Li Jie 1, Gao Shugong 2, Lei Dong 3 (1. Kunming University of Science and Technology, Kunming, Yunnan 650504; 2. Honghe Gejiu Power Supply Bureau of Yunnan Power Grid Co., Ltd., Honghe 661000, Yunnan; 3. Honghe Power Supply Bureau of Yunnan Power Grid Co., Ltd., Honghe, Yunnan 661000)

Abstract: In order to solve the optimization method of substation equipment entity information acquisition process, AR technology is not used to collect substation equipment entity information acquisition process, resulting in high packet loss rate, low convergence speed and information acquisition rate during information transmission. To this end, this paper proposes an AR-based hardware design of the substation equipment entity information acquisition process optimization system. According to the information acquisition process optimization system, the

Hardware requirements, design the system hardware framework and power supply module on the basis of building the system hardware design; adopt AR technology to collect substation equipment entity information acquisition process, formulate substation equipment entity information acquisition process optimization rules, and promote the system to optimize substation equipment entity information acquisition process. function, complete the system software design. Determine the experimental object and information transmission speed, change the information transmission distance and the number of system iterations, and compare the information acquisition view, packet loss rate and information acquisition rate. The experimental results show that: the designed substation equipment entity information acquisition process optimization system can obtain comprehensive substation equipment entity view information without being affected by the communication distance, and has a lower packet loss rate, higher information acquisition rate and Faster convergence rate. Keywords: AR technology; substation equipment; entity information; process optimization; system design; information collection; ⁃0129⁃06

0 Preface

At present, the number of substations is particularly large, coupled with its complex equipment structure, it is very unrealistic to monitor the substations manually. For this reason, some scholars have developed a substation equipment entity information acquisition system, which extracts substation equipment entity operation information and obtains substation equipment entity operation data. Substation inspectors can detect the operation of substation equipment only in the substation control center, which greatly reduces substation monitoring. The number of personnel can reduce the maintenance cost of substation equipment [1-2]. However, in this process, some scholars have found that the current information acquisition system, the process of acquiring substation equipment entity information does not conform to the substation equipment entities in various regions, and there are problems such as low information acquisition efficiency and incomplete information [3]. At present, domestic and foreign research information process optimization methods are all derived from the concept of process reengineering, and it is divided into two optimization methods: transformation and repeated verification and improvement. In response to this concept, some scholars believe that process optimization is to improve the fundamental problems existing in it, and quickly solve the problems caused by this process, thereby improving the practical application effect of the process [4]. According to this concept, foreign scholars have developed process optimization methods such as ESIA rules, Petri net model, process visualization and simulation for information process optimization [5-7]. Domestic scholars optimize the comprehensive performance of the process according to process factors, quality, effect, relationship model, etc. [8-10]. However, there is a lack of systematic and in-depth research results for the optimization of the above information acquisition process. To this end, it is proposed to use AR technology to seamlessly connect the ability to display the world. After superimposing the information acquisition process through computer simulation, it is displayed in front of people’s eyes to improve the efficiency and quantity of information acquisition. Therefore, an AR-based optimization system design of substation equipment entity information acquisition process is proposed.

1 Substation equipment entity information acquisition process optimization system hardware design

1.1 Design time control system hardware framework

The design of the substation equipment entity information acquisition process optimization system considers that when the system optimizes the substation equipment entity information acquisition process, the substation equipment entity information acquisition process needs to be collected. On the basis of the current research on the substation equipment entity information acquisition process optimization system, the design of this The hardware framework of the substation equipment entity information acquisition process optimization system is shown in Figure 1.

