State Of Power Equipment Research Articles

A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

Monitoring method for power equipment operation condition based on data warehouse

in the process of monitoring the operation state of power equipment, it is easy to be affected by the external environment, resulting in a large amount of data of the operation state of power equipment and the monitoring effect. A monitoring method of power equipment operation condition based on data warehouse is proposed. By analyzing the constraints of power equipment operation condition monitoring, a data warehouse is established; The transition probability and stability probability are determined by Markov chain model, and the characteristic values of operation state level variables are obtained by calculating the comprehensive correlation value and sensitivity; The entropy weight coefficient method calculates the weight of each performance index of the monitoring object, constructs the operation state monitoring model of power equipment, and realizes the monitoring of the operation state of power equipment. The experimental results show that the improved method can effectively improve the monitoring efficiency and shorten the monitoring time.

Sustainable Energy Safety Management Utilizing an Industry-Relative Assessment of Enterprise Equipment Technical Condition

The study considers approaches to ensuring energy management for the safe operation of facilities and their equipment and ways to improve it. It has been established that to ensure effective safety management of industrial enterprises, one of the critical areas is the technical diagnostics of power equipment during operation. An assessment of the actual technical condition of power equipment of VVER-1000 power units is proposed based on establishing the aging mechanisms and determining the relative evaluation coefficients for the characteristics of individual equipment elements. The results of the calculations allowed us to conclude that the obtained results correspond to the coefficients of relative assessment Ki of the technical characteristics of the power equipment that determine its degradation. Studies indicates that when assessing the state of power equipment, it is necessary to consider the presence and impact of the following operational factors that are not considered in the design calculations: loads, high levels of mechanical stress, fatigue damage, and metal defects, which primarily indicate the presence of degradation changes. To assess the technical condition of the equipment, considering the degree of mechanical wear, 17 technical characteristics were selected to determine the aging mechanisms by signs of degradation. A mathematical model of the dependence of the relative evaluation coefficient K on changes in the operating parameters is presented, and it is noted that the most significant influence on the value of the coefficient is the temperature of the coolant at the inlet (K = 0.56). The developed approach makes it possible to improve the safety management system of power facilities by introducing the proposed model to assess the technical conditions of power equipment by defining the parameters in the overall safety management system.

Assessment of the Power Equipment State by Measuring Dielectric Absorption Currents

The need to operate electrical devices the design capabilities of which have been exhausted almost completely under the conditions of more stringent reliability requirements gives rise to increased attention to the development and research of methods for diagnosing the power equipment state. This problem acquires not only a scientific interest, but also practical significance, which follows, in particular, from the mass-scale use of power transformers for operating voltages above 35 kV. One of promising development lines is the method of constructing dielectric absorption spectra, which is based on using the capabilities of isothermal depolarization current measurements. In this case, the information that accumulates as a result of the insulation system’s dielectric medium polarization as a certain number of loosely coupled charges displaced from their locations is adequate (equivalent) to the information obtained as a result of depolarization when measuring the return voltage (RV) value. Owing to the above-mentioned adequacy, it is possible to use the quantity tI(t) as a parameter for monitoring the state of the power equipment insulation system. The spectrum construction algorithm presented in the article is similar to the algorithm for constructing the RV spectrum, but is produced in the course of computer-aided processing of measurement results using the time windows that specify the insulation gap polarization time interval. As a result, it becomes possible not only to produce the dielectric absorption spectrum, but also to calculate the values of the main parameters characterizing the gap state.

Multi-parameter infrared sensing method for operation state of power equipment

Aiming at the problems of difficult extraction and reconstruction of multi-parameter signals and poor perception effect of power equipment operation status, a multi-parameter infrared perception method of power equipment operation status is proposed. The multi-parameter (sound, light, electricity, vibration, heat, and environment) sensors of power equipment operation status are selected, the multi-parameter sensor integration framework is built based on the Internet of things technology, the multi-parameter sensors are effectively integrated, the power equipment operation status parameter signals are collected and reconstructed, the power equipment operation status perception model is built, the unsupervised classification and AR algorithm are introduced, and the reconstructed signals are fitted, to realize the infrared perception of power equipment operation status. The experimental data show that the integrity of power equipment operation state parameters and the accuracy of power equipment operation state perception obtained by the proposed method are higher, which proves that the proposed method has a good effect on power equipment operation state perception.

Calculation and Analysis of Technical and Economic Indicators of the NPP Power Unit

Technical and economic indicators (TEI) characterize the efficiency, reliability, and durability of power equipment during its operation. Based on automating the determination of TEI directly during the production of electricity, objective information can be obtained on the efficiency of the flow of technological processes, as well as on the actual state of power equipment. The conditions for optimal operation of power plants are determined in the process of analyzing its technical and economic indicators. Therefore, the analysis of indicators is one of the most important tasks of automated process control systems (APCS) of nuclear power plants, which largely determines the efficiency of the power unit.

