Operation Of Power Equipment Research Articles

Partial Discharge Diagnosis With Siamese Fusion Network

Partial discharge is a common fault type in the operation of power equipment. Recently, deep learning methods have shown great potential in partial discharge (PD) diagnosis. These methods construct a fitting relationship between input and output with mass training samples. Due to the scarcity of PD samples, it is hard to train a classification model. Thus, it is challenging to apply traditional deep learning methods for diagnosing PD. To overcome this issue, this paper introduces a siamese fusion network to diagnose PD. This method comprises three main steps. First, an ultra-high-frequency (UHF) sensor generates two spectrums from power equipment, including phase resolved partial discharge (PRPD) and phase resolved pulse sequence (PRPS). Based on a few-shot learning strategy, a support set is constructed. There are four different types of PD samples and a normal sample. Then, two siamese networks are employed to estimate similarity scores between a test sample and support set samples. One network measures similarity scores in PRPS, and the other measures similarity scores in PRPD. Based on similarity scores, initial diagnosis results are generated. Last, a simple and effective decision fusion technique fuses initial diagnosis results. The final diagnosis result can be generated by jointly exploiting complementary information in two spectrums. With limited training samples, experimental results show that the proposed SFN method can achieve an outstanding diagnosis performance, compared with several classical classification methods.

The influence of some geometric parameters on heat transfer in axial cooling systems of electronic equipment made on the basis of split fins

The active development of advanced digital technologies over the past 10 years has led to the formation of a new set of tasks related to ensuring the uninterrupted and predictable operation of electronic and electric power equipment. Thus, modern telecommunication and computer systems during their normal operation are characterized by significant heat release. One of the main problems of such systems, along with the observance of weight and size characteristics, is the provision of certain operating thermal conditions of the components in a wide range of external conditions. In such situations, passive and active systems with axial fins are very often used. As a result of the work, experimental studies of heat transfer intensification under conditions of forced convection using split ribs were carried out and presented. The effectiveness of the use of split fins has been shown, during the experiment the most optimal geometric parameters of the working section and the corresponding operating characteristics have been identified. Based on the processing and analysis of experimental data, it was found that this method of intensification makes it possible to increase the intensity of heat transfer up to 64%, since the cutting of the ribs makes it possible to actively prevent the formation of a boundary layer and additionally turbulize the flow, which in turn favorably affects the thermal parameters of the entire system. The visualization of the heat transfer process was performed, which illustrates the active vortex flows formed during the flow around and separation from the edges of the rib of the oncoming coolant flow. The calculation of the thickness of the boundary layer near the split rib was made, in which it was found that for all investigated angles of rib bending γ, an increase in the Re number is characterized by a decrease in the thickness of the boundary layer in the entire range of thermal loads. In conclusion, a general assessment of the influence of geometric and regime parameters on the intensity of heat transfer with forced air supply was carried out.

An Improved Biometric Fuzzy Signature with Timestamp of Blockchain Technology for Electrical Equipment Maintenance

The power infrastructure of the power system is massive in size and dispersed throughout the system. Therefore, how to protect the information security in the operation and maintenance of power equipment is a difficult problem. This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance. Compared with previous blockchain transactions, the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance. Finally, the effectiveness of the proposed method is verified by experiments.

Design and Implementation of Ultra-Wide Band Antenna for Partial Discharge Detection in High Voltage Power Equipment

Partial discharges (PD) are the most common and harmful threat to the health of electrical insulation of high voltage (HV) and high field (HF) equipment. The occurrence of PD activity in HV and MV equipment is at the same time a cause and a sign of insulation degradation that may eventually result in the breakdown of the HV power equipment. For the safe and reliable operation of HV power equipment, continuous PD monitoring needs to be conducted conveniently to prevent any unplanned power outages and damage to electrical power equipment. The ultra-high frequency (UHF) PD measurement method has been widely used as an effective technique to detect PD activity on HV power equipment due to its non-invasive principle. To enable PD detection accuracy, there is still a need for more sensitive PD sensors that can assist the UHF PD monitoring system by suppressing ambient background noise and low-frequency electromagnetic interferences from telecommunication such as GSM signals. To tackle the sensitivity issues, this article presents a new design of ultra-wideband (UWB) microstrip patch antenna that is capable of effective suppression of low-frequency interference signals. The proposed antenna, designed, simulated, and optimized using the CST Microwave Studio software, has a bandwidth of 3.3GHz, below -10dB, in the operating frequency range of 1.2GHz-4.5GHz. The prototype of this antenna, printed on an FR4 substrate of a thickness of 1.6mm and a dielectric constant of 4.4, featuring a compact size of 100mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times100$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times1.6$ </tex-math></inline-formula> mm, was implemented to detect PD through laboratory experiments. This paper shows that the designed UWB antenna has high sensitivity, good noise rejection and it is, therefore, a promising candidate sensor for PD detection on HV equipment.