As can be seen from Figure 1, the system hardware framework designed this time selects the STM32F103R8 microcontroller with a 32-bit RISC processor as the core processor of the system. The processor has the characteristics of low function, high-performance simulation technology, JTAG simulation and debugging, etc. [11], which can fully meet the function of system optimization of substation equipment entity information acquisition process, and its low power consumption also reduces the difficulty of power management and power requirements. , so that it can be installed on the substation equipment entity information acquisition system to optimize the substation equipment entity information acquisition process in real time. In addition, the optimized system designed this time also adds an A/D conversion module and a storage module. Among them, the A/D conversion module will convert the substation equipment entity information acquisition process sensed by the sensor module into digital semaphore, which reduces the difficulty of the system to optimize the substation equipment entity information acquisition process; the storage module will be optimized by the STM32F103R8 microcontroller. The information acquisition process is stored in the storage module to form a storage memory and reduce the difficulty of optimizing the same type of information acquisition process.

1.2 Power Module Design

Considering the information acquisition process optimization system, it needs to be installed on the substation equipment entity information acquisition system, and it is difficult to detect the power operation of the system in real time. Therefore, it is necessary to design a power module to reduce the space occupied by the power supply and determine the duration of the power supply. For the power supply of the design system, a tiny lithium-ion battery with a voltage between 4 and 5V is selected to supply power to the designed system as the power supply module of the system. However, due to the components used in the system framework, the required voltage needs to be maintained at about 3.3V. For this reason, the LT1761ES5-3.3 voltage conversion chip with very small shape and package is used to step down and input the voltage of the power module battery selected this time. range and power handling. The circuit diagram of the system power module designed this time is shown in Figure 2.

In Figure 2: VIN represents the tiny lithium-ion battery used in this system; GND represents the ground; IN represents the input current; SHDN and BYP represent the two interfaces of the TLC2950 circuit breaker control chip; VOUT represents the 3.3V constant direct current. As can be seen from the circuit diagram of the system power supply module shown in Figure 2, the chip is installed on the power supply, directly controls the voltage flow change, and outputs a voltage that meets the requirements of the system components.

2 Software design of AR-based substation equipment entity information acquisition process optimization system

Based on the hardware of the substation equipment entity information acquisition process optimization system designed this time, AR technology is used to collect the substation equipment entity information acquisition process, formulate the substation equipment entity information acquisition process optimization rules, and make the system have the function of optimizing the substation equipment entity information acquisition process. , to complete the software design of the substation equipment entity information acquisition process optimization system.2.1 The process of acquiring information based on ARSince the substation equipment entity information acquisition process only runs in the information acquisition system software, it is in a virtual state, which greatly affects the optimization effect of the system optimization information acquisition process [12]. Therefore, the virtual and real registration function of AR technology is used to transform the virtual information acquisition process. In order to display the running process of the scene, the collection process of the substation equipment entity information acquisition process as shown in Figure 3 is formed, thereby reducing the difficulty of optimizing the substation equipment entity information acquisition process.

As can be seen from Figure 3, the acquisition process of substation equipment entity information acquisition this time uses AR technology to extract the substation equipment information acquisition system and obtain the substation equipment entity information acquisition process. In the intelligent terminal, the information acquisition process is compressed, and the virtual and real information acquisition process is registered by AR technology, so that the actual image of the information acquisition process is output to the STM32F103R8 microcontroller to optimize the information acquisition process. In Figure 3, the establishment and transformation process of the AR technology coordinate system is as follows: Considering that when the information acquisition system acquires the information of the substation equipment, it needs to use a sensor module to sense the operating parameters of the substation equipment, and transmit the substation information to the control center according to the set process. Therefore, the camera calibration method is used to establish the AR technology coordinate system to complete the virtual and real registration of the information acquisition process. For this reason, it is assumed that there is no lens distortion when the information acquisition system acquires the information of the substation equipment, and the transformation model of the camera imaging is:In the formula: s represents the distortion parameter [13-14], that is, s=0; M represents the virtual space point of the information acquisition process; A represents the camera internal parameter matrix; [R|t] represents the camera transformation parameter; R represents the external parameter; M’ represents the real space point converted into actual coordinates. Substitute the formula (1) into Fig. 3, the process of acquiring the substation equipment entity information can be obtained.2.2 Develop rules for optimizing the information acquisition processOptimize the acquisition process of substation equipment entity information, improve the efficiency of substation equipment entity information acquisition in time, provide more accurate data for substation operation and maintenance personnel, ensure the operation of substations, and reduce the workload of operation and maintenance personnel [15]. Therefore, the substation equipment entity information acquisition process needs to have a low packet loss rate and a high information acquisition speed, that is, reduce non-value-added activities in the process. To this end, the four principles of elimination, simplification, integration and automation of ESIA process optimization are used to optimize the acquisition process of substation equipment entity information. The specific optimization steps of the information acquisition process are as follows: 1) Check the repetitive processes, unnecessary processes and fearless waiting processes in the information acquisition process, and clear them according to the inspection results; 2) Connect the information acquisition process to form a simplified Information acquisition process; 3) Check the scattered processes and parallel processes in the process, connect them together, and use serial processes to replace parallel processes to the greatest extent; 4) Use multiple information acquisition technologies to achieve automatic information acquisition capabilities, reduce Data transfer time. Convert the above-mentioned information acquisition process optimization steps into the system running code as follows:

