Electrical Monitoring Research Articles

Assessing the risk of slope failure to highway infrastructure using automated time-lapse electrical resistivity tomography monitoring

Electrical resistivity tomography (ERT) monitoring provides time-lapse images of the subsurface. These images can be used to assess spatiotemporal variation in moisture content, which is a key driver of slope failure, making ERT monitoring an effective tool to evaluate precursory conditions of failure. This work presents the results of ERT monitoring on a slope above a major highway located on the border between England and Wales. During highway construction in the 1960s the slope was subject to several large landslide events which resulted in the re-design of the carriageway and installation of engineered mitigation measures. A section of the slope known as the ‘partially slipped area’ exhibited partial displacement during this time but did not progress to full slope failure, and therefore presents an ongoing risk to the highway, even though it does not experience ongoing displacement. An ERT monitoring system was installed across this area to monitor subsurface variations in moisture content. The results show a complex pattern of subsurface moisture dynamics within the partially slipped area when compared to the adjacent area of stable slope. This is most likely a result of the uneven and hummocky terrain in the partially slipped area and its effects on rainfall infiltration, storage and drainage, combined with the displacement-induced jointing present in the underlying sandstone units. The ERT results are used to assess the volume of unstable ground, placing the volume at the upper end of estimates from previous studies. Furthermore, analysis of the ERT dataset for surface displacements shows no movement at the site, which is confirmed by analysis of differential LiDAR plots and ground motion data derived from InSAR. This study demonstrates the application of ERT monitoring on a low activity, high risk slope, highlighting the need to understand subsurface processes at the slope-scale to inform long-term slope management.

Application of wireless sensor network technology based on artificial intelligence in security monitoring system

Abstract The safety monitoring system has been used to monitor and manage engineering safety operation. The application scope of the safety monitoring system is very wide. It has a wide range of applications in the fields of pipeline safety monitoring, electrical safety monitoring and household safety monitoring. This article studied the application process of the household safety monitoring system. Many home safety accidents are caused by inadequate monitoring of safety problems. Therefore, it is very important to establish a household safety monitoring system. Traditional home safety monitoring systems only rely on cameras for safety monitoring, and the traditional home safety monitoring system uses too few sensors. With the continuous development of wireless sensor network (WSN) technology, it is possible to build a sensor node network, but provides real-time information for home security monitoring to the greatest extent. This article compared the home safety monitoring system based on the WSN technology of artificial intelligence (AI) with the traditional home safety monitoring system. The experimental results showed that in the large-scale home environment, the average monitoring accuracy of the traditional home security monitoring system and the home security monitoring system based on the WSN technology of AI was 77.76 and 89.36%, respectively. In the small-scale home environment, the average monitoring accuracy of the traditional home safety monitoring system and the home safety monitoring system based on the WSN technology of AI were 87.63 and 94.43%, respectively. Monitoring accuracy refers to the accuracy of the household safety monitoring system in detecting safety issues. Therefore, the application of the WSN technology based on artificial intelligence to the home safety monitoring system can effectively improve the accuracy of home safety monitoring.

Piezoelectrically controlled wave propagation in laminates with auxetic core: Transient analysis incorporated with electrical stability monitoring

Piezoelectrically controlled wave propagation in laminates with auxetic core: Transient analysis incorporated with electrical stability monitoring

SISTEM MONITORING DAYA LISTRIK MENGGUNAKAN INTERNET OF THING (IOT) BERBASIS MOBILE

This research aims to develop an electrical energy monitoring system using Internet of Things (IoT) technology and based on mobile applications to increase the efficiency of electrical energy use and reduce energy waste. The context of this research is the increasingly urgent need to monitor and optimize electrical energy consumption, both in domestic and industrial environments, as part of efforts to reduce environmental impact and Operating costs related to electricity consumption. The methodology used in this study includes the design and implementation of an IoT system consisting of energy sensors and hardware connected to the Internet, as well as the development of a mobile application as a user interface. Energy consumption data recorded by the sensors will be transmitted wirelessly to mobile applications for real-time monitoring and analysis. The results of this study show that an IoT-based electrical energy monitoring system is capable of providing accurate and timely information on electrical energy consumption, thereby helping users increase energy efficiency and Reduce operating costs related to electricity consumption. Additionally, the system allows users to easily monitor and control electrical energy consumption through their mobile devices, which in turn can contribute to more resilient and sustainable energy management. This research is important in efforts to create a more sustainable and energy-efficient environment, as well as raise public awareness about electrical energy consumption.

