Power Distribution Room Research Articles

MAMRS: Mining Automatic Meter Reading System Based on Improved Deep Learning Algorithm Using Quadruped Robots

The meter reading work in the power distribution room of a mine is traditionally carried out by manual inspection, which requires several workers and often lacks timeliness and efficiency. Owing to the widespread use of quadruped robots, this paper proposes a Mining Automatic Meter Reading System, named MAMRS, to identify and read pointer and digital meters in mine distribution rooms using quadruped robots. The new method consists of three stages. Initially, this technique identifies the type of meter using the ResNet18 convolutional neural network model, then proceeds to extract the dial area image using the YOLOv5 object detection algorithm, and finally reads the pointer and digital meters’ data using the U2-net algorithm and SVM, respectively. Experimental results show that the recognition accuracy rates for meter classification, pointer meter reading, and digital meter reading are 99.87%, 85.35%, and 90.73%, respectively. The MAMRS fulfills the need for fully automated and intelligent inspection of mine distribution rooms, resulting in significantly enhanced accuracy, flexibility, and innovation in inspection processes. Moreover, it reduces labor costs and improves inspection efficiency. The findings of this study serve as a reliable reference for intelligent inspection practices in the power distribution rooms of metal mines.

A BIM-FDS Based Evacuation Assessment of Complex Rail Transit Stations under Post-Earthquake Fires for Sustainable Buildings

Post-earthquake fire is considered as a catastrophic secondary disaster to personal and property safety, especially in complex rail transit station. This is primarily attributed to intricate infrastructure, densely populated floors, and the unrestricted layout of these areas. The aim of this study is to evaluate the evacuation capacity of complex railway stations under post-earthquake fires, and provide sustainable recommendations for building design. In this article, an evacuation assessment of a complex rail transit station under the post-earthquake fire for sustainable buildings was conducted from the internal environment and external rescue based on Building Information Modeling (BIM) and Fire Dynamic Simulation (FDS). The internal environment evacuation assessment simulation experiments were conducted in six hypothetical high-risk scenarios. In addition, the external rescue assessment was based on investigation of the route and the required rescue time during different periods of holidays and workdays. The results show that (1) The influence caused by different sizes of fire area in the power distribution room is smaller than those in the train at the platform floor. (2) In fire scenarios with the same fire area but different fire locations, the temperature is more affected than the CO concentration in the power distribution room. (3) It shows slight differences between single-floor fire and double-floor fire on evacuation of small area fire in power distribution room. Meanwhile, optimized design recommendations are proposed to reduce the risk of emergency evacuation in both internal and external environments of rail transit stations for sustainable future buildings, which include strategically locating the power distribution room away from public areas, installing fire-resistant doors around the room, increasing the quantity of smoke detectors and alarms with regular maintenance, minimizing the size of the power distribution room, developing specific emergency plans for train fires, and incorporating small fire stations in urban planning near complex public buildings to mitigate post-earthquake road obstruction challenges.

Three-dimensional model construction of power distribution room and its digital twin physical structure research

Abstract Aiming at the problem of complex internal structure and difficult monitoring of the distribution room, a new monitoring method for the distribution room based on a digital twin is studied. Firstly, a 3D model design method for the distribution room is proposed by combining visualization technology and 3D Max software to complete the modeling. The 3D laser technology is used to scan the entity to get the texture photos of the 3D laser points and the raw data, then a complete model is obtained by modeling the details, and finally, the texture photos are mapped with the model to get the final model. To enhance the model’s accuracy, the optimal minimum spanning tree model optimization method is proposed, and the power distribution room’s three-dimensional model design is now finished. On the basis of the feature extraction of the sensor data of the distribution room, the digital twin technology is used to realize the matching of the information model architecture from the real entity of the distribution room to the virtual model, digital conversion, correlation of the operating data features and real-time monitoring, and then the monitoring method of the distribution room based on the digital twin is obtained. The experimental analysis yields a 3D Max modeling time of 9.98s and a fineness of 90%, which is more suitable for the construction of complex 3D models of distribution rooms than the other two methods. The accuracy of digital twin wireless communication is kept at 98% or above, and the system operation is disturbed to a small extent. It shows that digital twin technology is suitable for monitoring the complex structure inside the distribution room.