Remote Data Acquisition and Management Technology of Power Equipment Based on Internet of Things

This paper proposes an architecture scheme for realizing the power equipment data acquisition IoT monitoring network using the latest Internet of Things technology. At the same time, a power equipment data acquisition and monitoring system based on Narrowband Internet of Things (NB-IoT) and exclusive-OR (XOR) encryption algorithms is designed. The system is mainly researched and designed from the aspects of monitoring terminal software and hardware design, data encryption processing, and remote data management cloud platform. This paper installs a big data analysis processor and a network routing processor based on the Internet of Things technology and modifies the system memory, display, and connection circuit to optimize the system hardware equipment. Finally, the system conducts experimental tests to monitor the state of power equipment, such as transmission and distribution towers, power manhole covers, cable tunnels, and distribution line knife gate temperature. The experimental results show that the system can realize low power consumption, comprehensive coverage, low-cost unified access, and centralized management of power equipment status information in different power scenarios. The application of this system dramatically improves the reliability and efficiency of the operation and maintenance of terminal equipment of the power system. The promotion of this system has a great application value.

Informational Task “Calculation of Technical and Economic Indicators” NPP I&C ULCS and Risk Potential Assessment

Calculation of technical and economic indicators (TEI) is part of the information tasks of the upper-level control system of the NPP instrumentation and control system (I&C system). The TEI characterize the efficiency, reliability, and durability of the power unit equipment during its operation. Based on the automation of the TEI determination, objective information can be obtained about the efficiency of technological processes, the actual state of power equipment, as well as the risk potential of the ongoing processes.

Methodology for assessing the resource of power equipment of metallurgical workshops

Prognostic is central to manufacturing and infrastructure management. The development of new methods for determining the residual resource of equipment is an important task aimed at increasing the efficiency of using industrial electrical complexes. Solving the problem of reliably determining the state of power equipment in the mining and metallurgical complex makes it possible to move from an outdated system of scheduled preventive maintenance to maintenance based on the current state of the unit. Complex technical systems are characterized by complex nonlinear interactions between their constituent elements, complex scenarios of cause-and-effect relationships between hazardous, probabilistic events and processes occurring during their operation. Therefore, methods and tools are being developed to assess and manage wear mechanisms in high-risk industries. The article presents the results related to the development of a methodology for assessing the residual life of industrial equipment. An algorithm for assessing the residual life of power equipment is proposed, based on the comparison of the results of calculation of predicted values of the criteria of the residual life of industrial equipment with the normative values. The methodology uses probabilistic mathematical methods to predict the remaining service life and information collected during audits and equipment monitoring. Along with the classical methods, methods are presented based on the use of the entire potential of the modern element base of microprocessor technology and technologies for the use of artificial neural networks, machine learning, and «big data». Based on the application of probabilistic analysis, a methodology for monitoring the predicted residual life of power equipment has been proposed, for which an algorithm for solving diagnostic problems using neural networks has been developed.

Methodology for assessing the resource of power equipment of metallurgical workshops.

Prognostic is central to manufacturing and infrastructure management. The development of new methods for determining the residual resource of equipment is an important task aimed at increasing the efficiency of using industrial electrical complexes. Solving the problem of reliably determining the state of power equipment in the mining and metallurgical complex makes it possible to move from an outdated system of scheduled preventive maintenance to maintenance based on the current state of the unit. Complex technical systems are characterized by complex nonlinear interactions between their constituent elements, complex scenarios of cause-and-effect relationships between hazardous, probabilistic events and processes occurring during their operation. Therefore, methods and tools are being developed to assess and manage wear mechanisms in high-risk industries. The sources of information in the systems of technical diagnostics and control of technological processes are measuring transducers (sensors) of physical quantities. Using information from sensors and real-time operational diagnostics, the operator can predict the occurrence of malfunctions, equipment failures and process disturbances, which reduces downtime of process equipment and the occurrence of emergency situations. The article presents the results related to the development of a methodology for assessing the residual life of industrial equipment. The methodology uses probabilistic mathematical methods to predict the remaining service life and information collected during audits and equipment monitoring. Along with the classical methods, methods are presented based on the use of the entire potential of the modern element base of microprocessor technology and technologies for the use of artificial neural networks, machine learning, "big data".

