Industrial Electrical Equipment Research Articles

Predictive-Adaptive Maintenance Applied for Optimizing the Performance of Industrial Electrical Systems and Equipment

Abstract In the Industry 4.0 era, predictive maintenance became a crucial element in ensuring the efficiency and reliability of intelligent industrial systems. This paper proposes a critical study on the role and benefits of predictive maintenance in the context of optimizing and enhancing the performance of industrial electrical systems, more specific the on the asynchronous machine, highlighting emerging perspectives and challenges associated with the implementation of this advanced technology. Additionally, it brings to the forefront the latest concepts and solutions in predictive maintenance to provide a more comprehensive and conclusive view at the time of conducting this case study.

A New Control Strategy of Hybrid Reactive Power Compensation System in Distribution Network

Reactive power compensation is an important measure to improve the power quality of distribution networks, especially with the increasing connection of distribution transformers, heavy industrial electrical equipment, and asynchronous motors, which generate a large amount of reactive power demand. Traditional reactive power compensation devices, such as fixed capacitors (FC) and thyristor switched capacitors (TSC), have some drawbacks, such as low response speed, harmonic generation, and switching shock. A static synchronous compensator (STATCOM) is a flexible AC transmission system (FACTS) device that can provide fast and continuous reactive power compensation, but it also has some limitations, such as high cost, large capacity requirement, and low utilization rate. Therefore, hybrid reactive power compensation systems that combine FC or TSC with STATCOM have attracted much attention in recent years. Many researchers have proposed and studied different topologies and control methods of hybrid systems to overcome the disadvantages of single devices and achieve better performance. However, most of the existing hybrid systems are designed for low or medium-voltage distribution networks, and their performance under high-voltage distribution networks is still unclear. Moreover, the coordination control strategy of the hybrid system and its subsystems is also a key issue that affects the effectiveness and efficiency of reactive power compensation. In view of this, this paper aims to propose a TSC+STATCOM hybrid reactive power compensation system for high-voltage distribution networks, which optimizes the shortcomings of traditional reactive power compensation devices. The system uses the synergistic effect of TSC and STATCOM to achieve large-scale static reactive power and small-scale dynamic reactive power compensation and adopts a switching strategy based on an expert decision system. This paper introduces the working principle of each subsystem of the hybrid reactive power compensation system in detail and builds a simulation model of the hybrid reactive power compensation system under a high voltage distribution network on Matlab/Simulink platform. The simulation results verify the feasibility and excellent performance of the system control method in reactive power compensation and improving power quality.

PROFIT PREDICTION USING MULTIPLE LINEAR REGRESSION METHODS PYTHON PROGRAMMING LANGUAGE AT PT TRI ERDNOV REZEKI

PT. Tri Erdnov Rezeki is a national private company founded in 2016 and fully under the leadership and ownership of Indonesian entrepreneurs. PT. Tri Erdnov Rezeki is also engaged in Construction Development Planning and Repair and Maintenance of Steam Aircraft and Pressure Vessels, Supply of Technical Equipment, Industrial Work Equipment, Electrical Equipment, and Machinery, especially in the Palm Oil Plantation and Power Plant Sectors. In carrying out a project, the company does not only carry out technical design but also must carry out the economic design so that the company can determine the economic feasibility of a project. However, companies often experience cost calculation errors. Data science can be utilized to predict the value achieved in a period using previous data. Data science will analyze patterns related to data with other data to produce a reference or a formula that can be used as a value prediction in the future. So, the author will use the multiple linear regression method using the Python programming language, which functions to perform statistical analysis, namely predicting profits in a project. The analysis results in this study show that the profit variable is influenced by 99.7% by the variables material cost, labor cost, and utility cost. In comparison, other variables outside the study influence the other 0.3%. The material cost variable has the most significant influence, where the value is below 0 .05 compared to the variable labor cost and utility cost to the profit variable. The average percentage of Python prediction errors is 0.97%, where the average percentage of Python prediction errors is smaller than the average percentage of SPSS prediction errors which is 2.04%.