Problems of acoustic safety in power engineering

Environmental safety issues are becoming increasingly important in the life of society. Among environmental safety issues in power engineering, acoustic safety occupies a special place. The problem of acoustic safety is associated with the fact that the regular operation of power equipment leads to an increased noise level, and power facilities are located in close proximity to residential areas. In this work, acoustic calculations were performed to determine the sanitary protection zone for gas turbines units (GTU) and combined cycle gas turbine units (CCGT) of various capacities. A formula was obtained for calculating the width of the sanitary protection zone depending on the capacity of gas turbine units and combined cycle plants and their number. It is shown that the sanitary protection zone (SPZ) of a power unit of high capacity is smaller than the sanitary protection zone of several power units of the same capacity. It is found that the noise levels from individual groups of equipment can determine the noise level at the entire border of the sanitary protection zone or in its individual sections. At the same time, noise suppression measures should be taken for all sources that generate noise levels in excess of standards. It is necessary to start noise suppression measures from those sources that generate excess noise in a larger section of the sanitary protection zone.

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The safe operation of power equipment largely depends on the overvoltage protection level of the arrester. The ZnO varistors are widely used as the core components of the arresters in power systems because of the excellent nonlinear volt-ampere characteristics. In order to study the electrical properties of ZnO varistors under different external electric fields from the microstructure, the method of first-principles based on density functional theory (DFT) is used, and structure of ZnO/&lt;i&gt;β&lt;/i&gt;-Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; interface containing zinc interstitial (Zn&lt;sub&gt;i&lt;/sub&gt;) and oxygen vacancy (V&lt;sub&gt;o&lt;/sub&gt;) defects is built. The results show that the V&lt;sub&gt;o&lt;/sub&gt; defect migrates after full relaxation. The Zn&lt;sub&gt;i&lt;/sub&gt; shifts to the interface under an external electric field. The interface energy increases rapidly after the electric field intensity has exceeded 0.1 V/Å, which means that the interaction force between the interfaces becomes larger, the distance between ZnO and &lt;i&gt;β&lt;/i&gt;-Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; layers decreases, and the conductivity increases rapidly. The differential charge density, work function and Bader charge analysis method are used to calculate the barrier height at the interface, which proves that the built-in electric field is an important cause ingredient responsible for the non-linear volt-ampere characteristics of ZnO varistors. The effects of atomic orbital energy level, trap energy level and energy gap on the macroscopic conductivity of ZnO varistors are analyzed by using the method of density of states analysis. In this work are analyzed the different electrical parameters of the ZnO/&lt;i&gt;β&lt;/i&gt;-Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; interface with aggregation defects by adjusting the intensity of the external electric field, and a new idea is provided for learning the electrical characteristics of ZnO varistors.

Electrical Fault Detection Equipment Based on Infrared Image Fusion

Maintaining the safe and stable operation of power equipment is the first task of power system operation and maintenance. The equipment in normal operation may have various faults, so it needs to be located, detected and repaired. In order to improve the on-line detection efficiency of electrical equipment, diagnose the operation state of equipment in time and locate faults, this project proposes a multi focus infrared and visible image fusion algorithm based on NSST and adaptive PCNN. Taking the power equipment as the research object, the infrared image of the power equipment is compared with the visible image, and the feature points are selected, so as to realize the image registration. After image registration, the registered visible and infrared images are fused and reconstructed, and the effective features of the two kinds of images are integrated into one image. The target image constructed by fusion not only has thermal energy information, but also has object texture, highlights the target, and can detect the fault location without power failure. It has certain practical significance for quickly removing the fault point of power equipment.