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

Apparent Hysteresis in Archie’s Law: Implications of Changing Pore Fluid Conductivity for Electrical Resistivity Monitoring of Infiltration

The electrical resistivity of the vadose zone is highly dependent on soil moisture content (VWC), making ERT a potential tool for non-intrusive infiltration monitoring. There’s a long history of using Archie’s law to convert resistivity values to VWC, often under the assumption that the pore-fluid conductivity is constant. Analysis of two years of soil conductivity and VWC data from sensors buried in a bioswale in Philadelphia, Pennsylvania showed that the characteristic curve of VWC versus resistivity exhibited hysteresis. The same value of resistivity was associated with different VWC during imbibition and drying. Accurate VWC could be estimated using Archie’s law only after incorporating changes in pore-fluid conductivity. Additionally, pore-fluid conductivity varied seasonally, with the highest values recorded during winter from road salt runoff. Archie exponents differed for sensors within 25 cm of each other despite both the uniform nature of the bioswale soil and fits to Archie’s Law for individual sensors, indicating considerable heterogeneity. This pattern was confirmed at two additional sites. Assuming the conductivity of the pore-fluid remains constant during infiltration not only produces erroneous soil moisture estimates, it can even reverse the apparent soil moisture trend. We conclude that to use ERT to monitor infiltration it is essential to independently record changes in pore-fluid conductivity, and that multiple sensors are required because changes in pore-fluid conductivity and Archie parameters can vary on a sub-meter scale for even apparently homogeneous sites. Additionally, long-term changes in Archie parameters, notably m, are possible, and may indicate long-term changes in soil fabric.

Electrical resistance-based monitoring of CFRP thickness during vacuum assisted resin infusion

During vacuum-assisted resin infusion (VARI), the thickness of the fabric manufactured into carbon fiber (CF)-reinforced plastic was monitored using electrical resistance, which was correlated with the mechanical and electrical status of the CF with respect to resin infiltration. Furthermore, the electrical network within CF varied in terms of its volume and number of electrical contacts. These phenomena were monitored by installing a Cu tape on the mold. Thus, an in-situ qualitative monitoring system for composite manufacturing was developed utilizing the electrical resistance variations in the fabric. The proposed thickness-monitoring system for VARI can potentially minimize the cost, time, and labor in composite industries.

Sistem Monitoring Pemakaian Energi Listrik Pada Kamar Kost Menggunakan Aplikasi Blynk Berbasis Internet of Things

Currently, the electrical energy monitoring system can only be monitored by PLN. Electricity users can usually only see the amount of usage each month without knowing electricity usage at any time. Excessive use of electricity will cause payments to increase. Therefore, this research was conducted to monitor the electrical power used in boarding houses via an internet connection using the blynk application. This study aims to determine the amount of electrical energy consumption every day. This lot-based monitoring tool uses the PZEM-004T sensor to read the amount of electrical energy consumption such as voltage, current, power and energy. As for the media interface that displays the amount of electrical energy consumption using the LCD and the Blynk application.

Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications.

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

Design of Speed Limiter Display For Uwin Fly Electric Bike Based on Internet of Things

To contribute to the development of electric vehicle technology in Indonesia, a data monitoring system has been designed to monitor and remotely control the condition of electric motorcycles. The device is designed using a nodeMCU ESP8266 microcontroller, voltage sensor, switch, button, Hall sensor, and buzzer. Data is processed and displayed on Blynk. Experiments were conducted on battery monitoring, speed, speed mode, crush control, and speed limiter alarms. Implementing the device on the Uwin Fly electric bicycle can serve as a reference for the feasibility of the device on other electric bicycles. Additionally, the development of electric bicycle monitoring displays and speed limiter features can increase awareness among electric bicycle users, especially mothers and children. Overall, the monitoring system could contribute to enhancing the safety and efficiency of electric motorcycles on the roads of Indonesia.Meanwhile, if the article is written in English, the abstract should be typed in English only. The abstract should be typed as concisely as possible and should be composed of: problem statement, method, scientific finding results, and a short conclusion. The abstract should be typed in one paragraph only and two-columns format. All sections in the manuscript should be typed using Times New Roman font. Authors should use this document as their article template.