Intelligent detection method of abnormal state of power equipment based on infrared and visible image information

Infrared thermal imaging cameras are usually used to detect the thermal state of the equipment. According to the heat radiation situation and temperature value emitted by the equipment, the thermal failure of the equipment is inferred, and whether the equipment is working is verified properly. This paper analyzes the thermal imaging principle of infrared technology and the characteristics of infrared images, and quantifies the surface temperature indicators of insulators. Through infrared and visible light image information, the pollution degree of insulators can be detected, and the abnormal condition of the equipment can be checked. The evaporation of moisture on the surface of the insulator mainly depends on the assumption that the surface heats up. A method for analyzing heat on the surface of wet-contaminated insulators is proposed, and the judgment conditions for arc generation in the drying zone and the drying zone and the heating models of the insulators in different operating conditions are established. The computer simulation results reveal the distribution of heat on the surface of insulators under different operating conditions, and the influence of the occurrence of drying zone and drying zone arc on leakage current and heating. The infrared thermal imaging test results of wet-contaminated insulators show that the model is reasonable and provides theoretical support for infrared thermal imaging detection of insulator contamination levels.

Composition Form and Function Configuration of Intelligent Terminal Based on Edge Computing for Power Internet of Things

The intelligent terminal is a key device in the Power Internet of Things (PIOT), which plays a core role in the process of accurately sensing the state of power equipment and adjusting the user’s electricity behaviour in real time. As a decentralized computing architecture, edge computing plays an important role in improving the data processing capacity and operation reliability of PIOT. This paper describes the operation process of PIOT based on edge computing, by studying the form of intelligent terminals in the field of power transmission, distribution and consumption, putting forward the basic configuration, specific function and principles of intelligent terminal. Combined with the operation characteristics and actual demand of power system, the application analysis of information perception ability and online control ability of intelligent terminal is carried out.

Development of a consensus algorithm and data block structure based on digital blockchain technology for distributed systems for storing information about the state of power equipment

The article considers the main directions of technological transformations in the Russian electric power industry, which performs an integrating function for the fuel and energy complex of the country. The methods of achieving and ensuring a high level of reliability and safety of power equipment for thermal power plants, hydroelectric power plants, and nuclear power plants at all stages of their life cycle from design to decommissioning are considered. The issues of creating a unified system of information support for power equipment of generating systems for thermal power plants, hydroelectric power plants, nuclear power plants and power complexes based on renewable energy sources, taking into account the technical condition during the entire life cycle, are studied. In order to improve the security and efficiency of planning and forecasting of power equipment operation modes, it is proposed to consider an information model consisting of a centralized data warehouse and a distributed registry based on blockchain technologies. The principle of operation of a distributed closed registry, the structure of data blocks, and the algorithm of consensus between private users of the registry are considered.

FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode

The current distribution of the grounding electrode in a high-voltage direct current (HVDC) transmission system affects the state of power equipment in its vicinity, which depends on the soil resistivity and shape of the grounding electrode. In this paper, current distribution in the vicinity of an ±800 kV grounding electrode is investigated by simulation and experiments. Firstly, the model to calculate the current distribution with two typical frozen soils is set up, and simulation models and experimental platforms are established; meanwhile, the finite element method (FEM) is used to calculate the current and potential dispersion of linear, cross-shaped, and ring-shaped grounding electrodes in the simulation models. After obtaining the lab current data from the simulation, an innovative method based on a “drainage wire” with a Hall sensor is proposed to measure the current in an experimental setup. The results show that current and potential distribution characteristics are related to the shape of the grounding electrode and soil resistivity. Meanwhile, the current measurement scheme can measure the current in soil with a lower error. This article concludes that these two typical models can reduce the complexity of frozen soil analysis, and the measurement scheme can accurately monitor the current to reduce the damage to the surrounding power equipment.

Машинное обучение как инструмент повышения эффективности управления жизненным циклом высоковольтного электрооборудования

The purpose of the study is to analyze the practical implementation of high-voltage electrical equipment technical state estimation subsystems as a part of solving the lifecycle management problem based on machine learning methods and taking into account the effect of the adjacent power system operation modes. To deal with the problem of power equipment technical state analysis, i.e. power equipment state pattern recognition, XGBoost based on gradient boosting decision tree algorithm is used. Its main advantages are the ability to process gapped data and efficient operation with tabular data for solving classification and regression problems. The author suggests the formation procedure of correct and sufficient initial database for high-voltage equipment state pattern recognition based on its technical diagnostic data and the algorithm for training and testing sets creation in order to improve the identification accuracy of power equipment actual state. The description and justification of the machine learning method and corresponding error metrics are also provided. Based on the actual states of power transformers and circuit breakers the sets of technical diagnostic parameters that have the greatest impact on the accuracy of state identification are formed. The effectiveness of using power systems operation parameters as additional features is also confirmed. It is determined that the consideration of operation parameters obtained by calculation as a part of the training set for high-voltage equipment technical state identification makes it possible to improve the tuning accuracy. The developed structure and approaches to power equipment technical state analysis supplemented by power system operation mode data and diagnostic results provide an information link between the tasks of technological and dispatch control. This allows us to consider the task of power system operation mode planning from the standpoint of power equipment technical state and identify the priorities in repair and maintenance to eliminate power network “bottlenecks”.

