Intelligent Power System Research Articles

Application Research of Power System Automation Based on Electrical Automation Technology

Power is an indispensable energy in our daily life, and the power system is to manage and maintain the normal supply of electricity, which directly affects the stability of the entire power network. At present, China’s power network is in an important upgrading stage, from the traditional power system to intelligent power system, in this process, automatic electrical technology plays an important role. Therefore, based on electrical automation technology, this paper puts forward the application research of power system automation. This paper deeply studies the power system and the application of automation technology in power system in China. It is considered that PLC and automatic power detection technology, which are extended from electrical automation technology, play an important role in the whole power system operation process. But at present, there are still many deficiencies in the power system based on electrical automation technology, including the problem of large measurement error. Therefore, this paper proposes an improved optimization method. The experiment shows that the maximum error of the improved method is 0.125%, especially in the case of synchronous current power grid frequency of 50 Hz, the error rate is 0. The results show that the optimized method has good measurement accuracy. According to the actual application of the existing electrical automation technology in the power system, this paper puts forward the optimization and improvement measures, and forecasts and analyzes the future application development trend.

Fault dynamic monitoring of intelligent power system telecontrol dispatching based on improved fault tree

The number of transmission messages is small when the traditional power system monitoring methods collect signals, Therefore, a fault dynamic monitoring method of intelligent power system telecontrol dispatching based on improved fault tree is proposed. The telecontrol communication model of intelligent power system is constructed. The communication signal of telecontrol dispatching channel is collected. The parameter of signal time-domain characteristic is measured. The time-domain waveform can pass through the zero period to get the carrier frequency range, judge whether the system telecontrol dispatching has fault, and analyze the fault mode by improving the graphical logic deduction of fault tree. A comparative experiment is carried out. Compared with the traditional methods, the results show that the design method can improve the number of packet transmission per unit time, increase the network traffic, and ensure that the communication network has very low load in the dynamic monitoring process.

A methodical approach and computer programs to determine the power of active harmonic filters

Poor power quality creates big problems. With the poor power quality, the service life of the equipment is reduced. In connection with the creation and development of intelligent electrical power systems and the digital economy, the requirements for the power quality are increasing. Active harmonic filters began to used to solve problem with the poor power quality. The paper presents a methodical approach and computer programs for determining the power of the active harmonic filter. The power supply system of the Vietnamese coal sorting plant is considered to develop the methodical approach. The results of measurements of the power quality indices and the results of calculating the parameters of the modes of the electrical network of the plant at the fundamental frequency were used. To process a large amount of measured information, the special computer program was developed. On the basis of the methodical approach, the results of measurements of the power quality indices and the computer complex “Stationary permissible optimal mode”, the algorithm and the computer program have been developed to calculate the power of the active harmonic filter. The power of the active harmonic filter for the coal sorting plant was determined with the help of this program.

Insulator Surface Breakage Recognition Based on Multiscale Residual Neural Network

Insulators are one of the widely used important devices in power systems, and proposing an accurate and effective online monitoring method for insulator defects is an urgent need for intelligent and informative power systems. We propose a multiscale residual neural network (MSResNN) for insulator surface breakage recognition. The proposed multiscale convolution filter uses three different size convolution kernels for convolution filtering and performs feature maps fusion, which can enrich the spatial correlation and channel correlation of feature maps. We investigated the effects of different learning rates on the model performance and confirmed that the model performs best with learning rate of $10^{-6}$ . Compared with VGG16, Inception_v3, and Resnet50, the MSResNN model has significant advantages in detection capability and occupies less computational resources, and the detection speed can meet the industrial real-time detection requirements. Through dark–light and noise experiments, it is demonstrated that the proposed method is robust and can effectively deal with low-quality insulator images.