So far, the software design of the substation equipment entity information acquisition process optimization system has been completed. The substation equipment information acquisition process optimization system designed this time, based on the hardware design, considers that the information acquisition process belongs to the virtual state, adopts AR technology to collect the information acquisition process, determines the constraints, and formulates information process optimization rules, so as to promote the system to have Optimized the process function of obtaining physical information of substation equipment.

3 System test

The substation equipment entity information acquisition process optimization system designed in this test adopts the method of comparative experiment to verify the substation equipment entity information acquisition process optimization system designed this time. In this system test experiment, a certain regional substation equipment entity information acquisition system is selected, the set information acquisition process is taken as the research object of this experiment, and Windows 7 is selected as the operating environment for this software experiment. The designed substation equipment entity information acquisition process optimization system is recorded as A system; the two traditional substation equipment entity information acquisition process optimization systems are respectively recorded as B system and C system. Determine the experimental object and information transmission speed, change the information transmission distance and the number of system iterations, and compare the information acquisition view, packet loss rate and information acquisition rate.3.1 Experiment preparationThe substation equipment entity information acquisition system selected for this test is mainly used to monitor 110 kV substations. The monitoring communication network used is the international IEC 61850 protocol communication to obtain substation equipment entity information. The information acquisition process is shown in Figure 4. Show.

Based on the substation equipment entity information acquisition process shown in Figure 4, three systems are selected to drive and operate Windows 7 64 bit, and the substation equipment entity information acquisition process is optimized. The operating environment of the system is shown in Table 1.

Based on the test object and system operating environment shown in Figure 4 and Table 1, the Pofiler analyzer was used to debug the performance of the three groups of systems, and the Pofiler analyzer was used to check the display capability of the system hardware data, and to check that the three groups of systems were optimizing the substation equipment entity information acquisition process. At the same time, the hardware operation of the three groups of systems is compared, and the effect of the three groups of systems to optimize the acquisition process of substation equipment entity information is compared.

3.2 Comparison of information acquisition views

Based on the experimental parameters set in this experiment, the first group of comparative experiments was carried out. The main transformer main wiring in the substation equipment is selected as the experimental object of this group. As shown in Figure 5a), three groups of systems are used to optimize the substation equipment entity information acquisition process selected in this experiment, and the optimized substation entity information acquisition process is adopted. , respectively obtain the main wiring view information of the main transformer of the substation equipment as shown in Figure 5a). As can be seen from Figure 5, the main transformer main wiring selected in this experiment is a double incoming line structure. The three groups of system optimized substation equipment entity information acquisition process, C system and B system both have the problem of incomplete acquisition of main transformer main wiring view information. Part of the structure of the main connection of the main transformer is obtained; and the optimized process of obtaining the information of the substation equipment entity of the A system obtains all the view information of the substation equipment entity, which is consistent with the actual structure of the main connection of the main transformer.