Rheology and setting time of saline cemented paste backfill

As many mines around the world are situated in freshwater-scarce regions and corporations are challenged to meet increasing sustainability and environmental responsibility standards, many mining operations are seeking to use locally available saline groundwater or ocean water as mixing water in cemented backfill. However, the implications of this decision on the rheological properties (which control the flow ability or transportability) and setting time of cemented backfill must be better comprehended to allow for a confident selection of this practical solution. Hence, the effects of mixing water salinity and binder type on the rheological properties and setting time of cement paste backfill (CPB) were investigated. Viscosity, yield stress, and setting time testing were conducted on CPB made with Portland cement type I (PC) and varying slag replacement quantities, silica tailings (ST), and mixing water with NaCl concentrations from 0 g/L to 300 g/L. Supplemental testing and analyses included pH determination, TG/DTG and XRD microstructure analyses, zeta potential measurements, and electrical conductivity monitoring. Results indicate that low NaCl concentrations (10 g/L − 35 g/L) in PC-CPB increased the yield stress above the control while higher concentrations (100 g/L − 300 g/L) decreased it. An increase in NaCl concentration led to slower increase in viscosity over the first two hours of curing. Low NaCl concentrations reduced the time to initial and final setting while high concentrations increased it. These behaviors were mainly due to the effects of the Na and Cl ions on the dissolution of the cement components, and on the changes in zeta potential due to chloride adsorption. Increases in slag replacement quantities on saline CPB had minimal effect on the yield stress during the first two hours after mixing but decreased the viscosity. All tested slag contents increased the time to initial setting, though a replacement content of 75% did not increase the initial setting time as much as lower quantities. A slag replacement content of 25% increased the final setting time, though higher contents decreased it. This behavior was due to the slower reaction rate of slag, combined with slag’s consumption of NaCl and CH and greater chloride binding capabilities. The results of this study will aid mines in designing more cost-effective and efficient backfill programs.

IMPLEMENTASI SISTEM MONITORING PRODUKSI ENERGI PLTS BERBASIS PROTOKOL MODBUS RTU DAN MODBUS TCP

The use of renewable energy sources is currently starting to grow rapidly in line with the need for environmentally friendly power sources. Solar power plants are a choice of solutions for the community, especially those in areas that have not been reached by the electricity grid. The production of the resulting electrical energy is highly dependent on the capacity of the installed solar panels. In order to monitor solar power plants electricity production and usage load, a facility is needed that can provide real-time information. Besides that, it can easily be accessed remotely by utilizing a network that uses the TCP/IP protocol. The problem that arises then is that the sensor data obtained from the readings must comply with the standard parameters that apply to all sensor equipment. The Modbus RTU and Modbus TCP protocols are the right choice to answer this problem. This research will discuss the implementation of the Modbus RTU/TCP protocol within the scope of solar power plants. Thus, solar power plants electrical energy monitoring system allows it to be applied to networks that are located far from the control or monitoring center.

Monitoring and Control of 3 Phase Electrical Energy Internet of Things (IoT) Based

Monitoring the use of electrical energy is generally done by looking at the kWh meter on the installed panel. Power consumption can also be done by measuring directly on the network or electrical load using a Watt meter. Monitoring of 3-phase electrical energy needs to be monitored, especially to find out the amount of energy used in each phase, and on the other hand, it needs to be controlled so that energy use can be controlled, both for each phase or 3 phases. This study aims to design an IoT-based 3-phase electrical energy monitoring and control application. Data on the flow of electrical energy flowing to the load can be monitored from the android, and vice versa can be controlled by disconnecting and reconnecting the energy flow. The PZEM004T component is used as an energy flow sensor, buttons on the application function to disconnect and connect energy flows, and LCD12C and smartphones are used as monitoring devices. The Blynk application is equipped with several switch buttons used to control energy use, along with electronic device control. The data read by the tool can be retrieved and stored in Blynk using the superchart widget so that the operator can see previous history record data sent to emails that have been linked to the Blynk application according to the operator's wishes. The results of the research show that the research tool can display voltage, current, power, energy, and frequency data that can be displayed on a smartphone and LCD. The average error of the mains power compared to the reference meter is 0.02%.

IMPLEMENTATION AND ANALYSIS OF THE INTERNET OF THINGS SYSTEM FOR ELECTRICAL ENERGY MONITORING AT INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Measurement of electric power usage is carried out using simple measuring instruments and the recording is still manual so that the data obtained is not real-time and accurate. This research aims to implement an electrical energy monitoring system using the Internet of Things (IoT) to obtain real-time information related to electrical energy in the education industry. This research uses an Industrial Grade Power Meter to get a more accurate measurement value. To connect the Power Meter device with the IoT system, this research uses Modbus RS485 communication and a mini PC to process data from the meter, so that the data can be sent to a server using the MQTT communication protocol, and displayed on the Dashboard. The test results of this study indicate that the monitoring system can be implemented and the system runs well with end-to-end measurement results. From the measurement results, the current value (3 phase average) has an average deviation of 0.001 Amperes, Voltage (3 phase average) has an average deviation of 0.519 V, Power factor has an average deviation of 0.012, Active power has a deviation average of 0.000 kW, reactive power with an average deviation of 0.000 kVAR, apparent power with an average deviation of 0.000 kVA and frequency with an average deviation of 0.124 Hz. Then the MQTT protocol has a quality of service with index 4 based on TIPHON standardization on delay, throughput, and packet loss parameters, and index 3 based on TIPHON standardization on jitter parameters.