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

Association of living environmental and occupational factors with semen quality in chinese men: a cross-sectional study

Sperm quality can be easily influenced by living environmental and occupational factors. This study aimed to discover potential semen quality related living environmental and occupational factors, expand knowledge of risk factors for semen quality, strengthen men's awareness of protecting their own fertility and assist the clinicians to judge the patient’s fertility. 465 men without obese or underweight (18.5 < BMI < 28.5 kg/m2), long-term medical history and history of drug use, were recruited between June 2020 to July 2021, they are in reproductive age (25 < age < 45 years). We have collected their semen analysis results and clinical information. Logistic regression was applied to evaluate the association of semen quality with different factors. We found that living environment close to high voltage line (283.4 × 106/ml vs 219.8 × 106/ml, Cohen d = 0.116, P = 0.030) and substation (309.1 × 106/ml vs 222.4 × 106/ml, Cohen d = 0.085, P = 0.015) will influence sperm count. Experienced decoration in the past 6 months was a significant factor to sperm count (194.2 × 106/ml vs 261.0 × 106/ml, Cohen d = 0.120, P = 0.025). Living close to chemical plant will affect semen PH (7.5 vs 7.2, Cohen d = 0.181, P = 0.001). Domicile close to a power distribution room will affect progressive sperm motility (37.0% vs 34.0%, F = 4.773, Cohen d = 0.033, P = 0.030). Using computers will affect both progressive motility sperm (36.0% vs 28.1%, t = 2.762, Cohen d = 0.033, P = 0.006) and sperm total motility (57.0% vs 41.0%, Cohen d = 0.178, P = 0.009). After adjust for potential confounding factors (age and BMI), our regression model reveals that living close to high voltage line is a risk factor for sperm concentration (Adjusted OR 4.03, 95% CI 1.15–14.18, R2 = 0.048, P = 0.030), living close to Chemical plants is a protective factor for sperm concentration (Adjusted OR 0.15, 95% CI 0.05–0.46, R2 = 0.048, P = 0.001) and total sperm count (Adjusted OR 0.36, 95% CI 0.13–0.99, R2 = 0.026, P = 0.049). Time spends on computer will affect sperm total motility (Adjusted OR 2.29, 95% CI 1.11–4.73, R2 = 0.041, P = 0.025). Sum up, our results suggested that computer using, living and working surroundings (voltage line, substation and chemical plants, transformer room), and housing decoration may association with low semen quality. Suggesting that some easily ignored factors may affect male reproductive ability. Couples trying to become pregnant should try to avoid exposure to associated risk factors. The specific mechanism of risk factors affecting male reproductive ability remains to be elucidated.

Foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network

In order to avoid safety problems caused by foreign bodies such as mice that may appear in the power distribution room and by demarcating the electronic fence area for key monitoring in the video surveillance screen, a foreign body intrusion monitoring and recognition approach in a power distribution room based on the improved YOLOv4 deep learning network is proposed. To optimize the detection effects, the YOLOv4 algorithm is improved from the aspects of network structure, frame detection, and loss function. At the same time, the channel pruning algorithm is used to prune the model to simplify the model structure. The experimental results show the effectiveness of the improved YOLOv4 deep learning network, which has high detection accuracy, fast detection speed, and takes up less space after pruning.

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

Communication Bandwidth Prediction Technology for Smart Power Distribution Business in Smart Parks

Accurate prediction of power business communication bandwidth is the premise for the effectiveness of power communication planning and the fundamental guarantee for regular operation of power businesses. To solve the problem of scientifically and reasonably allocating bandwidth resources in smart parks, communication bandwidth prediction technology of intelligent power distribution service for smart parks is proposed in this paper. First, the characteristics of mixed service data arrival rate of power distribution and communication mixed services in smart parks were analyzed. Poisson process and interrupted Poisson process were used to simulate periodic and sudden business of smart parks to realize accurate simulation of the business arrival process. Then, a service arrival rate model based on the Markov modulation Poisson process was constructed. An active buffer management mechanism was used to dynamically discard data packets according to the set threshold and achieve accurate simulation of the packet loss rate during the arrival of smart park business. At the same time, considering the communication service quality index and bandwidth resource utilization, a business communication bandwidth prediction model of smart parks was established to improve the accuracy of business bandwidth prediction. Finally, a smart power distribution room in a smart park was used as an application scenario to quantitatively analyze the relationship between the communication service quality and bandwidth configuration. According to the predicted bandwidth, the reliability and effectiveness of the proposed method were verified by comparison with the elastic coefficient method.

