Power Monitoring Research Articles

Smart Microgrid : Power Monitoring And Management System With Self-Healing Ability

This research presents a smart microgrid power monitoring and management system with self-healing ability. Solar power plants have been integrated into the microgrid, which previously relied on DG and PLN power supply. However, the system’s monitoring and control are limited. This study develops an effective, efficient and reliable smart microgrid using integration of online monitoring and control system, power management system and self-healing ability. Online monitoring and control system is effectively to monitor and control the microgrid, can detect and analyze potential fault on grid at ease. Power management system is necessary to enhance efficiency and reliability to optimize power distribution. Then, with self-healing ability, enable to detect, to prevent, to isolate and to recover fault on grid automatically, make the smart microgrids more efficient and reliable. Through the Siemens TIA Portal, the study compared the simulation results of a conventional microgrid system with a smart microgrid system incorporate with online monitoring and control system, power management system, self-healing ability. Using some fault scenarios, MTTD value, MTTR value and Availability Rate value are obtained. As result, the applied smart microgrid system decrease the duration to detect and to recover fault on microgrid efficiently, thus the reliability also increased significantly.

Feasibility of using thermoelectric generators to power monitoring platforms of SNF canister internal conditions

Feasibility of using thermoelectric generators to power monitoring platforms of SNF canister internal conditions

Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances

Power quality disturbances (PQDs) are major obstacles to maintaining the reliability and stability of electrical systems. This study introduces a new multi-scale deep learning method to classify PQDs, aiming to enhance the accuracy and efficiency of power quality (PQ) analysis and monitoring systems. By combining 1-D convolutional neural networks (CNNs) with an attention mechanism, this approach overcomes the limitations of traditional techniques. Moreover, varying-size convolutional layers allow for the direct learning of complex patterns and features from PQ signals. To address the challenge of limited labeled PQ datasets, this research utilizes an open-source dataset generator to create large-scale datasets with annotated PQDs. Through a comparison with existing models in the field, the superiority of the proposed CNN-based approach is evident, achieving an accuracy level of up to 99.49%. The results demonstrate promising classification performance in terms of simplicity and accuracy, highlighting the potential of this approach to improve PQ analysis and disturbance identification.

Stable bias control of Mach-Zehnder modulator for arbitrary optical pulse picking via reference pulse power monitoring

Stable bias control of Mach-Zehnder modulator for arbitrary optical pulse picking via reference pulse power monitoring

Electronic Equipment Power Usage Control and Monitoring System in the Home Internet Of Things (IOT) Based

The objectives of this study are (1) to achieve energy efficiency and cost savings (2) Internet of Things (IoT)-based control and monitoring systems using Node MCU ESP32 as a data processing center (3) enabling data processing from PZEM-004T sensors and sending control commands to solid state relays (SSR) based on user input via a website application. The implementation of this system shows significant potential in reducing energy consumption and costs in households. With real-time feedback on energy consumption, users can make wiser decisions about the use of electronic equipment, thereby reducing energy waste. Remote control capabilities allow users to manage electronic equipment more effectively, improve security, and reduce unnecessary energy consumption. This study shows that manual electricity usage reaches 9.59%, while with the implementation of the IoT system it is only 5.49%, so there is a saving in electricity consumption of 4.1%. This proves that the IoT system is more effective and efficient in managing the power consumption of electronic equipment.

Algorithm optimization based on intelligent management of computer electrical equipment: A comprehensive method for PT power monitoring and remote fault indicator

