Band 24, Heft 4 - Special Issue on Smart Energy Infrastructures for Smart Cities

In some special geological areas, the inclination and displacement of transmission line towers are relatively common, which should be analyzed from various factors. Transmission towers are the support structure for overhead transmission lines and play a pivotal role in the safe operation of the power grid. Transmission lines are widely distributed. Transmission poles and towers are generally built in areas with poor geological conditions such as goaf, river bed and slope. Under severe weather conditions, the force on the tower may be affected to a certain extent, causing the tower to tilt, deform, or even collapse, and thus causing a wide range of power system failures, which have a great impact on people’s production and life. Based on this objective problem, this paper has mainly studied the intelligent recognition methods and key technologies based on machine learning. In the experimental study of support vector machine (SVM) model based on machine learning, the average training time of genetic algorithm support vector regression (GA-SVR) was the longest, reaching 1.462 s. The average training duration of double chain quantum genetic algorithm-least squares support vector regression (DCQGA-LSSVR) was the shortest, with 0.156 s. The average pose error of double chain quantum genetic algorithm-support vector regression (DCQGA-SVR) was the smallest, only 0.136, while the average attitude error of genetic algorithm-least squares support vector regression (GA-LSSVR) was the highest, reaching 0.45. Therefore, it is of the great significance to analyze abnormal vibration of the transmission towers based on machine learning method.

With the acceleration of power grid modernization, the scale of power grid is expanding. Running operations are becoming increasingly complex. There is a great demand for the analysis and calculation of the operation status of the power grid, line transformation design, fault analysis and treatment, etc. At the same time, with the construction of modern power grids and the continuous development of the power industry, the security problems of power systems have become increasingly prominent. This paper introduced the background of the power data service platform and then conducted academic research and summary on the research and development and design of power data service platform and multidimensional power data service platform. Then it was summarized with the analysis of international journals of emerging power systems. After establishing the algorithm model, the spark platform algorithm framework various algorithms were proposed to provide a theoretical basis for the R&D (research and development) and design of the multidimensional power data service platform. Then it analyzed the factors related to the R&D and design of power data service platform. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average value of the neural network on the three criteria is 78.03 %. Therefore, the stability rate, accuracy rate, and recall rate of such algorithms as neural network are excellent in the research and development and design of power data service platform.

The daily production and life of human beings are inseparable from electricity. As the power supplier, electric power enterprises provide power demand for the majority of users. In the new era, electric power enterprises are also facing market-oriented reform. The focus of reform is electric power marketing. The formulation of electric power marketing strategy needs scientific decision-making analysis as guidance. With the increase of power demand, the scale of power grid is also expanding continuously. With the introduction of new energy equipment, the power system is becoming more and more complex, and it is difficult for relevant staff to effectively monitor and analyze the system. Combined with the above situation, this paper combined Bayesian network to build a power marketing decision analysis system, and combined Bayesian algorithm to test the power marketing real-time cost control system. The experimental results showed that the average judgment accuracy was 91.90 %, and the average warning time was 0.39 s. From the above data, it can be seen that this algorithm can play a good optimization effect on the performance of the system. In this paper, the elasticity test of the power system was also carried out from the aspect of wind speed, and the results showed that the maximum elasticity value can reach 0.94. It can be seen that the elasticity effect of the power system is good as a whole under different wind speeds.

With the rapid development of social economy, the demand for electric power engineering is gradually increasing. The power supply system is constantly developing in the direction of large space and automation, and various high and new technologies are also constantly improving. The power monitoring data access control system is used to monitor and control the power production and supply process and improve the power supply efficiency. The further development of the region also has a higher demand for power and energy supply. For the problem that the natural environment of transmission and distribution lines in various power grids is uncertain, which makes the line operation unsafe. This paper proposed a power monitoring data access control system based on BP (back propagation, abbreviated as BP) neural network. This paper described the related concepts of BP neural network and power monitoring system, and described the functions and construction methods of power monitoring data access control system. On this basis, relevant experiments were carried out to verify the performance of the proposed system. The experimental results showed that the fault detection accuracy of the traditional algorithm was about 93 %, while the fault detection accuracy of the algorithm in this paper was more than 98 %. The highest accuracy rate was 99.88 %, and the accuracy rate of fault detection was greatly improved.