Development of an Industrial Electrical Installation Training Unit

The purpose of this study is to obtain a modular unit for training and instruction in industrial electrical installations as a suitable learning media. This study uses research and development (RD) with ADDIE model, which consists of five phases: analysis, design, development, implementation, and evaluation. The research subject is Diploma 4 student in Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Yogyakarta. The research result as follows. (1) Manufacture of industrial electrical equipment training module products. (2) Application program feasibility for material-related assessment includes content aspects, learning aspects, and efficiency aspects. All aspects of the substantive feasibility study were rated as highly feasible with a score of 94.5 points. Application program feasibility for media professional evaluation includes visual, hardware, usability, and portability aspects. It has an average score of 109.5 on all aspects of the media feasibility test and is categorized as highly feasible. (3) Student user reactions, including learning, visual, hardware, usability, and portability. All aspects of student user response were rated as very good with an average score of 107.1.

Large-area, highly thermally conductive, but electrically insulative industrial aramid papers with hierarchical multiscale compositing structures

The rapid development of emerging 5G electrical fields has put forward high requirements to the thermal dissipation performance of industrial aramid papers (IAPs), which are commonly used as the insulation layers of electrical equipment. Herein, we reported a facile method for preparing large-area (>7000 cm2), thermally conductive, but electrically insulative IAPs by compounding multiscale boron nitride nanosheets (BNNSs), industrial diamond microparticles (DMPs), and industrial aramid micropulps and microfibers (AMPs & AMFs) towards hierarchical compositing structures. Specifically, BNNSs can adhere to the surface of DMPs through electrostatic interaction and reduce the thermal resistance between neighboring DMPs, which ultimately ensures high in-plane thermal conductivity (11.66 W/(mK)) of IAPs, which is about 7 times that of pure aramid papers. Meanwhile, IAPs also exhibit high volume resistivity of 4.95 × 1015 Ω·cm. At last, the IAPs were successfully applied in a transformer for insulation and thermal conducting, demonstrating their high potential in industrial electrical equipment.

Intelligent Fault Diagnosis Method for Industrial Processing Equipment by ICECNN-1D

Intelligent algorithm has been widely implemented to effectively diagnose faults in industrial instrument, electrical equipment and mechanical equipment. In addition, the rapid development of sensing technology generated enormous time series signal. Accordingly, diagnosing faults by analyzing time series signal has been widely developed. This paper aims to diagnose faults by applying improved Convolution Neural Network with Compression Enhancement (ICECNN-1D) to analyze time series signal, which effectively considers time series property of signal while diagnosing faults by artificial intelligence. Additionally, a large number of trend features and fluctuation features in high-frequency time series are also considered. the recognition rates of almost other machine learning algorithm are less than 90% in the experiments. Other methods may provide high rate of recognition, but their fluctuation of the recognition rate has varied obviously with different loads, and results provide undesirable ability of generalization under different working conditions. Comparatively, ICECNN-1D model provides high recognition rate and terrific ability of generation while processing time series with high frequency, and its accuracy of the recognition rate fluctuates inconspicuously with different loads.

Application of the gas-chromatographic analysis method for the characterization of oils in high capacity industrial electrical equipment

The article aims to highlight one of the highest environmental issues, caused by pollution with persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs). This form of environmental pollution with a negative impact on human health would not be possible without the use of high voltage industrial electrical equipment with dielectric fluids infested with various PCB isomers. In order to carry out the experimental program, samples of dielectric fluids both from Romania and Indonesia were investigated. The PCBs-containing samples were characterized using GC-ECD technique, employing an Agilent 7890 gas chromatograph equipped with a 15 mCi 63Ni µECD detector. PCBs concentrations were calculated using the standard calibration method based on the internal standard according to the corresponding normative documents. Ongoing work is carried out to adapt the technique for the analysis of PCBs in soils contaminated with oil products.

Design and Implementation of Hybrid Harmonic Filter for Converter System

Quality of power is essential for all electrical systems towards their reliable operation. In specific voltage/current magnitude, frequency and waveforms are the key parameters that decide the quality of electric power supply. Recently enormous nonlinear loads such as converters, inverters, variable frequency drives, and uninterrupted power supply are used in all modern industrial sectors which leads to power quality disturbance in power systems In particular, harmonic distortion is one of the main power quality causes produced by nonlinear loads which will affect the reliable operation of industrial electrical equipment. Harmonic filters help to maintain the harmonic limits as per the IEEE 519-2014 standard. This research article overviews the various types of harmonic filters and the design procedure for the passive harmonic filter. Also, this research paper briefly converses the design and control technique procedure for the proposed hybrid harmonic filter, used to control the harmonic distortion produced by the three-phase converter system. The results of the experimental setup with the implementation of a hybrid harmonic filter clearly show that the harmonic distortion is greatly reduced. The high accuracy power quality analyzer used in this research work measures the real-time voltage/current waveforms, power/energy, and harmonic profiles.

