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Pd Pd Pd Pda Compress Application
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Jonathan dos Santos Cruz Jonathan dos Santos Cruz Scilit Preprints.org Google Scholar 1, * , Fabiano Fruett Fabiano Frutt Scilit Preprints.org Google Scholar 1, Renato da Rocha Lopez Renato da Rocha Lopez Scilit Ta. Gabio Luis Takaki, chapter 2, Claudia de Andrade Tambrigues de Leyda Scientist Tanaubregt Mats Giesbricht Tratus Giesbricht Tratus Giesbricht. org Google Scholar 1
Received: September 22, 2022 / Revised: October 7, 2022 / Accepted: October 12, 2022 / Published: October 27, 2022
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Online monitoring of partial discharges (PD) in rotating electrical machines is a useful tool for machine prediction as it offers lower costs compared to intrusive inspections and their associated problems. Although this method of monitoring has been in development for almost 50 years, recent advances in process automation and signal processing techniques have resulted in improvements that are still being studied by academic and industrial researchers. In order to analyze the current context of PD monitoring, this article presents a literature review based on the concepts of PD in rotating machines, data acquisition techniques, modern commercial equipment, and the latest techniques for PD detection and pattern recognition. Issues identified in the literature that motivate the development of more robust and reliable PD monitoring systems are presented and discussed.
The global energy matrix has changed and diversified since the industrial revolution. The world’s energy consumption is provided by primary sources such as biofuels, solar energy, wind, hydropower, nuclear power, natural gas, oil, coal, biomass and other renewable energy sources. In 2021, this consumption will be 176,431 TWh. The most significant contribution to electricity generation comes from sources such as wind, hydropower, nuclear power, natural gas, oil, coal, and biomass, totaling 170,215 TWh [1]. Most of these sources can be converted into electrical energy using rotating electrical machines. These machines are also used as engines for converting electrical energy into mechanical energy in industry, commerce and at home. Since these machines operate in very difficult conditions, high operational reliability is required. Premature equipment failure under these conditions can result in significant economic losses, either due to process failure or physical damage to assets [2].
In demanding installations, machines operate under intense load conditions, also known as TEAM load, i.e. thermal, electrical, environmental and mechanical stress leading to structural deterioration of assets [3, 4]. In these installations, rotating electrical machines can act as generators or motors. A rotating electrical machine consists of two main structures, defined as a stator and a rotor. In most generators, the stator conducts most of the electrical energy converted from mechanical energy, so it is important. It can be seen from [5] that stator failures in turbogenerators account for 23% of the total number, while rotor and other types of failures account for 14% and 63%, respectively. In hydro generators, the stator winding insulation is the design most prone to defects. In electric motors, failures of the stator, rotor and other types correspond to 36%, 9% and 55%, respectively, while a large number of stator failures are associated with insulation wear.
Given the presented data, one can be convinced that the area of research related to the development of methods for diagnosing malfunctions of rotating electrical machines is extensive. It attracts more and more attention of researchers and companies that implement diagnostics based on data collected for different operating conditions of machines in different areas of analysis [6, 7].
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The main wear factor is electrical, mainly associated with high intensity partial discharges in the stator winding. Partial discharge (PD) is a small discharge of short-term electric current due to local breakdown of the dielectric of a small section of the winding insulation system [8, 9, 10].
Continuous PD activity greatly contributes to the deterioration of the stator winding insulation and can eventually lead to damage. Due to the high costs associated with these assets [11], especially for medium and high voltage devices, early diagnosis of the presence of partial discharges is necessary in order to be able to analyze the quality of the winding insulation system. As a result, asset monitoring through partial discharge analysis is essential to implement an appropriate maintenance plan.
Partial discharges can be measured using several signal domains, including electrical, thermal, mechanical (acoustic and inertial), magnetic, optical, and chemical. Electrical methods are the most widely used and studied commercially. Non-electrical methods generally complement published electrical methods.
In the face of the challenges and solutions presented by the last decade of development of PD monitoring systems, this study provides a review of the literature on PD concepts, commercial equipment, methods for measuring and identifying PD. Literature search was carried out in Scopus, the largest database of abstracts and citations of articles from peer-reviewed scientific journals and conferences. The most recent studies were selected to present the most effective methods for collecting, analyzing and interpreting data. In addition, relevant but rarely covered topics in the literature, such as: the influence of the drive system on measurement data and the position of the PD in the coils, are also selected as reliable with high reliability. this should be taken into account. Two factors.
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To develop the study, Section 2 presents previous reviews of partial discharge in rotating machines. Section 3 describes the main principles of the PD. Then, in section 4, an overview of the commercial equipment used for partial discharge detection is presented. Section 5 is devoted to modern measurements of partial discharges. Section 6 discusses directions for further development, including some improvements needed to implement a reliable system. Finally, Section 7 presents the conclusions.
The development of partial discharge monitoring systems for rotating electrical machines requires a general study of the subject, as it involves very complex hardware and software concepts and methods. Over the past few years, several studies have been developed on this topic. However, the selection of methods and potential problems used in these studies is still rarely addressed.
[12] provides a historical background on partial discharge measurements from the 1940s to the 1970s, in addition to such important points as a cursory description of the components and methods for measuring and analyzing partial discharges used from the 1940s to the 1970s. e years. PD measurement reliability issues related to noise, PD indicators, sensor reliability, machine insulation life, and misidentification of failure causes are also commented on superficially. The study also presents state-of-the-art noise reduction techniques, identifies poor air insulation, and suggests improvements to improve system reliability.
An overview of the main characteristics of PD, such as types, causes, characteristics and risks, is presented in [13]. The study describes the installation configuration of capacitive sensors and current transformers for PD pulse transmission and data acquisition modes. Statistical methods for signal and time domain filtering are also discussed. Supervised and unsupervised machine learning methods for pattern recognition are presented. In addition, suggestions for improvement were discussed.
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Reference [14] provides an overview of PD in inverter driven rotating machines and discusses the effect of inverter voltage waveform characteristics such as rise time, pulse width and frequency, and number of inverter levels on PD patterning. PD detection methods are also described.
Topics covered in [12, 13] include the importance of determining a PD threshold indicating an abnormality during machine operation, taking into account the characteristics of each machine, the impact of machine drive systems on PD detection, and the exact position of the machine. PD sources in the stator winding of machines are not discussed. In [14]
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