Construction Of Smart Grid Research Articles

Research on the application of distributed smart grid protection technology scheme

Abstract Distributed smart grid is an organic carrier for achieving renewable energy substitution and promoting new energy consumption. It is an important component of the construction of new power systems. To meet the current operational needs of distributed smart grid construction, a distributed smart grid protection method based on AC/DC hybrid distribution network is proposed. The distributed smart grid protection and application method proposed in this article, has good promotion and application value.

Mobileception-ResNet for transient stability prediction of novel power systems

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

Research on Security Detection of Embedded Terminals in Power Grids Under Artificial Intelligence

Artificial intelligence (AI) has an important significance in the construction of smart grids. There is a problem of low accuracy of behavior detection due to a large number of attacks and long data fragments in the power grid data. In this paper, a behavior detection method based on a generative adversarial network (GAN) is proposed. The method focuses on optimizing the learning strategies of the generator and discriminator based on a GAN. In this regard, the loss function is converged by maximizing the root mean square error of the attack in the discriminator, and the linear activation function is used as the modulation function before the output in the generator. Therefore, the improved GAN can utilize the data enhancement features in the original mode and ensure the stability of attack behavior recognition, and then, effectively detect the attack information in the grid. The experimental results show that the method in this paper can accurately and effectively monitor abnormal attack behavior in the embedded terminals of the smart grid, and its effective detection rate is 97.6%.

Non-Intrusive Load Identification Based on Retrainable Siamese Network.

Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification accuracy and satisfying outcomes. However, unknown load identification is rarely involved among those methods and the scalability of NILM is still a crucial problem at the current stage. In light of this, we have proposed a non-intrusive load identification method based on a Siamese network, which can be retrained after the detection of an unknown load to increase the identification accuracy for unknown loads. The proposed Siamese network comprises a fixed convolutional neural network (CNN) and two retrainable back propagation (BP) networks. When an unknown load is detected, the low-dimensional features of its voltage-current (V-I) trajectory are extracted by using the fixed CNN model, and the BP networks are retrained online. The finetuning of BP network parameters through retraining can improve the representation ability of the network model; thus, a high accuracy of unknown load identification can be achieved by updating the Siamese network in real time. The public WHITED and PLAID datasets are used for the validation of the proposed method. Finally, the practicality and scalability of the method are demonstrated using a real-house environment test to prove the ability of online retraining on an embedded Linux system with STM32MP1 as the core.

PSGformer: A novel multivariate net load forecasting model for the smart grid

Smart grid intelligently transforms modern power system through the introduction of various advanced techniques, promoting the growth of distributed renewable energy sources and improving the efficiency of load management. The construction of smart grid recognizes the importance of net load forecasting, which represents the difference between the demanded load and the renewable energy generation, and has a significant impact on power system monitoring and control. Recently, some Transformer-based models have shown excellent performances in load forecasting. However, most of them perform weakly in face of net load forecasting due to the lack of emphasis on the specific trend variations and the inter-feature dependencies in cyclical temporal patterns. To address the two issues, we present the PSGformer that consists of multiscale series decomposition, seasonal module and trend module, which can focus on unique properties that affect the accuracy of net load forecasting by processing different components obtained from the series decomposition. The seasonal module seamlessly introduces graph attention network into the Transformer to compensate for the capability of capturing local inter-feature dependencies. The trend module adopts a new segment-wise iterative approach to capture trend patterns. Compared to the six baseline models, PSGformer has higher forecasting accuracy and stability on four load datasets from different countries.

EFTA: An Efficient and Fault-Tolerant Data Aggregation Scheme without TTP in Smart Grid

Abstract With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes.

Design and application of VRGIS 3D simulation system for power grid soil and water conservation

Under the development trend of economic globalization, the rapid development of social economy and science and technology provides a new direction for the research of soil and water conservation technology in power grid. As an organic combination of geographic information system and virtual reality technology, VRGIS not only continues the basic functions of traditional spatial data storage and processing, but also has the unreal interface of virtual reality technology, building a new platform for the construction and management of smart grid in the new era. Therefore, after understanding the research status of smart grid construction, this paper mainly studies the design and application measures of 3D simulation system for water and soil conservation of power grid according to the application advantages of VRGIS technology in smart grid management, so as to provide an effective basis for the development of smart grid.

Adaptive plc intelligent control based on the integrated system of smart grid thermal energy conversion

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications.

Research on Power IoT Technology Integrating 5G Communication and Distributed Computing

Abstract With the development of 5G technology and distributed computing, the Internet of Things (IoT) for electric power shows excellent potential in realizing efficient and secure energy management. The objective of this study is to investigate how 5G communication technology can be combined with distributed computing to enhance the performance of power IoT. This paper examines the power of IoT technology that integrates 5G communication and distributed computing. The study is designed to enhance the power system’s efficiency and security. Through experiments and simulations, this study utilizes 5G technology to optimize IoT communication while using distributed computing to enhance data processing. According to the results, the integrated system improves the data transfer rate by 50% and reduces the potential security risk by 25%. The conclusion demonstrates that combining 5G and distributed computing can significantly enhance the efficiency of power IoT, which is crucial for the construction of future smart grids.