MGCAN: A Novel Method for Visual Power Monitoring Systems

Imaging quality judgement provides useful meaning to benefit intelligent visual power systems. However, accurate identification of possible failures of the monitoring equipment remains challenging. This paper proposes a novel deep architecture to improve the assessment of abnormalities by considering a novel mutual-guided convolutional attention network (MGCAN) and a multi-region scheme. More specifically, the MGCAN exploits various adequate, low-level information to generate spatial and structural flows. Accordingly, it further extends attention mechanisms to model both inter-flow correlations and inter-channel relationships in the network design. The multi-region scheme features a spatial pyramid random-crop strategy and a region-fusion strategy to handle locally non-uniform characteristics among categories. In this way, the whole architecture provides an end-to-end and adaptive learning procedure relevant to quality perception that focuses on learning important features and mining discriminative regions. Experimental results demonstrate its superiority to prior methods for judging abnormalities. The proposed method can be easily extended to an entire surveillance system.

Analysis of the influence of non-sinusoidal and unbalanced network modes on induction motors

The parameters of electrical network modes do not correspond the requirements GOST 32144-2013 of Russian and the National technical regulation of Vietnam. In real operating conditions in electrical networks in non-sinusoidal and unbalanced modes there are harmonic components of voltages and currents as well as voltages and currents of negative sequence. They cause additional losses of active power, which leads to additional heating and causes premature aging of the insulation, and as the result, the reduction in the service life of induction motors. Currently, we see that the process of formation of intelligent electrical power systems is underway. Systems for continuous monitoring of power quality indices and parameters of electrical systems modes are being developed. These systems can be supplemented with programs for calculating characteristics that issue the warning when the unfavorable influence of the parameters of non-sinusoidal and unbalanced modes is detected on various electrical equipment of both electrical power systems and consumers of electrical energy. The paper provides an overview of the characteristics used to analyze, assess and predict the influence of poor power quality associated with non-sinusoidal and unbalanced of currents and voltages on induction motors. A computer program was developed to calculate these characteristics. The program was used to study the influence of non-sinusoidal and unbalanced modes on the induction motors of the coal sorting plant of the Vietnamese company “Cua Ong-Vinacomin”.

ОБЕСПЕЧЕНИЕ ОПТИМАЛЬНОГО УРОВНЯ ПОТЕРЬ ЭЛЕКТРОЭНЕРГИИ В СИСТЕМАХ ЭЛЕКТРОСНАБЖЕНИЯ ПРЕДПРИЯТИЙ

On the basis of the system approach, the analysis of the basic technologies of the intelligent electric power system that provide adaptive management of the electric network is carried out. The expediency of their application for adaptive power quality management in electric networks with traction load by expanding the functions is shown. The variants of their configuration are proposed to improve the quality of electricity and ensure optimal parameters of electricity quality indicators.

ПРИМЕНЕНИЕ ТЕХНОЛОГИЙ АКТИВНО-АДАПТИВНЫХ СЕТЕЙ ДЛЯ УПРАВЛЕНИЯ КАЧЕСТВОМ ЭЛЕКТРОЭНЕРГИИ В ЭЛЕКТРИЧЕСКИХ СЕТЯХ С ТЯГОВОЙ НАГРУЗКОЙ

On the basis of the system approach, the analysis of the basic technologies of the intelligent electric power system that provide adaptive management of the electric network is carried out. The expediency of their application for adaptive power quality management in electric networks with traction load by expanding the functions is shown. The variants of their configuration are proposed to improve the quality of electricity and ensure optimal parameters of electricity quality indicators.

Research of a smart energy system in the internet things

The work considers and investigates the intelligent power system in the Internet of Things. The purpose of the article is to study the intelligent network. The answer to the question «What is a smart energy system?». We compared traditional and smart power systems. Defined measurement infrastructure and technological criteria. Protocols and standards are reviewed. The relevance of the topic is because every day the demand and consumption for electrical energy is growing. The evolution of smart grid concepts in various power grids connects a huge number of devices, many of which are due to outdated automation protocols and internal information systems. Since many of these devices provide little or no indication of the semantics of their data, it is customary to manually create this information, which is cumbersome, expensive, and prone to errors. The Internet of Things is the next step in the evolution of our today’s Internet, where any physical object equipped with computing and communication capabilities can be easily integrated into various areas of our life. Thus, there is a need for an intelligent grid that can effectively solve various aspects of transmission and distribution, reducing the load on the power system, making it reliable and secure. One option to meet peak demand is to reinforce the distribution network with thicker cables and more powerful power systems. Another, more sustainable solution is the introduction of smart grid technology to balance energy demand and supply by increasing the flexibility of the electricity grid through the use of information and communication technologies and remote control in real time, for example, with the Internet of things, artificial intelligence, cloud computing and others. However, security and reliability is seen as one of the main obstacles to the rapid and largescale implementation and deployment of both the IoT concept and smart grid. It is expected that the smart grid and the Internet of Things will revolutionize various spheres of everyday life, industry and business.