Основні вимоги до сучасних систем діагностики потужних турбогенераторів

A turbogenerators design complication and the use of new auxiliary systems for the cooling intensification with increasing power unit lead to the decrease of their reliability. Therefore, the system and methods of control and diagnostics of generators main component parts to compensate this decrease of turdogenerators reliability were developed. In the paper, a system analysis for the reliability of nuclear power stations turbogenerators was carried out, and the main damaging elements and components, that were determined. It was found that almost half of the damages are stator and rotor, a third of which — stator core and winding bars defects. Modern diagnostic methods of technical condition for stator and rotor elements of powerful generators as well as their advantages and drawback are considered. All this allowed us to formulate the basic principles of diagnostic system construction for nuclear power stations powerful turbogenerators, which are based on the continuous control of the data on their most important parameters and defects, and deviations identification occurring in the most of operation cases as well as preventing further work of equipment. The economic effect owing to the introduction of monitoring and diagnostic modern systems is ensured by increasing the reliability of electric power equipment operation, reduction downtime due to emergency shutdowns of generators, decrease unplanned electricity underproduction, reducing in time and cost of repair and restoration work, the number of consumable spare parts and materials, as well as economic losses due to unplanned downtime of nuclear power plant unit.

Predictive maintenance scheduling for multiple power equipment based on data-driven fault prediction

In view of the maintenance of multiple power equipment operating in abnormal conditions in large-scale power plants, based on the prediction of fault state, a predictive maintenance scheduling method is proposed to schedule the maintenance activities. Firstly, based on the actual operating data, combined with the influencing factors of fault state deterioration by Pareto analysis, a time-variant function is improved to predict the deterioration state of potential fault in future maintenance interval. Then, maintenance priority is divided based on the fault state, considering the constraints of maintenance resources and equipment downtime, with the objective of minimizing the total maintenance cost, a scheduling model is built for the maintenance of multiple equipment. Finally, aiming at the continuity of maintenance time, a two-stage algorithm is proposed, which divides the maintenance time-window to transform the complex continuous time optimization problem into a combinatorial optimization problem of time periods, and then develops the optimal maintenance scheme. Taking the maintenance of multiple power transformers as an example, combined with the data resources provided by Yunnan power grid of China, the effectiveness of the improved prediction function of fault state is proved. In addition, by comparing with the traditional maintenance strategy based on the principle of first-fault-first-repair, the superiority of the proposed maintenance scheduling method is verified in reducing cost and improving system stability.

Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

Key Technology and Application Prospect of Operation and Maintenance of Power Equipment in New Power System

Key Technology and Application Prospect of Operation and Maintenance of Power Equipment in New Power System

Analysis of Two-Stage Fuel Combustion in Multi-Fuel Boilers

The significant growth of industry in the second half of the 20th century led to a number of problems, one of the most important problems is the protection of the environment from pollution. The main source of air pollution is heat and power generating plants, which are based on combustion processes. The largest pollutant is thermal power plants, which burn a large volume of fossil fuel and form a corresponding amount of toxic substances, in particular nitrogen and sulfur oxides. In this paper, combustion processes with various burner devices will be considered and two fuels – natural gas, fuel oil. One of the most important tasks operation of power equipment is the development and widespread use of effective methods to reduce the formation of harmful substances and determining the optimal modes of fuel combustion, ensuring a minimum level of emissions of toxic combustion products. Two-stage fuel combustion is an effective method of reducing nitrogen oxide emissions. In two-stage combustion, one fuel burnout zone is replaced by several zones, as isolated as possible. The paper will compare several options for the implementation of two-stage combustion in the boiler. The influence of boiler load on the concentration of nitrogen oxides in the exhaust gases is analyzed. Also at two-stage combustion of fuel oil in gas-oil boilers reliability of screen surfaces of heating as a result of decrease in the maximum falling heat streams on a screen surface of heating of a copper increased.

Numerical simulation of surface charge inhibition performance on epoxy insulator under DC voltage by flexible coating strategy

Epoxy insulator is affected by surface charge accumulation under a strong electric field, which causes a decrease in the surface flashover voltage, threatening the safe operation of power equipment. This study reports on a flexible coating strategy for the insulator to inhibit the charge accumulation through numerical simulation. The experiment was conducted initially to reveal the charge accumulation feature. Then, the flexible coating method was proposed, in which the insulator surface was divided into three parts, and surface coatings with various conductivities were painted on the designated areas to adjust the charge distribution. Finally, the efficiency of such a coating strategy was estimated considering the temperature gradient. The results presented that the homo-charge was mostly deposited on the non-planar area of the spacer. The maximum charge density first decreased and then increased with increasing surface conductivity. The charge accumulation could be restrained by increasing the surface conductivity of insulator effectively. A significant suppression was achieved by properly coating with high conductivity at the non-planar region and with low conductivity at the planar region. The suppression performance of such a strategy became reduced as the temperature gradient increased. The results indicate that the flexible coating can balance charge deposition in the non-planar region and restrain charge injection near the electrode, thereby inhibiting the charge accumulation.