Inspection behavior detection of underground power distribution room based on conditional variational auto-encoder

Inspection behavior detection of underground power distribution room based on conditional variational auto-encoder

Design and implementation of intelligent monitoring terminal for distribution room based on edge computing

An intelligent monitoring terminal for power distribution room based on edge computing is designed in this paper, which is important for the power distribution Internet of Things. Compared with the traditional monitoring terminals, it employs an edge server (ES) to store and process the data collected by sensing devices at the edge, such as the voltage, current, state and security information. Furthermore, a FPGA and DSP based embedded digital control system in the monitoring terminal is designed to collect and process the on-site power and environmental data, which overcomes the data delay problem caused by insufficient communication bandwidth of traditional system and the problem of transmitted data dropout caused by low cloud computing capacity. The engineering application results showed that, the designed intelligent monitoring terminal based on edge computing has the advantages such as high detection accuracy, stable communication effect and fast calculation speed, which will reduce the alarm and action response time by more than 5 times.

Construction of IoT management system for intelligent monitoring of distribution room

In order to further improve the quality and efficiency of distribution network operation and maintenance work, and support the transformation of distribution network technology to digital, lean, and intelligent, this article proposes an IoT management system for intelligent monitoring of power distribution rooms, as well as a design plan for IoT management platforms and edge IoT agents. It also conducts key research on the environmental information monitoring and perception system of the power distribution room, the power protection and security perception system, and establishes an intelligent and visualized intelligent distribution room to realize the digital transformation and intelligent improvement of the operation and maintenance of the power distribution network. The results are applied in the construction of Zhejiang intelligent distribution room, providing important guidance for the standardized construction of intelligent distribution room in China.

5G Technology-Based Smart Power Distribution Station Simulation Design Specifications and Applications

The paper develops a 5G-based simulation design of a smart power distribution room. Through 5G’s large-capacity, high-reliability connection technology, various operating data of the distribution network are collected in real time, so as to achieve a complete overview of the power environment and operating status of various power distribution rooms. Process data collection effectively improves the operation of safe equipment. At the same time, it provides massive basic inversion data for the interactive design of smart power distribution rooms, enhances the ability to control the power distribution room system and its equipment, and enhance the overall reliability of the power distribution room system.

Study of Sensor Layout Acquisition and Application Analysis within Distribution Room

The traditional power distribution room lacks intelligent monitoring business functions, which is not conducive to the requirements of intelligent operation and maintenance. This article takes the intelligent IoT terminal(Internet of Things terminal) as the center, carries out the layout design of the distribution room sensor, combines the intelligent collection of the distribution room, and accesses the sensing equipment to build a smart distribution room, meanwhile, this paper designs the intelligent terminal which adopts the edge computing technology to carry out the environment temperature and humidity assessment, equipment partial discharge analysis and assessment, temperature rise assessment and other micro-applications to provide support for the construction of smart distribution room.

IoT based cloud monitoring system for high-voltage power distribution room in the electric substation

Under the background of developing smart grids and ubiquitous electric Internet of Things (UEIoT) in China, an IoT based cloud monitoring system for high-voltage power distribution room in the electric substation has been proposed in this paper as a typical pioneer project. This system includs perception layer, network layer, application layer. The Key technologies, which contribute to realize real-time automatic control, intelligent regulation, online analysis and decision-making, are illustrated in detail. The proposed IoT based cloud monitoring system has been conducted in several areas of Hunan province and the field application illustrates the benefits of this system in the aspects of reducing manual operation, raising efficiency and preventing accidents.

Development of Distributed Low Voltage Distribution Remote Monitoring System

Intelligent power distribution monitoring technology is an important technology to improve the intelligence and automation level of the power distribution system. However, because the current power distribution monitoring methods in low-voltage power distribution rooms are not very developed, regular manual inspections and manual data recording are needed, leading to efficiency very low, so the research and development of intelligent distribution monitoring system in low-voltage distribution room is very necessary.This system is mainly composed of intelligent electrical parameter collection terminal, 4G data transmission module, Alibaba Cloud Internet of Things platform, and remote power host computer, and each device can perform effective data communication. The system is assigned to each low-voltage power distribution room for unified management and viewing. The low-voltage power distribution real-time monitoring system can realize the real-time monitoring of transformer parameters and wireless transmission, and control the 4G communication module to receive or send data to the Alibaba Cloud Internet of Things platform. The remote cloud can monitor and query electrical parameters in real time, and can also send control instructions to the intelligent collection terminal. The terminal host receives various data (such as the three-phase voltage and current of the transformer, active and reactive power, power factor, frequency, harmonics, environmental temperature and humidity, etc.) from the data collector in real time and analyzes it. Once the data exceeds The set threshold will cause an alarm to prevent potential safety hazards in the low-voltage power distribution room. The experimental results prove that the system can monitor the working status of various electrical equipment in the power distribution room in real time, accurately and efficiently, thereby ensuring the safety and stability of the entire power distribution system, and has certain research value.