Exploration of Energy Meter Data Security and Fast Response Technology in Spot Power Trading

Abstract With the advancement of smart grid construction, higher requirements have been put forward for energy meter data, which need to realize real-time, accurate, efficient, safe, and economical data transmission. In this paper, an intelligent collection of energy meter data is designed to monitor energy meter data. The PCA algorithm is utilized to downscale the energy meter data, and the LMR algorithm is combined to monitor abnormal data from the meter. Based on the smart contract in blockchain technology, a parallel Pedersen commitment algorithm based on privacy protection is designed, a hybrid signature algorithm is created to ensure the secure transmission of energy meter data and a cluster load balancing model for energy meter data is also designed in combination with Hopfield neural network. The PCA-LMR algorithm identifies 95 anomalies of the data in 12 months and under the encryption of a smart contract. The packet loss rate of energy meter data is maintained at about 2%, and after optimizing the cluster load capacity of energy meter data using the Hopfield neural network, the cluster load variance value is about 1.84 in 20 h. Modern technology can improve the secure transmission of energy meter data and enhance its rapid response ability to ensure the economic benefits of the power system.

Traceless Kalman filter threshold estimation for distributed power loads

Abstract Load forecasting is an important part of smart grid construction, energy management, and sustainable design of power systems, and has a great impact on the reliable operation of power grids, facility planning and other decisions. In this paper, we utilize the traceless transform of the UKF algorithm to obtain the sigma feature points generated by the mean value of the high-frequency wavelet components of the power signals and combine them with the EKF algorithm. We put forward a novel neural network hybrid Kalman TUKF algorithm, which will be used to carry out simulation experiments on the distributed electric loads and to estimate the threshold value of the loads in the numerical experiments. The results show that in comparison with the actual measurements, the TUKF algorithm improves by 34.7% in the RMSE metrics, 38.7% in the MAE metrics, and 40.6% in the MAPE metrics compared to the PFWNN. The TUKF algorithm is closer to the real curves and has the best prediction performance for all the time intervals of the prediction. The change of the threshold value has a more obvious effect on the prediction accuracy, and the best effect is in the scale δ = 0.5, i.e., the threshold frequency is selected as the middle value of the intermediate frequency.

Innovation of new energy equipment in the context of smart grid

Charging pile technology is of great significance for the popularization of electric vehicles and the construction of smart grids. By analyzing the development status and trend of charging pile technology against the background of the smart grid, this paper discusses the future development direction of charging pile technology. The research results show that the development prospect of charging pile technology under the background of smart grids is very broad, and it is necessary for the government and enterprises to make joint efforts to solve the problems of high construction costs and unreasonable layout of charging piles through technological innovation and policy support, promote the rapid development of charging pile technology, and provide better support for the development of the electric vehicle market. Charging pile technology is an important device connecting electric vehicles and power grids, which is of great significance for the popularization of electric vehicles and the construction of smart grids. This essay designs a kind of high-power intelligent charging pile which can meet the security protection level, carry out real-time monitoring, real-time data acquisition and interconnection.

Research on a Non-Intrusive Load Recognition Algorithm Based on High-Frequency Signal Decomposition with Improved VI Trajectory and Background Color Coding

Against the backdrop of the current Chinese national carbon peak and carbon neutrality policies, higher requirements have been put forward for the construction and upgrading of smart grids. Non-intrusive Load Monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. This technology obtains detailed power information about the load without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This study first utilized the Long Short-Term Memory Denoising Autoencoder (LSTM-DAE) to decompose the mixed current signal of a household busbar and obtain the current signals of the multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with the voltage signals to generate multicycle colored Voltage–Current (VI) trajectories, which were color-coded according to the background. These color-coded VI trajectories formed a feature library. When the Convolutional Neural Network (CNN) was used for load recognition, in light of the influence of the hyperparameters on the recognition results, the Bayesian Optimization Algorithm (BOA) was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. The experimental results show that the proposed method exhibited better performance in load decomposition and identification than current methods.

Short-term photovoltaic power forecasting based on hybrid quantum gated recurrent unit

Photovoltaic power generation forecasting is crucial for energy management, smart grid construction, and energy markets. This study proposes a hybrid quantum–classical gated recurrent unit (HQGRU)-based framework for forecasting short-term photovoltaic power generation in a time-series manner. The HQGRU model uses a classical layer followed by a quantum embedding circuit to convert classical data into quantum data. Subsequently, variational quantum circuits are used for feature extraction. To demonstrate the performance of the proposed model, we used practical data on photovoltaic power generation and the weather in Busan, Republic of Korea. The results demonstrate the high accuracy of the proposed HQGRU model.