Detection of Abnormal Hot Spots Infrared Images of Power Equipment Based on YOLOv4

The stable operation of power equipment is very important to ensure the stability of power grid. In this paper, an object detection model is designed for detection of the abnormal hot spots of electrical equipment. In order to improve the performance of the object detection model, data augmentation of image is applied to extending the training set. The test results show that the model based on YOLOv4 can identify and locate the abnormal heating fault point in the infrared image with high accuracy. The precision and recall rate of the object detection model on test set can reach 88.51% and 91.66% respectively.

Experience of using the NALCO 1392 scale inhibitor in the circulating water supply system of the Novovoronezh NPP

Power facilities use large amounts of water to cool steam in the steam turbine condensers, and lubricating oils, gas and air in turbine sets. The key requirement for the quality of cooling water is to ensure normal vacuum in condensers. Cooling water must not form mineral and biological deposits and corrosion products in the system. Deposits of mineral salts in the condenser tube system, as well as in auxiliary cooling systems, lead to deterioration in heat exchange and a major decrease in the cost effectiveness of the power equipment operation, and require the heat-exchange equipment to be periodically cleaned. The source water used for cooling is normally taken from nearby water bodies (large rivers or lakes). Circulating water supply systems are used most commonly: these systems use repeatedly the same water inventory for cooling, and require only small amounts of water added to make up for evaporation losses. Coolers, in this case, are cooling towers, spray pools and evaporation ponds. The water chemistry should ensure the operation of equipment without any damage to its components or the loss of efficiency caused by the corrosion of the internal surfaces as well as without scale and sludge formation. It is exactly when using circulating water supply that a stabilizing treatment program is the most practicable way to ensure a cost-effective and environmentally friendly mode of operation. To inhibit scaling processes on the heat-exchange surfaces of the turbine condenser tubes at the Novovoronezh NPP’s unit 5, the cooling water was treated with the NALCO 1392 inhibitor. The results of the NALCO 1392 inhibitor pilot tests in the circulating water supply system (with a cooling pool) are presented.

Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil

Condition assessment of insulating oil is crucial for the reliable long-term operation of power equipment, especially power transformers. Under thermal aging, critical degradation in oil properties, including chemical, physical, and dielectric properties, occurs due to the generation of aging byproducts. Ultraviolet-visible (UV-Vis) spectroscopy was recently proposed for the condition assessment of mineral oil. However, this absorption technique may involve all electronic states of the investigated material which typically yield a broad spectrum, and thus cannot precisely reflect the electronic structure of aged oil samples. It also cannot be implemented as an online sensor of oil degradation. In this paper, photoluminescence (PL) spectroscopy is introduced, for the first time, for effective condition assessment of insulating oil. The PL technique involves emission processes that only occur between a narrow band of electronic states that are occupied by thermalized electrons and consequently yields a spectrum that is much narrower than that of the absorption spectrum. Aged oil samples with different aging extents were prepared in the laboratory using accelerated aging tests at 120 °C, under which 1 day of laboratory aging is equivalent to approximately 1 year of aging in the field. These aged samples were then tested using PL spectroscopy with a wavelength ranging from 150 nm to 1500 nm. Two main parameters were evaluated for quantitative analysis of PL spectra: The full width at half-maximum and the enclosed area under the PL spectra. These parameters were correlated to the aging extent. In conjunction with PL spectroscopy, the aged oil samples were tested for the dielectric dissipation factor as an indication of the number of aging byproducts. Interestingly, we find a correlation between the PL spectra and the dielectric dissipation factor. The results of PL spectroscopy were compared to those of UV-Vis spectroscopy for the same samples and the parameters extracted from PL spectra were compared to the aging b-products extracted from UV-Vis spectra. Finally, the corresponding physical mechanisms were discussed considering the obtained results and the spectral shift for each spectrum. It was proved that PL spectroscopy is a promising technique for the condition assessment of insulating oil when compared to conventional transformer oil assessment measuring techniques and even to other optical absorption techniques.