Optical flash payment technology is a form of optical flash payment application. Its payment scenario is mainly to directly realize “card free payment” in the consumption process through mobile devices, Bluetooth and other connection methods on mobile terminal devices such as smart phones under the mobile Internet environment. The “flash payment” is a more convenient payment method and a new payment channel without the function of electronic wallet. Compared with the current mainstream payment methods, it is more collaborative and complementary. Aggregated payment is a fourth party payment platform, which is different from Alipay, WeChat and other third payment platforms. Aggregated payment is a combination of technology and a platform that combines multiple payment methods. Based on the background of massive distributed power generation connected to the grid, this paper studied the key technologies of flash payment in combination with marketing blockchain, and discussed the data sharing mechanism of marketing information in multiple scenarios from the perspective of marketing blockchain by studying the data sharing mechanism in different scenarios. In this paper, a distributed optical flash payment system and a blockchain based encryption algorithm were proposed. Based on this research, the experimental results of key technologies of aggregated optical flash payment based on marketing blockchain were analyzed. The experimental results in this paper showed that 38.7 % of the users contacted the aggregation payment through the online platform. 11.7 % of people communicated through chatting with friends, and 9.8 % of enterprises got in touch through preferential policies. 4.5 % of them publicized through the media, and 8.3 % made requests through payment plans. 21 % of them transferred money through remittance, and 6 % of them got it through other ways. When using aggregate payment, the main reasons for users to choose aggregate payment were the network, friends chatting, and the acceptance of other people’s remittances. In the case that users used aggregate payment services, they most often used transfer and remittance, and most of these purchases were paid through mobile payment and credit card. In terms of users’ use of third-party services, it could be seen that transportation services and movie tickets were the most common ways of use. In terms of the aggregation payment strategy, the improvement of the three levels of aggregation payment could improve the risk prevention ability. First, the complaint mechanism was established to detect and correct potential vulnerabilities in the system in a timely manner. Second, consumers’ sense of self-protection should be strengthened. Third, the legal construction of the payment industry should be promoted to increase the strike force against illegal use.

In order to improve the CNC roll grinder production efficiency, a novel three-point non-contact measurement device is proposed. An adaptive back-stepping control system that combines with a radial basis function neural network (RBFNN) was designed to control the measurement device working position and also to track the periodic reference movement trajectory. In the proposed control system, the RBFNN approximation and tracking ability are used to identify the unknown measuring device dynamic information. Then, the Lyapunov stability theorem is used to derive the adaptive online learning algorithm. All control algorithms are placed in the control chip based on the TMS320F28335 archive. The simulation results show that the proposed control system has a good control effect when applied to the three-point non-contact measuring device in CNC roll grinders.

By measuring and analyzing the vibration signals of the transformer core and windings, the operating conditions of the transformer can be diagnosed. In this paper, the surface vibration signals of 500 kV autotransformer with different structures are measured, and the frequency domain characteristics of the surface vibration signals are analyzed. The blind source separation algorithm is used to separate the surface vibration signals and obtain the vibration signals of core and winding respectively, which provides strong data support for further condition evaluation and fault diagnosis of the core and winding using their vibration signals. Firstly, the surface vibration signal measuring points are set according to the specific arrangement of the core and winding of the transformer; secondly, the vibration signals of different vibration measuring points of the transformer under the same load and different loads of the same vibration measuring point are measured respectively; finally, the frequency characteristics of the measured vibration signals are compared and analyzed. Then, use blind source separation algorithm for transformer surface vibration signal processing, the two signals obtained are close to the vibration characteristics of core and winding respectively, and the separated, it is proved that the accurate vibration signals of core and winding can be obtained by blind source separation, it is helpful for further analysis of the operation state of the core and winding.

