Smart Grid Applications Research Articles

Evaluation of radiality constraints for power distribution networks

This work carried out an empirical study with the objective of determining the most efficient strategy in guaranteeing the radiality of the distribution network during the reconfiguration. The reconfiguration problem in a distribution network aims to reduce technical losses by changing the paths that energy takes through the network until it reaches consumers. In this problem, a mandatory constraint is to ensure that the network operates radially. However, the literature does not present a consensus on the best way to guarantee this restriction. Computational experiments were carried out using the two main methodologies in the literature: limiting the number of input edges; and search with removal of cycles in each partial solution. A third and new strategy was proposed as an alternative to the literature using the concept of minimal cuts in graphs. Computational tests showed that the strategy of limiting the number of input edges was the most efficient in ensuring radiality. We also point out a drawback in the applicability of this strategy to networks with high penetrations of distributed generation (DG). For this reason, our experiments also analyze the other strategies that are suitable for DG power flows. For this case, we show that the methodology proposed in this paper provides a better way to tackle modern networks arising from applications of smart grids.

Hybrid Communication Architecture Based on Hierarchical Computing for Low Voltage Power Network Automation in Developing Countries

Utility companies in developing countries, such as Tanzania, have made significant advancements in automating the generation, transmission, and primary distribution segments of their electrical grids. However, automation in the Low Voltage Power Network (LVPN) remains minimal and largely unmonitored, leading to manual fault management that results in losses and inconveniences for both utilities and customers. Effective Smart Grid (SG) infrastructure relies on seamless communication between end-devices and controlling systems to ensure timely execution of critical applications. Key SG applications for LVPN automation include Advanced Metering Infrastructure (AMI), Distributed Energy Resource (DER) coordination, and Distribution Automation (DA), each with specific latency requirements that must be met. Current architectures outlined in the literature present challenges for utility companies attempting to implement these applications while satisfying latency criteria for automation. This paper introduces a hybrid communication architecture based on a three-level hierarchical computing model specifically designed for LVPN automation. Laboratory results indicate that this architecture achieves an average delay of 7.059ms, significantly below the maximum allowable latency of 20ms for AMI, DER, and DA which demonstrates the architecture's capability for effective LVPN automation.

Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems.

Application of Digital Twin Technology in Substations: High Voltage Reactor Meter Visual Field Analysis of Phase A

This paper explores the application of Digital Twin (DT) technology in the monitoring and maintenance of high-voltage reactor meters within substations. Traditional inspection methods face limitations in covering the extensive areas and complex structures within substations, which DT technology can address by providing real-time monitoring, predictive analytics, and data-driven insights. The study focuses on visual field analysis for optimizing camera placement and configuration around high-voltage reactors, enhancing inspection accuracy and operational efficiency. Key components include IoT sensors, real-time analytics, and advanced visualization techniques. The findings suggest that DT-enabled systems significantly improve the reliability, safety, and efficiency of substation management, demonstrating potential for broader smart grid applications.

Preface

Focusing on the latest research development and progress in the fields of power system engineering and smart grid, the 2024 International Conference on Power System Engineering and Smart Grid (PSESG 2024) was held in Beijing, China from August 16th to 18th, 2024. It was presided over by North China University of Technology (NCUT), with the full support of many universities, research institutes and enterprises at home and abroad. PSESG 2024 was created to build a high-quality academic forum to exchange and share successful experiences of innovation in the domains of power system engineering and smart grid, and establish an effective cooperation platform for researchers and scholars from universities, research institutes and enterprises all over the world. Calling for papers on such topics as Transmission and Distribution System and Equipment, Electrical and Energy Circuits and Systems, Power System Planning and Dispatch, Power System Security Defense and Restoration Control, Smart Grid Deployment, Smart Grid and Distributed Energy, etc., this conference attracted about 80 scientists, engineers, practitioners and graduate students worldwide to exchange their current technical application experience and research results, so as to promote the development of relevant fields and industries. The conference took place for three days, among which day two could be deemed the most critical. On behalf of the organizer, Professor Jinghua Zhou (North China University of Technology), first made an opening speech, introducing NCUT and its cross-disciplinary characteristics of energy storage to the experts and scholars attending the conference, and expressing his sincere welcome to the attendees. And next is the keynote speech part performed by several renown experts, including Professor Shunli Wang (Inner Mongolia University of Technology), Professor Zechun Hu (Tsinghua University), Professor Jinghua Zhou (North China University of Technology), Professor Yu Wang (Chongqing University), and Associate Professor Chenye Wu (The Chinese University of Hong Kong). Their speeches not only covered the latest advances in power system engineering and smart grid, but also explored the innovative application of energy storage technology and the trend of the digital transformation of power systems, providing participants with valuable academic insights. Then, the oral and poster presentation mainly presented by young scholars were held to showcase their research achievements. They talked about the technical principles, operational characteristics, optimization methods, as well as challenges and solutions in practical applications of power system and smart grid from different perspectives, stimulating the spark of thought collision and promoting the deepening of academic cooperation. Discussion and Question and Answer happened throughout the meeting. We would like to thank all the authors and speakers for their hard work and wonderful contributions, and all the experts and scholars for their attention and support to this conference. We believe that with our joint efforts, research and application in the fields of power system and smart grid will achieve more remarkable results and make greater contributions to the cause of sustainable development of mankind. Finally, we look forward to meeting again in the next International Conference on Power System Engineering and Smart Grid to discuss the future development path of power system and smart grid. List of Committee Member is available in this Pdf.