The Function and Application of 3D Visualization in Power Operation and Maintenance System

Traditional power system monitoring of substation and distribution related equipment often relies on sensor measurement parameters and relay action displayed on the system platform, and it is often difficult to intuitively obtain power system operating status information. In unmanned operation and maintenance, smart substations and other systems there are many shortcomings in the design process. Three-dimensional visualization technology provides a boost to the upgrade of the power operation and maintenance system, which can realize the identification of the physical image of the power equipment and the display of the three-dimensional model, and can combine the big data processing technology to realize the efficient operation and maintenance management of the power equipment. In the process of specific applications, electric power companies can correlate the actual operation of power equipment with the operation and maintenance system, and can also assist operation and maintenance managers in decision-making analysis by obtaining relevant data including three-dimensional time and space, but can also build a simulation platform Space to assist in relevant training.

A novel partial discharge localization method for GIL based on the 3D optical signal irradiance fingerprint and bagging‐KELM

Partial discharge (PD) is one of the main reasons that endanger the safe operation of power equipment. The effective detection and localization of PD in a gas-insulated transmission line (GIL) is essential for timely finding the insulation defects to improve maintenance efficiency. Therefore, this paper proposes a method based on the three-dimensional optical signal irradiance fingerprint and bagging-kernel extreme learning machine (Bagging-KELM), which introduces optical simulation into the PD localization. Using a simulation model with the same size and optical sensor arrangement as the experimental GIL, optical simulation signals emitted from different PD sources are collected. Principal component analysis is used to extract the signal features to construct optical PD fingerprints that correspond to PD source locations. This paper builds all the simulated PD fingerprints into a PD fingerprint database. Afterwards, the Bagging-KELM is used to match PD optical fingerprints detected on site with the fingerprint database to identify the location of the PD sources. The experimental results show that the average localization error of this method is 0.93 cm, with 93.75% of the errors being less than 1.5 cm. The proposed method has better performance than the traditional KELM and BPNN, which can achieve precise PD localization in GIL.

A Method of Signal Transmission in Conditions of a High Level of Interference from a Powerful Plasma Installation

The problem of signal transmission in experimental installations is of particular importance for plasma physics due to the characteristic powerful impulse noise caused by the operation of power equipment. This article describes a filter based on a broadband in-phase transformer designed to transmit signals at the KI-1 solar plasma simulation facility of the Institute of Laser Physics, SB, RAS. The parameters of the plasma produced by exposure to a pulse of powerful CO2-laser to a solid-state target, sharply differ from the plasma parameters of confinement installations, which makes the known methods of signal transmission based on classical transformers and optocouplers unacceptable. The presence of X-rays also imposes restrictions on the use of fiber optic solutions.

Defect analysis of the same batch of substation equipment based on big data analysis algorithm

High-quality power equipment is the basis for ensuring the safe operation of the power grid and improving the reliability of power supply. In actual operation, although some electrical equipment can continue to be used, abnormal operation or hidden dangers will affect the safety of people, equipment and power grid, reliable and economic operation of power grid and equipment, equipment output or life span, and power quality. Therefore, research on multi-dimensional analysis of the same batch of substation equipment, and identification of suspected family defects and frequent defects is of great significance for improving the company’s equipment management and defect management. The maturity and promotion of unstructured text data mining, graph computing technology, and semantic analysis have provided a wider dimensional space for the analysis of power equipment defect data. Aiming at the shortcomings of traditional defect data analysis, this article summarizes and analyzes the defects and hidden dangers found in the equipment, traces the distribution status of the same equipment through the physical “ID”, carries out multi-dimensional analysis of the same batch of equipment, and investigates the hidden dangers of equipment family defects. A big data analysis algorithm to build automatic identification models of suspected familial defects and frequent defects in main substation equipment such as transformers, disconnectors, circuit breakers, and use graph computing technology to quickly integrate analysis capabilities in multi-source heterogeneous data fusion with hidden relationship discovery capabilities, identify equipment defects with potential impact relationships. From historical data, discover which manufacturers or purchased batches of equipment are more likely to have the same defect, and which defect types have a higher frequency, improve the recognition model of family defects and frequent defects, and improve the accuracy and comprehensiveness of defect recognition.