An analytical approach to model transverse inter-fibre fracture evolution in cross-ply carbon fibre reinforced polymers laminates under a three-point bending test

The prerequisite to equip structures with in situ electrical health monitoring systems in a meaningful manner is to estimate potential damage locations. In this context, an analytical approach to model the spatial and temporal evolution of transverse inter-fibre fractures (IFF) is exemplified for [Formula: see text] cross-ply laminates under three-point bending. The model is based on the tensile load in the bottom 90°-layer. The effective stress is computed directly from the specimen displacement, whereas the strength is considered to be periodically distributed due to material inhomogeneities along the specimen. The continuous comparison between the sinusoidal strength function and the effective stress allows modelling progressive damage in form of IFF. A modified Hann window is used to consider their effect to the bottom 90°-layer. Experiments with the same configuration are performed and the observed IFF evolution is used to tune the model parameters in an optimisation procedure, e.g., the peak separation of the sinusoid and the length of the Hann window. The comparison between model and experiment shows a high level of agreement. The model is thus capable to reproduce experiments with minimal computational effort. This makes it highly suitable as input for electrical monitoring models that rely on the continuous damage evolution of the investigated structure. In its current form, the model is limited to the specified configuration. However, the simple analytical approach allows it to be easily adapted. It is furthermore shown that progressive composite damage in terms of spatial and temporal evolution can be accurately described using an analytical approach.

Advanced Energy and Industrial IoT

The Internet of Things (IoT) has been enforced in different industries, including the electrical sector and the broader industrial landscape. IoT offers immense potential for enhanced electrical system monitoring, control, and optimization by seamlessly connecting devices, sensors, and equipment to the Internet. This issue explores IoT's current realization and implementation in electrical and industrial sectors and its impact on them. Additionally, it delves into the crucial connection between IoT, relay protection, and automation, highlighting the benefits of this integration in terms of efficiency, reliability, and safety. Furthermore, this issue explores perspectives and adaptations of IoT in the energy and industry sectors. The realization of IoT in electrical systems and the industry sector holds significant promise. As IoT technology continues to evolve, it will enable the seamless integration of various components within electrical systems and industrial processes. This integration will lead to intelligent automation, streamlined workflows, and real-time decision-making, improving operational efficiency, productivity, and resource optimization. The convergence of IoT, electrical systems, and the industry sector will create an intelligent ecosystem that redefines traditional practices and establishes a foundation for sustainable growth and competitiveness.

Lifetime Prediction of Lithium Ion Batteries by Using the Heterogeneity of Graphite Anodes

Lithium ion batteries for electric vehicles demand high-level monitoring of their safety and long-term cyclability. To this end, identifying defective cells at the postmanufacturing stage would be the most effective approach. Here, we introduce the lithiation heterogeneity as a key descriptor for inspecting and grading as-manufactured cylindrical cells. Specifically, the peak intensity of the differential voltage (DV) profile during charge provides a quantitative indication of the heterogeneity of lithium ion intercalation into graphite, in relation to the staging effect of the graphite anode. The degree of heterogeneity determined by the DV profile is closely correlated with the cycle life, implying that the heterogeneity analysis can serve as a useful tool for inspecting as-manufactured cells and predicting their cycle life at an early stage. Moreover, the ability to accurately grade as-manufactured cells allows a high-performance battery module to be built by integrating cells with low heterogeneity and minor deviations in their performance.

Autonomous Electrical Current Monitoring System for Aircraft

Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A – 100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A low-power sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring.

Reliable Long-Term Energy Load Trend Prediction Model for Smart Grid Using Hierarchical Decomposition Self-Attention Network

Extending the length of time series forecasting has a long-term impact on smart grid energy consumption planning, residential electricity monitoring, extreme weather warning, and other real applications. This article studies the reliable long-term load trend forecasting approaches in smart grid environment. In recent years, the improved neural networks models based on self-attention mechanism show good performances in many sequence tasks, but most studies focus on reducing the complexity of networks layer, and can not restrain the increase of calculation error in longer distance forecasting scenarios. Also, most models lack the ability to mine potential high-dimensional features of time series. Based on these problems, we design a reliable hierarchical self-attention model named as long-term stability network (LTSNet), which adopts a tree-shaped decomposition neural network architecture based on hierarchical residual self-attention blocks, to top-down incrementally mine high-dimensional features of temporal components. At the same time, the attention matrix is used for feature interaction at each layer, to reduce the distribution gap between time series fragments in different domains. Compared with existing study models, LTSNet maintains stable forecasting performance and speed in long-term forecasting services, achieved the most reliable multivariate and univariate forecasting results in multiple domain scenarios. Compared with the latest models, the forecasting accuracy of the proposed model is improved by 22.8&#x0025; and 13.8&#x0025;, respectively, covering three applications: energy consumption, residential electricity consumption, and weather forecasting. At the same time, our experiments verify that the hierarchical decomposition networks can be used as a backbone architecture to effectively extended to longer dimensional load trend forecasting scenarios.