Information Security Terminal Architecture of Power Transportation Mobile Internet of Things Based on Big Data Analysis

The progress of the social economy and the rapid development of the power field have created more favorable conditions for the construction of my country’s power grid. In this network age, how to further realize the connection between the power system and the Internet of Things is the key content of many scholars’ research. In the Internet of Things environment, there have been many excellent results in the collection, storage, and management of electric power big data, but the problem of information security has not been completely solved. Based on big data analysis and Internet of Things technology, this paper studies the architecture design of power information security terminals. In view of the diverse types of power grid mobile information and the large amount of data, this paper designs a power transportation mobile information security management system structure, which improves the effective management of power data by the system through big data, smart sensors, and wireless communication technology. According to the experiment, the power information security terminal constructed in this paper can effectively reduce communication resources and save communication costs in the process of aggregating multidimensional data. In the user satisfaction survey, residents’ satisfaction with the convenience and safety of the intelligent power system is also as high as 9.312 and 9.233. On the whole, the application of big data and Internet of Things technology to the construction of power information security terminals can indeed improve the service efficiency of power companies under the premise of ensuring safety and allow users to have a better experience.

Current status of hybrid structures in wind forecasting

Wind power is one of the most important clean energy and alternative to fossil fuels. More attention has been paid to this renewable resource in today’s world due to increasing public awareness, concerns about greenhouse gas emissions and environmental issues, and reducing the oil and gas reservoirs. Accurate and precise wind speed and wind power forecasts are the most critical and influential factors in making desired and efficient managerial and operational decisions in the wind energy area. Wind power and speed forecasting play an essential role in the planning, controlling, and monitoring of intelligent wind power systems. Therefore, several different models have been developed in the subject literature in order to predict this energy source more accurately. However, there is no general consensus on the model that must be selected and used in a specific situation of time horizon, sample size, complexity, uncertainty, etc. Hybrid models are the most frequently used and the most popular forecasting models in the energy literature. In this paper, combined approaches used in the wind energy forecasting field are first categorized into four main categories: 1) Data preprocessing based approaches, 2) Parameter optimization-based approaches, 3) Post processing based approaches, and 4) component combination-based approaches. Results indicate that the component combination-based category is the most diverse and extensive hybrid approach in the literature. Thus, in the next section of the paper, more attention is paid to these approaches and then classified into two major classes of series and parallel hybrid models. The literature review demonstrates that parallel hybrid models are more popular approaches in comparison with series hybrid models and more used for wind forecasting. Other specific and detailed conclusions and remarks are introduced in related sections.

Contingency Analysis to Evaluate the Robustness in Large-Scale Smart Grids: Based on Information Security Objectives and Frequency Stability

Smart grids (SGs) are attracting attention as high value-added platforms, which mass-produce new business models through real-time information sharing. However, the open-ended information structure of SGs increases the risk of exposure to cyberattacks through the creation of multiple communication access points. In a power system where a real-time balance of supply and demand is essential, cyberattacks result in cascading failures leading to power outages. Therefore, this paper proposes a method to evaluate the robustness of large-scale SGs against cybersecurity disturbances. The proposed evaluation method established a hierarchy quantification technique considering the structural characteristics of SGs. With respect to the cyber hierarchy, relevant standards (NERC CIP, NIST FIPS) were applied to classify the grades of information security risk. In the case of physical hierarchy, the power system was calculated by using optimal power flow and analyzed the frequency stability. This study was aimed at identifying the vulnerabilities in the physical topology aspect of intelligent power systems due to cybersecurity disturbances.