In response to the accuracy limitations and response delays in current power and fault detection of electromechanical equipment, long short-term memory (LSTM) network algorithm was applied for intelligent optimization and management. Firstly, voltage and power data were collected through a potential transformer (PT), and wavelet transform (WT) was applied to remove noise. Fast Fourier transform (FFT) was utilized to extract key features. Secondly, a multi-layer long short-term memory network was designed, and back propagation algorithms and time series power data were used for LSTM network training to analyze abnormal fluctuations and trends in power data in real-time, and adjust threshold settings. Then, combining the model output and historical fault data, a fault mode knowledge base was established. Potential faults were determined through pattern matching, and signals were sent out through remote indicators. Finally, the algorithm model was evaluated. The research results showed that the weighted response time of voltage drop faults was shortened by 3.3%, and the information entropy values of nine experiments were distributed from 5.11 to 5.28. The diagnostic accuracy for frequency drift was improved by 11.1%. The comprehensive algorithm model used can effectively improve the accuracy of power monitoring and response speed. This study optimizes power monitoring and fault diagnosis by applying the LSTM network algorithm, addressing the limitations of existing methods in terms of real-time performance and accuracy. It effectively enhances the fault prediction capability and response speed of power systems, offering significant application value in the fields of smart grids and equipment management.

Optimization of Tool Usage Range for Aircraft Engine Parts Machining Using Power Monitoring and K-means Algorithm

Optimization of Tool Usage Range for Aircraft Engine Parts Machining Using Power Monitoring and K-means Algorithm

Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models.

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming.

Research on Tool Wear Monitoring Technology Based on Variational Mode Decomposition and Back Propagation Neural Network Model.

Accurately predicting tool wear during the machining process not only saves machining time and improves efficiency but also ensures the production of good-quality parts and automation. This paper proposes a combined variational mode decomposition (VMD) and back propagation (BP) neural network model (VMD-BP), which maps spindle power to tool wear. The model is trained using both historical and real-time data. To improve accuracy, the internal power data from the machine tool are used to calibrate the model's input data. Data collected from milling experiments are used to test the model, with sensor-collected power being compared to the model's predicted power. The average error was 1.1256%, which confirms the reliability of the model. In practical applications, the model enables the real-time monitoring of spindle power, helping prevent excessive tool wear during machining. This offers significant guidance for actual production processes.

DESIGN AND CONSTRUCTION OF POWER MONITORING AND CIRCUIT BREAKER DEVICES FOR PLN CUSTOMERS BASED ON IoT

Abstract-Along with the modern Indonesian society's need for electricity consumption, the level of electricity consumption continues to increase. Electricity consumption among postpaid PLN customers with single-phase connections is often poorly regulated due to inadequate monitoring of power usage and frequent payment delays between the 1st and 20th of each month. This study involved the development of a monitoring device designed to track power usage for single-phase PLN customers. Additionally, an electricity circuit breaker was created that can be controlled remotely via the officer's smartphone. This monitoring system uses PZEM-0004T as the sensor. The voltage, current, power and energy data will be read by the sensor and then sent by the ESP8266 to the Internet of Things (IoT) based Blynk Website. This breaker tool uses a relay as an electrical circuit breaker on the customer side. With this tool, it is possible to reduce illegal use of PLN electricity and reduce the number of arrears in customer electricity bill payments to PLN Keywords: Electrical Power Monitoring, PZEM-004T, Internet of Things (IoT), Breaking Device

Review of research and application on digital twin technology

Digital twin is a new technical means to realize the interactive integration of the physical world and the information world. It has been widely used in many business fields and has received more and more attention. Based on the analysis of the connotation and architecture system of digital twin, this paper discusses its key technologies in model construction, data collection and analysis, virtual-reality interaction and visualization, summarizes the research progress of digital twin technology in hot fields such as intelligent manufacturing, smart cities, mining, power monitoring and water conservancy projects, summarizes the existing deficiencies and future development trends, in order to provide reference and inspiration for the development of digital twin technology.

Design and verification of a novel self-powered sensing system for intelligent bearings

Abstract This paper presents the development of a novel self-powered sensing system for roller bearing NU208. The system was designed based on an analysis of the bearing’s structural parameters and service condition monitoring signals. Through the selection of appropriate hardware and the design of a suitable circuitry, the power supply circuit, the main controller circuit, the parameter monitoring circuit, and the wireless data transmission circuit have been constructed. Additionally, the wireless data receiving module program and the upper computer software interface program have been developed. The upper computer software interface enables the real-time display of bearing vibration, cage speed, inner ring speed, and temperature signals. The self-powered sensing system is integrated and encapsulated within the roller bearing NU208, and its performance is verified on a small bearing test bench. The test results are precise and the data accuracy and output frequency meet the anticipated requirements. The self-powered sensing system developed in this paper fulfills the criteria for intelligent bearings that do not require an external power source or monitoring signals via cable transmission. It has significant scientific value and engineering significance for the study of intelligent bearings’ autonomous decision-making and self-regulation.