Оптимизация работы схемы электронного предохранителя с регулируемым током перегрузки

The article discusses the application of a rapid action electrical fuse with controllable overload current in the schemes of industrial and household electrical equipment. There has been found a deficiency in the basic version of the scheme. A variant of the circuit was proposed, which helps to get rid of the drawback in the basic version: if a condenser with relatively large capacity (C ≥ 100 μF) is present in the protected circuit (in the load circuit), the circuit may not reach the operating mode, because, when the condenser is on, its large charging current triggers the electronic fuse and the load remains unconnected. This situation can be avoided only if the charging current of the condenser is greatly reduced, for which a current source on a field-effect transistor VT3 and a timing circuit R10R11C1 were introduced into the circuit. As C1 is being charged, through the current source VT3 there is a smooth increase in the bias voltage at the emitter junctions of the composite transistor switch VT1VT2 with a gradual increase in its conductivity. As a result, the starting current through the capacitive load at the first moment of time turns out to be much less, and the electronic fuse enters the operating mode. The optimal relationships were found between individual elements of the circuit, their values and modes of operation, depending on the size and nature of the load, while, thanks to the use of the domestic element base, the cost of the device turned out to be minimal compared to other circuits of a similar type. The research data can be used for operation of the devices of a similar type in the household and for industrial applications.

English

English

Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

Monitoring of Industrial Electrical Equipment using IoT

This article presents a monitoring system for industrial electrical equipment in a production line, aiming at real-time monitoring of its operational state, allowing machine management and early detection of deviations and failures. The system measures the effective current draw of the monitored equipment using sensors connected to a data concentrator module, which stores the data collected by these sensors and performs preliminary processing before transmission to an Internet of Things platform. Preliminary data processing focuses on analyzing the time series of the electric current values in order to detect the operating state of the monitored machine. This information is then sent over the Internet to an IoT platform for long-term storage, post-processing and real-time visualization of data by end users. When a behavior deviation is detected in current consumption related to some type of potential failure, the system issues alerts and sends additional information to the production line supervisor, for example to proceed with equipment maintenance intervention without impact on production. The availability of full-time data series, as well as the history of all occurrences recorded by the monitoring system, also allow for correlations with data from other sources and their interpretation in contexts other than machine operation or maintenance. The proposed monitoring system provides minimal automation on older machines and opens up the possibility of independent, parallel and non-intrusive monitoring on machines that already have supervisory systems.

Proposal of identification method for material constants by the adjoint variable method for industrial electrical equipment

Proposal of identification method for material constants by the adjoint variable method for industrial electrical equipment

Identification Method for Material Constants of Industrial Electric Equipment Based on the Adjoint Variable Method

Identification Method for Material Constants of Industrial Electric Equipment Based on the Adjoint Variable Method

LSTM and Edge Computing for Big Data Feature Recognition of Industrial Electrical Equipment

With the rapid development of Industrial Internet of Things, the category and quantity of industrial equipment will increase gradually. For centralized monitoring and management of numerous and multivariate equipment in the intelligent manufacturing process, the equipment categories shall be identified first. However, manual labeling of electrical equipment needs high costs. For the purpose of recognizing industrial equipment accurately in manufacturing systems, this study adopts the long short-term memory to analyze big data features and build a nonintrusive load monitoring system. Edge computing is used to implement parallel computing to improve the efficiency of equipment identification. Considering the practical popularity, the fairly priced low-frequency Smart Meter is used to collect the appliance data. According to the proposed optimal adjustment strategy of parameter model, the average random recognition rate can achieve 88% and the average recognition rate of the continuous data of a single electrical equipment can achieve 83.6%.

Analysis of Voltage Sag Severity Case Study in an Industrial Circuit

This paper presents the results of a power quality study carried out in an industrial distribution system. The main objective of this study was to quantify the negative impact caused by voltage sags in industrial processes and its relationship to generated interruptions. A series of measurements of power quality variations were taken at the substation that feeds the circuit and at the point-of-common coupling of each industrial user. The new standard IEEE Std 1564-2014 “Guide for Voltage Sag Indices” was used to determine the indicators to assess the severity of voltage sags and also the calculation method. Sag severity was selected as it allows the calculation of the probability of voltage sags to generate interruptions in industrial processes or electrical equipment. Thus, an analysis of the quality of the supply voltage can be done. Severity was initially calculated at the substation that feeds the industrial circuit and later, at the point-of-common coupling of each user. Subsequently, the relationship between user interruptions and severity values was determined. Finally, recommendations of good engineering practices in industrial processes were done.