Rotation differential triboelectric nanogenerator for bird-repellent on transmission line towers

The security of transmission line towers is crucial to industrial development. Bird damage is a significant threat to transmission line towers in the field. However, the power supply to the bird-repellent devices is limited. In this work, we propose a rotation differential triboelectric nanogenerator (RD-TENG) to harvest wind energy for supplying electrical power to bird-repellent on transmission line towers. On the transmission line towers, the RD-TENG converts wind energy into electrical energy by rotation differential structure and stores power in a 1000 μF capacitor, then supplies power to the buzzer for bird-repellent. The device mainly consists of a wind cup, an input gear, output gears, and rotors. The RD-TENG can achieve double rotation speed by rotating the differential structure compared to the single gear counterpart, hence doubling the current and power output through triboelectric nanogenerator. The RD-TENG has a starting wind speed of 4 m s−1 and can operate normally at lower wind speeds. At a wind speed of 8 m s−1, the output performance of the RD-TENG is 830 V, 13.5 μA, 330 nC, and the peak power is 19.6 mW. This work realizes a new solution for bird-repellent on transmission lines through the TENG energy harvesting technique and also contributes to the subsequent smart grid construction.

Design and Implementation of Power Big Data Platform

In recent years, with the advancement of the national construction of smart grid and the development of power grid enterprise integration system, the traditional power data platform has some defect, such as insufficient scalability, repeated implementation of two sets of logic of off-line data warehouse and real-time data warehouse, highly correlated storage of data warehouse during computation, and difficulty in platform migration, which restrict the deeper mining and analysis of power big data. Therefore, this paper adopts a technical architecture incorporating distributed calculating, micro-service, flow-batch integration and lake-warehouse integration, combining the current popular Web front-end and back-end technologies such as Vue, SpringCloud and Flask, and utilizing big data components such as Hadoop, Flink, Hudi and Kafka as well as Docker containerization technology. We explore and build a power grid system operation oriented big data platform that assembles real-time computing, multi-source heterogeneous data storage, and develop APIs for authority management, information management, data analysis and visualization, machine learning and other data supporting services that assist power enterprises in managing electrical equipment and users’ power consumption.With the help of this platform, power enterprises can intelligently track the equipment status, view the offline and real-time visual charts to analyze the power consumption by users, so as to have better basis in the power dispatching, maintenance and scheme adjustment of electrical market.

Research on the modeling and control mechanism of three-phase cascade rectification stage of non-power frequency transformer

Non-power frequency transformers are essential and important devices for new energy generation and smart grid construction. This article analyzes the working principle of voltage cascade rectifier stage, constructs a mathematical model of three-phase cascade rectifier stage system, and simplifies its model. Finally, the idea of overall control of the voltage loop in the rectifier stage system was derived, and the traditional control method of rectification was directly transplanted into the overall control system of the cascade rectifier stage. At the same time, the characteristics of voltage loop and current loop of cascade rectifier stage system are analyzed, and the relevant control structure block diagram and controller design idea are given, which lays an important theoretical foundation for in-depth study of non-power frequency transformer.

Applying the Geometric Features of Cumulative Sums to the Development of Event Detection

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s.

Research on environmental protection and water and soil conservation technology of power grid engineering mechanized construction based on modular design

In the construction and development of modern society, power grid engineering as an important content of urban construction and development and protection of residents' quality of life, in the face of the expanding scale of power grid investment, power grid project construction shows a rapid growth trend. With the continuous development of social economy and smart grid construction needs, the requirements of power grid construction quality and construction period are getting higher and higher. On the basis of understanding the research status of mechanized construction of power engineering, this paper mainly discusses the principle and structure of modular design by taking the actual soil and water conservation measures on site as an example, and then takes the mechanized construction of power engineering in a certain area as an example to determine the construction environmental protection technology, in order to improve the comprehensive level of China's power grid construction.

Discussion on the integration of intelligent substation operation and maintenance

In the development of intelligent power grid system construction in China, the scale of urban power grid construction is getting larger and larger. How to realize the integration of substation operation and maintenance has become the main issue explored by scholars in the field. Because there are still many problems in the operation and maintenance management of intelligent substation, such as ignoring safety measures, rising work risk factor, lack of high-quality talents, etc., the construction of smart grid should explore how to change from the traditional management mode to the integrated operation and maintenance mode. After understanding the basic concept of integrated operation and maintenance mode of intelligent substation, this paper mainly explores the system construction and application effect of integrated operation and maintenance management of intelligent substation according to the basic objectives and common problems of practical management, and finally proposes effective countermeasures for specific problems.