It can be seen that the design of the substation equipment entity information acquisition process optimization system can optimize the substation equipment entity information acquisition process according to the operation of the substation equipment, and obtain a comprehensive substation equipment entity view information.

3.3 Comparison of packet loss rate for information acquisition

A second set of comparative experiments was conducted based on the results of the first set of comparative experiments. When extracting the first group of experiments to obtain the main wiring information of the main transformer, the information communication speed control system obtains the information and transmits it to the substation information control center, and changes the substation equipment entity information acquisition system. For the acquired information communication transmission distance, set its initial distance as 120m, every 10m to detect the main transformer main wiring information packet loss rate, a total of 5 times, and compared with the unoptimized substation equipment entity information acquisition process, the experimental results are shown in Table 2. It can be seen from Table 2 that the packet loss rates of the three groups of substation equipment entity information acquisition processes after system optimization have decreased to varying degrees, but there are certain differences in their optimization effects.

Among them: System C has the worst optimization effect on the acquisition process of substation equipment entity information. When the communication distance reaches 160m, its packet loss rate is only optimized by 11.7%; B system optimizes the substation equipment entity information acquisition process. When the communication distance reaches 160m, The packet loss rate is optimized by 22.4%. Although the optimization effect is stronger than that of the C system, there is still a high packet loss rate; while the A system optimizes the substation equipment entity information acquisition process. When the communication distance reaches 160m, the packet loss rate is optimized. 78.1%, and the optimization degree is significantly higher than that of B system and C system. It can be seen that the optimized system for the acquisition process of substation equipment entity information designed this time, the optimized substation equipment entity information acquisition process, when the information is transmitted to the control center, is less affected by the communication distance, and will not produce high Packet loss rate.

3.4 Comparison of Information Acquisition Rates

Based on the results of the first and second groups of experiments, a third group of comparative experiments was conducted. The main transformer equipment used in the first group of experiments is selected as the information acquisition object of the substation equipment entity information acquisition system selected in this experiment. In the first group of experiments, the optimized information acquisition process of the three groups of systems was extracted. For the information acquisition rate of the main transformer equipment in the substation, the number of iterations of the system operation was changed, and the initial iteration number of the system was set to 10, and the convergence of the system identification information acquisition was judged. speed, and compared with the unoptimized substation equipment entity information acquisition process. The experimental results are shown in Figure 6. It can be seen from Figure 6 that the optimized information acquisition rates of the three groups of systems gradually tend to be stable with the increase of the number of iterations, but there are certain differences in the optimized information acquisition rates. Among them: the information acquisition rate of system B is only 3% higher than that before optimization, and the optimization of convergence speed is more obvious; the information acquisition rate of system C is only 16% higher than that before optimization, and 13% higher than that of system B, but its convergence The speed optimization effect is obviously not as good as that of system B; while the information acquisition rate of system A is only 23% higher than that before optimization, and the optimization effect of information acquisition process is significantly higher than that of system B and system C, and the number of iterations is less than 10 times, which is significantly less than System B and System C. It can be seen that the optimized system for the acquisition process of substation equipment entity information designed this time, the optimized substation equipment entity information acquisition process has a higher information acquisition rate and a faster convergence speed.

Based on the above three sets of experimental results, it can be seen that the design of the substation equipment entity information acquisition process optimization system can optimize the substation equipment entity information acquisition process according to the operation of the substation equipment, and obtain a comprehensive substation equipment entity view information without being affected by communication. The influence of distance has lower packet loss rate, higher information acquisition rate and faster convergence speed.