Dynamic model based energy consumption optimal motion planning for high-voltage transmission line mobile robot manipulator

Power transmission line live working robots are important equipment and useful exploration to ensure the reliable operation of high-voltage lines and they are the development trend to realizing intelligent automation power system operation and maintenance management. At present, most robots adapt lithium battery power supply and can be hoisted on line. The robot online endurance operation time is closely related to the system energy consumption. When the robot electricity of battery is insufficient, it needs to be charged offline. In order to reduce the frequency of robot hoisted on and off line as much as possible and improve the robot battery life time after it has been online, this has become a key technology which needs to be solved urgently. It is of great theoretical significance and practical application value to promote the robot overall operation efficiency. Based on the above, this paper establishes double different types manipulator energy consumption models for high-voltage transmission line damper replacement operation and drainage plate maintenance operation, through analysis and synthesis, a general energy consumption model for different tasks have been abstracted and an objective function for optimizing the robot manipulator motion energy consumption have been constructed based on the robot dynamics, thereby, GA(genetic algorithm) has been adapted, through selecting appropriate algorithm parameters, the optimal manipulator energy consumption has been solved and then it can be substituted back to the manipulator energy consumption model so as to obtain the optimal joint energy consumption motion function, based on the optimal energy consumption results, the optimal robot energy consumption motion planning has been carried out, according to the MATLAB simulation results, the energy consumption of the optimized trajectory is significantly lower than before, which can effectively reduce the frequency the robot hoisted online and offline, so as to improve the robot operation overall efficiency, at the same time, the optimal energy consumption trajectory planning method has strong versatility for different operation tasks. Finally, based on the optimal energy consumption trajectory planning, the robot drainage board tightening and damper replacement operation experiments on 220 kV power lines which verified the effectiveness and engineering practicability of the proposed method.

A hybrid prediction model based on improved multivariable grey model for long-term electricity consumption

The accurate and stable prediction of electricity consumption is essential for intelligent power systems in rapidly developing countries. Grey prediction model is one of choices for prediction under the condition of limited historical data. Nonetheless, it seems rather sceptical using single-variable grey prediction model to predict the dynamics of a complex system. This paper presents a novel multivariable grey prediction model based on first-order linear difference equation for long-term electricity consumption prediction. The proposed model solves the problem of parameter estimation and variable prediction deriving from different approaches through rewriting the whitenization equation of multivariable grey model (MGM(1, m)). To validate the effectiveness of the proposed hybrid model, the electricity consumption is estimated and predicted over the data from Shanxi province and Beijing city in China from 1999 to 2018. The results show that the hybrid model provides a better estimation and prediction performance compared with other prediction model for predicting electricity consumption.

Research on Transient Overvoltage Suppression Measures for Photovoltaics Connected to the DC Sending End in the Smart Power System for Industry 4.0

The development of smart power systems in Industry 4.0 will bring people better quality electrical energy. The photovoltaic power generation system in the smart grid for Industry 4.0 is also the focus of development. The rapid development of photovoltaic power generation systems will bring people cleaner and greener energy. However, it will cause the transient overvoltage phenomenon near the DC sending terminal when the large-scale photovoltaic plant is connected to the power grid, further bringing about a profound impact on the power grid and its security and stability. In this paper, it is proposed that the rise of transient overvoltage at the photovoltaic terminal is caused by three main factors including the reactive power surplus of DC commutation failure, the weak power grid at the sending terminal, and the control characteristics of the photovoltaic plant. Then, the coordinated control strategy of transient overvoltage is proposed, and the overvoltage control strategy of the DC transmission terminal and the photovoltaic power plant control strategy are, respectively, optimized. Finally, the influence parameters of overvoltage are analysed and the corresponding photovoltaic strategy is presented. The optimization results of photovoltaic parameters and the proposed strategies of photovoltaic power generation can effectively restrain the voltage or frequency fluctuation and power oscillation caused by HVDC commutation failure. This study promotes the development of the photovoltaic power generation system in the intelligent power system of industry 4.0.