A Strategic View for Rare Earths Production, in a Competitive and Sustainable form

The demand for rare earths (RE) has been intensified by their large use, especially in high technology sectors. Supply difficulties have forced RE users to seek alternative sources and invest in the development of recycling technologies and options of reuse for these elements. This article seeks to reveal the trends and ongoing changes in national and global prospects of RE. Additionally, it aims to analyze scientific collaboration networks in the area of industrial solid waste (ISW) and waste electrical and electronic equipment (WEEE) exploitation in Brazil, examining both researchers and institutions with greater representation in the field. For this purpose, social network analysis methods were used to build and analyze co-authorship networks based on scientific publications retrieved from the Web of Science (WoS) database. The results showed that the Brazilian collaboration network of ISW research was extremely fragmented and contained 105 different groups, which were not connected to each other. The institutional network of ISW research was composed of 125 institutions, 75.2% of them from Brazil. The Brazilian collaboration network of research in WEEE was small (37 researchers), but fragmented: researchers were divided into eight different groups that do not connect to each other. The institutional network of research in WEEE was composed by 12 institutions, nine of them from Brazil. Therefore, this article presents a network collaboration model to bring together actors involved in the management of waste electrical and electronic equipment (WEEE), emphasizing the potential for recovery of RE from these wastes, with the purpose of developing products and services.

Measurements, Observations, and Implications of Moving Electrical-Arc Behavior and Effect of Reclosure Events on Overhead Lines and Worker Protection

Characteristics of an electric arc in industrial and utility electrical equipment is dependent on many factors such as fault-current level, duration of the arc, geometry of feeding conductors and electrodes, proximity of panels, and other items affecting the direction or focus of the arc energy. The voltage rating of the system will affect the overall dimensions of conductor spacing, which will result in higher released energy for the same fault conditions. Another factor of importance is the type of heat energy (predominately radiant or predominately convective). This can be generalized into four types: 1) moving high-heat flux arc in open air of overhead lines or substation buswork; 2) stationary directional high-heat flux arc in open air at the end of overhead line or substation buswork or transformer bushing; 3) ejected directional high-heat flux arc in enclosed medium-voltage switchgear or cable-splice failure; and d) directional hot air exhaust from low heat-flux arc in low-voltage switchboards' cabinets and motor control centers. The results of experimental testing for type 1 arc behavior and incident heat energy measurements of a moving and stationary open-air arc are presented and discussed. Work practices for mitigating the effect of electric arc are recommended for overhead lines.

Design of Wireless Monitoring System for Electric Equipment Based on CC2530

In order to monitor a wide distribution, quantity, long working hours for industrial electric equipment, this paper provides a solution based on Zigbee wireless sensor networks. The core CC2530 chip is the control center of the entire system, and holzer sensor is selection as a current sensor. Through the wireless network which is composed of terminal nodes, routing nodes and coordinator, the collected data is sent to PC terminal and displayed. The experimental results show that the wireless sensor network can effectively and reliably collect current harmonics of electric equipment and transfer to the PC terminal in time.

Assessing the impact of space weather on the electric power grid based on insurance claims for industrial electrical equipment

AbstractGeomagnetically induced currents are known to induce disturbances in the electric power grid. Here we perform a statistical analysis of 11,242 insurance claims from 2000 through 2010 for equipment losses and related business interruptions in North American commercial organizations that are associated with damage to, or malfunction of, electrical and electronic equipment. We find that claim rates are elevated on days with elevated geomagnetic activity by approximately 20% for the top 5% and by about 10% for the top third of most active days ranked by daily maximum variability of the geomagnetic field. When focusing on the claims explicitly attributed to electrical surges (amounting to more than half the total sample), we find that the dependence of claim rates on geomagnetic activity mirrors that of major disturbances in the U.S. high‐voltage electric power grid. The claim statistics thus reveal that large‐scale geomagnetic variability couples into the low‐voltage power distribution network and that related power‐quality variations can cause malfunctions and failures in electrical and electronic devices that, in turn, lead to an estimated 500 claims per average year within North America. We discuss the possible magnitude of the full economic impact associated with quality variations in electrical power associated with space weather.