INFRARED THERMOGRAPHIC INSPECTION OF ELECTRICAL JUNCTIONS IN SOME ELECTRICITY DISTRIBUTION FACILITIES IN AHMADU BELLO UNIVERSITY

Electrical or insulation failure is usually accompanied by two major events among others; production of acoustic pulses (sound) and an increase in temperature owing to heating. These lead to costly energy dissipation. Distribution boards are elementary necessities in any electrical wiring but overheating at the junctions can result in serious energy loss and eventual fire outbreak. There are several methods and techniques used in monitoring and assessing the state of power equipment and components in the electricity network. One of such techniques is Infrared thermography (IRT). An average temperature of 92.4°C and 33.7°C were recorded on the three-phase distribution board for the building during the peak and off-peak periods respectively. The inspection of the distribution boards in the Physics Department building of Ahmadu Bello University, Zaria, using thermal imaging camera, revealed excessive heating at the three-phase distribution board during peak hours, leading to Ohmic loss. The dissipated energy during the peak period ranges from 665 W/m2 to 1.5 kW/m2. The heating resulted in an average energy loss of about 1 kW/m2. The energy loss at the junctions may have, among other factors, contributed to the high electricity billing at the university.

A high-precision synchronous signal acquisition device.

The synchronous and accurate acquisition of multiple features of states is key to assessing the state of power equipment. The highest recorded accuracy of currently available synchronous signal measuring devices in intelligent substations is the 0.2 class, which cannot satisfy the requirements of power equipment such as voltage and current transformers. The accuracy of measurement of a high-precision sigma-delta analog-to-digital converter (ADC) can reach the 0.05 class, but the phase error among several sigma-delta ADCs is large and dynamic because of inadequate real-time sampling. To solve this problem, this paper proposes a double-trigger-time digital conversion technology for synchronous and accurate measurement among sigma-delta ADCs. The results of tests show that its measurement accuracy can reach the 0.05 class.

Principles of Constructing Systems for Monitoring the Vibrational State of Power Equipment Using IEEE 1451.X Smart Sensors

The principles for developing smart sensors for monitoring the vibrational state and for vibration diagnostics of power equipment in thermal power plants are analyzed.

COMPUTER-INTEGRATED COMPONENTS OF THE AUTOMATED DECISION-MAKING SUPPORT SYSTEM FOR OPERATIONAL AND MAINTENANCE PERSONNEL OF NUCLEAR POWER PLANT UNITS WITH WWER

The purpose of this article is to describe the results of the research aimed at developing computer-integrated components of one of the ADMSS variants for operational and maintenance personnel of NPP units according to the criterion of technical and economic efficiency, taking into account the diagnostics of the technical equipment state based on the simulation model describing by means of up-to-date mathematical methods the technological processes in the main and auxiliary equipment of power units using up-to-date mathematical methods at the level of detailing, corresponding to their principle and deployed thermal schemes. The results of studies aimed at the development of computer-integrated components of the automated decision-making support system (ADMSS) for operational and maintenance personnel of NPP units by the criterion of technical and economic efficiency, taking into account the diagnostics of the state of the power unit equipment, are presented. The general structure of the software package interaction for the analysis of the performance and parameter diagnostics of NPP units with WWER has been developed. When creating the software package, the integrated programming environment Microsoft Visual Studios was used. The structure of the program block for the parameter diagnostics of the equipment of nuclear power units is presented. The main types of problems arising during the operation of NPP units with WWER, that can be solved with the help of the developed ADMSS are considered, and a form for presenting the results to the operational and maintenance personnel of power units is proposed. Developed on the basis of the described computer-integrated components, the automated decision-making support system for the operational and maintenance personnel of NPP power units can be used to solve a wide range of problems arising in the practice of short-, medium- and long-term control of the operation modes of power unit systems and equipment, including obtaining operational (energy) characteristics of power unit systems and equipment, optimizing operation modes and parameters, diagnosing and forecasting technical state of power equipment, predicting the amount of electrical and thermal energy generated by a power unit, as well as optimizing NPP repair cycles.

A Computational—Experimental Method for Determination of the Stress—Strain State of Thermally-Loaded Power Equipment according to Full-Scale Strain-Gauging Data

A method for construction of a monitoring system of the stress—strain state of power equipment based on the functional relations and regression dependences of the stress state on measured parameters is considered. An algorithm for construction of a monitoring system of the stress—strain stress for the elements of equipment subject to the nonstationary actions of a heat carrier with a variable temperature is presented. The results of use of this algorithm to study the stress—strain state of the connecting pipes of shell elements of power plants under operational conditions are given.