Lightweight Scheme for Security and Privacy in Smart Grids Applications

Lightweight Scheme for Security and Privacy in Smart Grids Applications

Electrode Engineering Using Carbonaceous Materials for Improving Electrochemical Performance of High-Voltage Lithium-Ion Batteries

Introduction Lithium-ion batteries (LIBs) with high energy density, long cycle life, and affordability are crucial to meet the soaring demand for energy storage in electric vehicles and smart grid applications. The cathode material, a key constituent of LIBs, plays a pivotal role in determining energy density and cycle life. Therefore, improving cathode characteristics is typically a top priority to enhance battery performance. Among the array of cathode materials available, spinel LiNi0.5Mn1.5O4 (LNMO) stands out due to its high operating voltage (~4.7 V vs. Li/Li+), rapid ion transport, eco-friendliness, and cost-effectiveness, capturing researchers' attention. However, LNMO-based LIBs often encounter significant capacity deterioration in high-voltage environments. Factors such as Al foil (current collector) corrosion [1], active material deterioration [2], and electrolyte decomposition [3] are identified as leading causes of battery failure. This study aims to boost the electrochemical performance of high-voltage LIBs by employing advanced LNMO-based cathodes infused with various carbonaceous materials. Experimental Three approaches, utilizing three distinct carbonaceous materials, were employed to integrate with LNMO active materials for the fabrication of advanced LNMO-based electrodes.In the first approach, carbon fibers (CFs) were utilized as current collectors, replacing the conventional Al foil. A slurry comprising LNMO-based active material, conductive agents, binders (optional), and solvents was thoroughly mixed and then cast onto CFs either through blade coating or vacuum filtration. After overnight drying, CF-based electrodes were obtained.For the second approach, carbon blacks (CBs) were employed as conductive agents, which were subjected to various treatments including thermal, hydrothermal, and chemically oxidative reactions for modification. The modified CBs were then mixed with LNMO-based active material, binders, and solvents to form a slurry. The subsequent electrode fabrication process mirrored that of the first approach, utilizing blade coating.In the third approach, polyvinylpyrrolidone (PVP) was introduced as an alternative to the commonly used PVDF binder. PVP was mixed with LNMO-based active material, CBs, and solvents to create a slurry. The resulting slurry was blade-coated onto CF substrate and subjected to a two-step thermal treatment, resulting in a partially carbonized PVP-based electrode.The electrode materials underwent characterization using various techniques such as SEM, TEM, XRD, XPS, nitrogen adsorption, and Raman spectroscopy. Electrochemical tests were conducted using coin-type test cells (CR2032) to assess the tailored LNMO-based electrodes in terms of cycling stability, rate capability, and internal impedance. In these test cells, lithium metal foil served as the counter electrode, and 1.0 M LiPF6 in ethylene carbonate/diethylene carbonate (1:1 v/v) without additives functioned as the electrolyte. Results and Discussion Our findings indicate that the LNMO-based electrode utilizing CF as current collectors demonstrates significantly enhanced cycling stability and rate capability compared to its counterpart using Al foil. A typical electrode can maintain 98% capacity retention over 200 cycles under high-voltage operation (4.9 V vs. Li/Li+) and exhibits favorable rate performance with approximately 120 mAh g-1 at 5 C. This improvement is attributed to the 3D electrically conductive framework of CFs, which facilitates rapid electron/ion transport within the electrode. In terms of the conductive agent, hydrothermally treated CBs contribute to the enhanced high-voltage performance of LNMO-based cells due to their specific nitrogen-containing groups and structural defects. Furthermore, the partially carbonized PVP (PC-PVP) also positively impacts the cycling and rate performance of LNMO-based cells. The conformally coated PC-PVP layer functions not only as an electrically conductive binder but also as a protective barrier that mitigates electrolyte degradation at high voltages. Overall, these findings pave the way for a deeper understanding of the roles played by carbonaceous materials in high-voltage LIBs, offering a new avenue for future research and development in this field.

Phasor estimation using micro-phasor measurement unit (μPMU) in distribution network for situational awareness

Phasor estimation plays a critical role in island detection, fault detection, voltage stability monitoring, and state estimation within power systems. This paper proposes a phasor estimation method using micro-Phasor Measurement Units (µPMUs) in a Smart Distribution Network. A µPMU model is developed utilizing a recursive Discrete Fourier Transform (DFT) algorithm combined with high-precision filters to enhance accuracy. The optimal placement of µPMUs in the distribution network is determined using a Mixed-Integer Linear Programming (MILP) approach for the IEEE 33 bus test system. The model is tested on the IEEE 33 bus system with Electric Vehicle Charging Stations (EVCS) and Distributed Generators (DGs) under both nominal and off-nominal frequencies. The results comply with the IEEE C37.118.1 standard, achieving phasor magnitude errors as low as + 0.01% and frequency accuracy of 1 mHz with minimal ripple. This study demonstrates the robustness and precision of the µPMU model, highlighting its potential for enhancing real-time monitoring and control in smart grid applications.

Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models

AbstractSmart grids represent a paradigm shift in the electricity industry, moving from traditional one‐way systems to more dynamic, interconnected networks. These grids are characterised by their intelligent automation, robust structure, and enhanced interaction with customers, backed by comprehensive monitoring and data analytics. The key of this transformation is the integration of data‐driven methods into smart grids. Compared to previous big data solutions, large language models (LLMs), with their advanced generalisation abilities and multi‐modal competencies, are crucial in effectively managing and integrating diverse data sources. They address challenges such as data inconsistency, inadequate quality, and heterogeneity, thereby enhancing the operational efficiency and reliability of smart grids. Furthermore, at the system level, LLMs improve human–system interactions, making smart grids more user‐friendly and intuitive. Last but not the least, the structure of LLMs performs inherent advantages in bolstering system security and privacy, alongside in resolving issues related to system compatibility and integration. The paper reviews the data‐empowered smart grids and for the first time finds and proposes opportunities and future directions for adopting LLMs to accelerate the mass‐scale application of Smart Grids.

Smart Grid in the Arctic City

This article presents the evolution of a smart grid in an artic city, and we analyze its development using the Smart Grid Reference Architecture (SGAM) and from historical, technological, and social points of view in an Arctic context. We illustrate the emergence of smart grid application and its relation to energy consumption habits and sustainability. The study includes observations from empirical research conducted using a mixed methods approach. This included two months of organizational ethnographies consisting of interviews with specialists at an electricity utility, shadowing the workers, participant observation in the control center, and a questionnaire survey among local residents. Three stages of transformation in the electricity network were observed: 1) the conversion from analog to digital in network operations, with residents as passive receivers of electricity; 2) introducing digital communications and a digital local area network that allowed residents to report consumption; 3) the electrical network as an evolving smart grid with a digital platform enabling two-ways communication among network actors. People became more active when smart grid applications were introduced in the electricity network and middle-aged men can now better manage their energy consumption, even though their motivation is financial, rather than environmental. At the same time, unplugging from the smart grid becomes increasingly difficult.

Attention Based Energy Demand Forecasting in Smart Grid Environments

The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8% better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 1/3 of the training time other models took. We also improved performance by 42% in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

Hybrid Triboelectric‐Electromagnetic‐Electric Field Energy Harvester for Simultaneous Wind and Electric Field Energy Capture in High‐Voltage Transmission System

AbstractWith the development of smart grids, efficient condition monitoring of high voltage transmission system has become crucial, necessitating reliable power supplies for distributed sensors. Traditional energy harvesters often focus on either internal or external sources, limiting overall efficiency. This study introduces a triboelectric‐electromagnetic‐electric field hybrid energy harvester (TEE‐HEH) that synergistically integrates triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), and electric field energy harvesters (EEHs) to simultaneously capture electric field and wind energy. Electric field energy is harvested via displacement currents between transmission lines and the ground, while TENGs and EMGs efficiently capture low‐ and high‐speed wind energy, respectively, enabling broadband harvesting (2.3–10 m s−1). The synergistic combination of TENG, EMG, and EEH within the TEE‐HEH leads to significantly enhanced energy capture efficiency from multiple sources. At a wind speed of 5 m s−1, a transmission line voltage of 25 kV, and a distance of 1.5 m, the TEE‐HEH achieved peak power outputs of 18.5 mW (TENG), 262 mW (EMG), and 1.85 mW (EEH), demonstrating enhanced energy collection efficiency. An environmental monitoring system has been powered, demonstrating the TEE‐HEH's practicality for dual‐source energy harvesting in smart grid applications.

Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems

Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

Improvement of Arc Erosion Resistance of Ag–SnO2 Contact Materials by Reducing Molten Pool Size

The splash of molten Ag under arc erosion is the major reason for the failure of Ag‐based contact materials. Changing the size of the molten pool may manipulate the splash process and suppress the arc erosion. Herein, the size effect by fabricating a SnO2 network with pores smaller than the typical Ag molten pool is investigated. Silver is subsequently infiltrated into the SnO2 network to form Ag–SnO2 interpenetrating contact materials. It is found that the SnO2 network with small pore sizes separates the Ag matrix into smaller regions, reducing the melting volume. Compared with the particle‐dispersed one, the interpenetrating composite decreases ≈90% mass loss and temperature rise, as well as provides superior microstructure stability. This finding demonstrates a promising way to improve the arc erosion resistance of contact materials by tailoring the size of molten pool, benefiting long‐lifetime contact materials for smart grid applications.

Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

A Review on Privacy Protection Techniques in Smart Grid Applications

: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.

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.