4 Conclusion

To sum up, the design of the substation equipment entity information acquisition process optimization system makes full use of AR technology and the ability to connect the real world and the virtual world to form an actual information acquisition flow chart to improve the actual application effect of the substation equipment entity information acquisition process. The substation equipment entity information acquisition process optimization system optimizes the substation equipment entity information acquisition process according to the operation of the substation equipment, and obtains comprehensive substation equipment entity view information. However, the design of the substation equipment entity information acquisition process optimization system has not considered the change and complexity of the substation equipment information acquisition process. Therefore, in future research, it is necessary to further study the substation equipment information acquisition process changes, and information acquisition. The complexity of the process further improves the applicability of the substation equipment entity information acquisition process optimization system. Reference [1] Xiao Li, Liu Zhengyang, Shi Youyi, et al. Reliability assessment of secondary system of smart substation based on Markov model [J]. Electrical Measurement and Instrumentation, 2018, 55(7): 36-40. [2 ] Fu Tianji, Zhang Dan, Wang Jianxin. Optimization and improvement of the rotor magnetic pole main lead structure process of Hainan Qiongzhong Pumped Storage Power Station [J]. Hydropower, 2019, 45(1): 70-72. [3] Shi Lianjun, Pang Bo , Liu Dunnan, et al. Transaction Analysis of the Power Market Composite Index of Beijing Electric Power Exchange Center under the New Power Reform [J]. Automation of Electric Power Systems, 2019, 43(6): 163-170. [4] Liu Linhu, Jin Liming, Xia Qing, et al. Spatial Modeling and Benefit Evaluation of Power System Transmission Operation Elasticity [J]. Automation of Electric Power Systems, 2019, 43(5): 7-13. [5] Feng Yue, Wu Yuekang, Ren Jie, et al. A new test method for the secondary system of a digital substation [J]. China Test, 2020, 46(1): 117-123. [6] Wang Liuhuo, Sun Shuai, Wang Zengbin, et al. Numerical Simulation [J]. High Voltage Electrical Appliances, 2020, 56(1): 24-29. [7] Wang Shuai, Jiang Min, Li Jianglin, et al. Research on key technologies for condition monitoring of all-dimensional intelligent substation equipment [J]. Electricity measurement Yu Gai, 2020, 57(7): 82-86. [8] Wu Di, Tang Xiaobing, Li Peng, et al. Condition monitoring technology of substation relay protection devices based on deep neural network [J]. Power system protection and control, 2020 , 48(5): 81-85. [9] Chen Yang, He Yong. Small UAV Oblique Photography System and Experiment Based on Rural Distributed Photovoltaic Power Station Survey [J]. Chinese Journal of Agricultural Engineering, 2019, 35(22): 305 ⁃313. [10] Chu Zhuang, Xu Jie, Huang Dawei, et al. Reactive power optimization operation of distribution network considering voltage fluctuation [J]. Electrical Measurement and Instrumentation, 2019, 56(11): 61-67. [11] Wang Shuai, Jiang Min, Li Jianglin, et al. Research on key technologies for condition monitoring of all-dimensional smart substation equipment [J]. Electrical Measurement and Instrumentation, 2020, 57(7): 82-86. [12] Lu Gaifeng , Ou Yulei, Du Shuai, et al. Research on internal reactive power optimization of wind farms based on improved HPSO algorithm [J]. Electrical Measurement and Instrumentation, 2020, 57(10): 36-42. [13] Shi Wenchao, Lv Lin, Gao Hongjun, et al. Optimal operation of active distribution network with DG and EV based on information gap decision theory [J]. Electric Power Construction, 2019, 40(10): 64-74. [14] Jiang Aihua, Wei Hua. Communication-based Distributed cooperation model and optimal control of interaction between large-scale air conditioning and power grid [J]. Chinese Journal of Electrical Engineering, 2018, 38(21): 6276-6283. [15] Wang Yi, Ma Qiang, Li Rui, et al. Ag Optimal control of chemical water production process in power plant based on ent architecture [J]. Chinese Science and Technology Papers, 2018, 13(11): 1291-1296.

About the Author:

Li Jie (1982-), male, born in Shiping, Yunnan, engineer, research direction is substation operation and maintenance.

Gao Shugong (1978—), male, from Xinping, Yunnan, senior engineer, his research direction is high voltage and insulation technology, and substation equipment maintenance.

Lei Dong (1983-), male, Sichuan Jingyan, engineer, research direction is primary equipment maintenance test of substation and power system.

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