At a time when global climate change poses a major challenge to human survival and development, all countries believe that people must actively deal with green and low-carbon (LC) development. As one of the hot topics in recent years, the concepts of “carbon neutral” and “carbon peak” have a significant impact on the construction of New Energy (NE) systems. The construction of NE systems with NE as the main component is an important way to achieve the “double carbon” goal. Affected by the “double carbon” goal, the energy system has shifted to a low carbon and high intensity system, reducing the dependence on fossil fuel power generation, accelerating the use of clean energy, and promoting the supply system of low carbon and clean energy. In order to improve the LC construction of electric power, this article studied the key technologies of LC index construction of electric power under the background of “double carbon”. This article first introduced the LC construction of urban electric power, including the connotation of LC electric power, the current situation of NE development and the difficulties of LC construction of electric power in the context of “double carbon”. Then this article constructed a new type of LC Power System (PS) on the basis of double carbon, and introduced the framework of the system and the key technologies included in the system. Finally, this article verified the effectiveness of the new LC PS in LC construction through experiments, and put forward suggestions for the realization of LC power construction. The experiment showed that the new LC PS can greatly promote the construction of the LC index of power, and is conducive to the development of urban power. The proportion of urban non-fossil energy and the new installed capacity of non-fossil energy power generation have been significantly increased. Among the four cities studied in this article, the proportion of non-fossil energy in two cities has increased from less than 50 % to more than 50 %, which has greatly promoted the construction of LC index of electricity.

In recent years, due to the energy crisis and serious environmental problems, the use and development of new energy has become the focus of research. Therefore, the application of new energy technologies has increased the demand for direct current to direct current (DC-DC) converters. The improvement of converter capability effectively enhances the functions of DC-DC converters, which makes it easy for secondary converters to meet the requirements of voltage gain, and expands the input voltage range of DC-DC converters, thereby having a good prospect in new energy applications. However, due to the many drawbacks of the quadratic Boost converter, such as pressure rise limitation, high switching stress, in order to reduce the working pressure of the improved quadratic Boost high gain converter and improve its operating performance, it is necessary to optimize it. Based on the application scenario of an modified converter for quadratic Boost high gain DC-DC, and taking the constant off time control method as an example, this paper conducted research to accurately recognize the current status of the converter and improve the utilization efficiency of the converter. In this paper, four groups of traditional converters and two types of improved quadratic Boost high gain DC-DC converters were selected as experimental objects for comparative analysis of images and data. The results showed that the conversion efficiency of the constant off time control method in the modified DC-DC converter based on quadratic high gain was 7.03 % higher than that of the traditional converter, while also improving the conversion efficiency of voltage gain and reducing switching stress, with higher safety. This indicated that the improved DC-DC converter with secondary high gain has better effects and wider application scenarios, thereby better serving new energy systems.

Edge computing gateway automation system is integrated in edge computing gateway. One of the main functions of edge computing system is to connect industrial instruments and communication equipment in the process of industrial production. It provides real-time data monitoring and analysis, and initiates responses to predetermined logical events. The operation process includes separating and designing different processes in a certain order. The production and processing process is susceptible to problems such as long production and processing cycles, multiple types of monitoring data, large amounts of processing data, and data vulnerability to external interference, which leads to inaccurate and unsynchronized data. Based on this, this article investigated the analysis of data processing systems based on cloud computing, focusing on analyzing the system architecture and processing, and elaborating the design of data collectors. Then, this article analyzed the efficiency of AI (artificial intelligence) automatic control system and data processing unit. This article discussed the application of AI in collecting and processing data, the composition of the data management module of AI automatic control system, and the data processing in the data module of AI automatic control system. This paper also described the construction method and process of the automatic control system of edge computing gateway, and discussed from the following aspects: data preprocessing module, data classification processing module, data accumulation analysis module, automatic control algorithm logic module, and instruction execution control module. Experiments and investigations showed that the accuracy of data analysis by using the new AI automatic control system and data processing system was 0.11 higher than that of traditional automatic control systems and data processing systems. The data processing effectiveness of the new AI automatic control system and data processing system was 0.10 higher than that of the traditional automatic control system and data processing system. By using AI technology and edge computing technology to structure the automatic control system and data processing system, a new AI automatic control system and data processing system were constructed, which were 9 % more satisfied than the traditional automatic control system and data processing system.

With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the interference of the external environment, and continuously and accurately detect and identify the fire situation.