance (O&M) in the PS, aiming to improve the challenges faced by traditional PS through new energy power and intelligent PS. The article studied the development of new PS, analyzed intelligent PS from the perspectives of intelligent maintenance, online fault diagnosis and status monitoring and evaluation, compared the capacity of new energy equipment and simulated the wind PS simulation structure diagram. Setting the Longyangxia Hydropower Station as an instance, it explored the complementarity between solar and hydro energy in the hydropower station. By studying the service life of transformers in 50 PS of Longyangxia Hydropower Station, it was found that amorphous alloys and silicon steel were relatively excellent and a total of 8 rounds of power generation equipment in the PS passed the safety assessment. The article proposed a long-term maintenance research method for the key technologies of equipment O&M in the new PS, achieving precise management and efficient maintenance of equipment and ensuring safe and reliable operation of equipment under complex working conditions.

The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields. [ABSTRACT FROM AUTHOR]

This article analyzes the problems of inappropriate discharges of geothermal waste waters to open ground and surface water bodies, as well as problems of the release of associated combustible gases with geothermal water into the open atmosphere as a result of flaring. An assessment and possible ways of technical prevention of environmental pollution due to the use of geothermal waters in arid regions are given. Schematic solutions for the operation of geothermal systems in the regime without scaling under conditions of the utilization of the associated combustible gas (methane) are presented. It is shown that the addition of carbon dioxide to the geothermal waste water that was reinjected into the aquifer provides the longevity of the injection wells. [ABSTRACT FROM AUTHOR]

The use of acoustic and magnetic methods of non-destructive testing to detect zones of stable localization of deformation in order to assess and predict the performance of long-term equipment is of scientific and practical interest at present. A structural–mechanical criterion was developed that reflects the relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests in the metal of long-term equipment made of structural 0.2 C steel and heat-resistant 0.12C-1Cr-1Mo-1V steel. The values of the structural–mechanical criteria Ks.-m for structural 0.2 C steel and for heat-resistant 0.12C-1Cr-1Mo-1V steel, corresponding to the moment of stable localization of deformation, are established. At the same time, it is recommended to replace the checked equipment nodes due to the exhaustion of the resource. The proposed and justified approach to assessing and predicting the performance and residual life of long-term power equipment, based on the identified relationships between the structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of the structural–mechanical criterion, was applied at a number of power plants in the Kemerovo region—Kuzbass. A methodology was developed for evaluating the residual life, based on the identification and use of relationships between structural and substructural states, internal stress fields and stable localization of deformations with the characteristics of non-destructive tests and the calculation of a structural–mechanical criterion. [ABSTRACT FROM AUTHOR]

Rolling machinery is ubiquitous in power transmission and transformation equipment, but it suffers from severe faults during long-term running. Automatic fault diagnosis plays an important role in the production safety of power equipment. This paper proposes a novel cross-domain co-attention network (CDCAN) for fault diagnosis of rolling machinery. Multiscale features cross time and frequency domains are respectively extracted from raw vibration signal, which are then fused with a co-attention mechanism. This architecture fuses layer-wise activations to enable CDCAN to fully learn the shared representation with consistency across time and frequency domains. This characteristic helps CDCAN provide more faithful diagnoses than state-of-the-art methods. Experiments on bearing and gearbox datasets are conducted to evaluate the fault-diagnosis performance. Extensive experimental results and comprehensive analysis demonstrate the superiority of the proposed CDCAN in term of diagnosis correctness and adaptability.