Inspection behavior recognition of underground power distribution room based on improved two-stream CNN method

Inspection behavior recognition of underground power distribution room based on improved two-stream CNN method

Power quality monitoring system of distribution room based on distributed sensors

A large number of non-linear devices has been used nowadays. Therefore, the power quality problem on the user side is becoming significantly prominent. The power distribution room is the end of the power system, which is directly connected to the users. But distribution room has noneffective power quality monitoring system established due to difficulties of data measuring and installation. In this paper, based on micro distributed sensor with coil energy acquisition and wireless communication technology, a distributed power quality monitoring system is developed in distribution room, which can be implemented quickly and complete the monitoring and analysis of power quality information in the distribution room. The system realizes the function of design requirement through field test.

A Path Planning Method Based on Multi- Objective Cauchy Mutation Cat Swarm Optimization Algorithm for Navigation System of Intelligent Patrol Car

The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.

Process parameters selection of insulator contamination cleaning by dry ice

The dry ice cleaning was applied to the charged cleaning of the insulators in the power distribution room. On the basis of the system parameters optimization experiments of the insulator dry ice cleaning, setting the air pressure to 0.6 MPa and the dry ice flow rate to 2.5 kg/min, the process parameters optimization experiments of the insulator dry ice cleaning were carried out, and the optimal cleaning process parameters of the insulator were obtained through experiments. Using multi-physics simulation software Comsol to analyze the force distribution of external surface of the insulator when spraying dry ice at different distances and different angles in the dry ice cleaning, the experiment results were verified and the following conclusions were obtained: the cleaning angle and cleaning distance of the nozzle are affecting dry ice cleaning effect. The main process factors of the cleaning effect are most effective when the nozzle cleaning angle is from 15° to 25° and the cleaning distance is from 18 cm to 24 cm.

Variations of brain magnetic resonance imaging among manganese-exposed workers

Variations of the signal intensities in the magnetic resonance (MR) T(1)-weighted image (T(1)WI) of globus pallidus among manganese(Mn)-exposed workers were explored to provide a scientific basis for exposed biomarker of manganese-injured central nervous system (CNS). The brain MR T(1) and T(2) WI in eighteen male asymptomatic Mn-exposed, eight manganism and nine healthy control workers were examined routinely by adopting a 1.5 Tesla signal superconducting system. The SIGP and the signal intensity in frontal white matter (SIFWM) in the same side were determined, then pallidal index (PI) was calculated. Concentration of MnO(2) in workplaces and content of manganese in red blood cell (MnRBC) among workers were respectively determined by flame atomic absorption spectrometer (AAS) and inductively coupled plasma-atomic emission spectrophotometry (ICP-AES). The follow-up investigation in the eight high Mn-exposed workers was made one year later. The results showed that the median of air MnO(2) in smelting workplace was 0.64 mg/m(3)(0.07 - 5.40 mg/m(3)), which were respective 0.56 mg/m(3)(0.09 - 1.71 mg/m(3)) in power distribution room (low Mn-exposure) and 0.89 mg/m(3) (0.07 - 5.40 mg/m(3)) in furnace (high Mn-exposure). PI in the Mn-exposed and high Mn-exposed workers were both higher than those of the manganism and control workers(116.4 +/- 8.2, 119.0 +/- 7.9, 105.3 +/- 8.4 and 102.2 +/- 1.5, respectively. Mn vs control, t' = 7.146, P = 0.000; Mn vs manganism, t = 3.181, P = 0.004. High Mn-exposure vs control, t' = 7.446, P = 0.000; high Mn-exposure vs manganism, t = 3.763, P = 0.001). The increased signal in T(1)WI of globus pallidus was observed in Mn-exposed workers, especially in high Mn-exposed workers. The content of manganese in red blood cell of Mn-exposed and control workers was significantly higher than those of the manganism workers [(151.6 +/- 40.5) ng/ml, (149.2 +/- 21.3) ng/ml, (154.5 +/- 46.6) ng/ml, (144.4 +/- 14.2) ng/ml, (20.8 +/- 7.4) ng/ml respectively. The difference was significant in statistics. Manganism vs control, t = 20.206, P = 0.000; manganism vs Mn, t' = 13.144, P = 0.000; manganism vs low and high Mn, t' = 12.964, 9.957, respectively, P = 0.000]. Only a decreased median of air MnO(2) in furnace was found one year later (0.89, 0.31 mg/m(3), Z = -2.142, P = 0.032). The difference was significant in statistics. Our data suggests that SIGP of MR T(1)WI among workers was obviously increased by manganese-exposure. PI may be taken as the signal of CNS injury which was induced by manganese-exposure.