A microgrid alarm processing method based on equipment fault prediction and improved support vector machine learning

Microgrids are important parts of modern strong intelligent power system. In recent years, new energy sources have developed rapidly in China, and research on microgrid technologies is in the ascendant. The typical microgrid has obvious advantages such as flexible network structure and small circuit loss. With its increasing scale and increasing application in the power system, it needs to be dealt with the complex operation mode. In order to achieve efficient operation and management, microgrid control still needs to be equipped with energy management system (EMS). One of the main problems that EMS needs to solve is the generation of massive alarm information within a short period of time after equipment failure. To this end, this paper proposes a microgrid alarm processing method based on equipment fault prediction and improved support vector machine learning. Based on historical operation risk and health state evaluation, the equipment fault prediction is carried out. Then we optimize and select kernel function for machine learning and have an improved SVM model. With this model, fault equipment sets are classified and verified so we can get the accurate source alarm and fault equipment. Finally, the validity and accuracy of this method are verified by a simple example.

Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems.

Research on Fake Data Injection Attack Detection in Intelligent Ship Power System

The application of computers in marine power systems is becoming more and more extensive, and the network security of marine power systems has become an important part. This paper studies the false data injection attack (FDIA) of the ship’s power information physical system. First, it analyzes the objectives and principles of the FDIA attack from the perspective of the attacker and the possible consequences on the power grid. From the technical point of view, it studies two aspects of FDIA attacks, analyzes and summarizes various methods in detection and defense. The FDIA defense model based on deep learning is proposed, and on this basis, the measurement points that need to be protected are confirmed according to the importance index Ik. And its index is applied to the established fake data injection attack detection model based on deep learning, and the accuracy of the two traditional fake data injection attacks is compared. Experiments show that the accuracy of this model is increased to more than 90% compared with traditional algorithms, which can effectively enhance the defense capability of the ship’s power system against FDIA.

Application of Power System in Electrical Engineering and Automation

Nowadays, with the continuous development of China’s economy, it promotes the continuous progress of electric power enterprises. However, in electric power enterprises, electrical engineering and automation play a very important role. Through the rational application of this technology, it can promote the rapid and stable progress of China’s electric power enterprises. Therefore, in this paper, This paper mainly analyzes the development of power system automation under the electrical engineering and automation technology, and then puts forward the following contents, hoping to provide the corresponding reference value to the staff of the same industry. In this paper, based on the investigation and analysis of the current situation of intelligent power system based on electrical engineering technology, the introduction and application of electrical engineering technology is completed based on the demand of intelligent power system. On this basis, the intelligent power system based on electrical engineering technology is successfully established. Based on a large group of perceptible terminal equipment, the system can complete all kinds of data collection, and send to the upper application by using different network communication modes for intelligent management and analysis. The experimental results show that it can promote the level of intelligence in the power system by about 20%.

Parallel Control Method of Microgrid Inverter Based on Adaptive Droop Control

Energy is the driving force of social and economic development. With the gradual depletion of conventional energy and the increasingly prominent ecological and environmental problems, the power industry needs to find a sustainable development path. Parallel control of microgrid inverters based on adaptive droop control is an important part of future intelligent and sustainable power systems. However, with the continuous increase of power generation equipment capacity and the continuous increase of load power consumption, the form of power supply by a single inverter can no longer meet the requirements. Therefore, parallel connection of multiple inverters has been widely used in the microgrid. In this paper, a set of parallel control methods for microgrid inverters based on adaptive droop control is established. This paper is based on the inductance of the inverter’s terminal impedance, and the amplitude of the inverter terminal voltage is positively correlated with the output reactive power and has a strong correlation It is concluded that the microgrid based on adaptive droop control has the ability to automatically adjust the voltage and frequency of the microgrid. Properly reducing the droop coefficient can improve the primary frequency modulation capability of the microgrid. In this paper, an improved resistive control equation is used in a parallel system, a multi-loop control structure with a quasi-resonant controller is used for the inverter in the parallel system, and a virtual complex impedance is added to perform small signal analysis on the improved resistive control equation The overall performance of the parallel system is analyzed, and the effect of different control coefficients on the system performance is clarified. The experimental research results show that when analyzing the effect of the inductance coefficient value on the equivalent output impedance in the virtual complex impedance, the value of KL is 1 At this time, the equivalent output impedance becomes resistive again, and the property is stronger than when KL is 0.05.