Advancements of PAPI for the exascale generation

The Performance Application Programming Interface (PAPI) serves as a coherent, operating-system-independent interface for accessing performance counter data across a wide range of hardware and software components. PAPI can operate autonomously as a performance monitoring library and tool for application analysis. However, its true value emerges when it functions as a middleware for numerous third-party profiling, tracing, and sampling toolkits, establishing itself as a universal interface for hardware counter analysis. In this role, PAPI manages the intricacies of each hardware component, presenting a streamlined API to higher-level toolkits. Within the Exascale Computing Project (ECP), PAPI has expanded its capabilities in performance counter monitoring and incorporated support for power management across cutting-edge hardware and software technologies. This includes performance and power monitoring for AMD GPUs through integration with AMD ROCm and ROCm-SMI, Intel Ponte Vecchio GPUs via Intel’s oneAPI Level Zero, and NVIDIA GPUs through the CUPTI Profiling API. Additionally, PAPI is compatible with interconnects, the latest CPUs, and ARM chips. These enhancements have been implemented while preserving the standard PAPI interface and methodology for utilizing low-level performance counters in CPUs, GPUs, on/off-chip memory, interconnects, and the I/O system, encompassing energy and power management. To strengthen PAPI’s sustainability, ECP has facilitated its integration into Spack and E4S, ensuring software robustness through continuous integration and continuous deployment. In addition to hardware counter-based data, PAPI now supports the registration and monitoring of Software-Defined Events. This feature exposes the internal behavior of runtime systems and libraries like PaRSEC, SLATE, Magma, to applications utilizing those libraries, broadening the scope of performance events to include software-based information. Additionally, PAPI has been expanded with the Counter Analysis Toolkit, aiding in native performance counter disambiguation through micro-benchmarks. These micro-benchmarks probe various essential aspects of modern chips, contributing to the classification of raw performance events. In summary, ECP has enabled PAPI to include comprehensive counter analysis capabilities, advanced performance and power monitoring support for exascale hardware components, and broadened the scope of performance events to encompass not only hardware-related metrics but also software-based information.

A 12.68× power-extraction enhancement piezoelectric energy harvesting using an inductor rectifier with adaptive pre-bias and maximum power point tracking

In this paper, an inductor-based rectifier circuit with an adaptive pre-bias structure is proposed, which is suitable for energy extraction from piezoelectric transducers (PZT) under wide vibration conditions. We developed a loss model to calculate the output power, revealing that the pre-bias voltage needs to be adjusted according to varying vibration conditions to achieve maximum power point tracking (MPPT). Accordingly, a successive approximation digital power monitor (SADPM) and hill-climbing algorithm are proposed to adaptive adjust the pre-bias voltage. This method enables the piezoelectric energy harvesting system to enhance power extraction under varying vibration conditions. Finally, the proposed system is designed in a 0.18μm HV BCD process. The post-layout simulation demonstrates that within the maximum open-circuit voltage range of 0.3–1 V, power extraction is enhanced by more than 10.57×, reaching a maximum of 12.68×.

Analysis of the Electric Power Monitoring System in Wind Power Plants Based on Arduino Uno

The Wind Power Plant is a renewable energy solution that can reduce dependence on fossil fuels and reduce greenhouse gas emissions. However, to ensure operational efficiency and reliability, an effective monitoring system is needed to monitor and manage the electrical power produced. This resear chaims to develop and analyzean electrical power monitoring system at PLT Busing the Arduino Uno platform. The proposed monitoring system consist sofsophisti cated sensors integrated with Arduino Unotocollect real-time data regarding wind speed, output voltage, current and turbine operational conditions. The data collectedby these sensor sissentto the Arduino Uno, which then processes andstores thedata for further analysis. Additionally,this data canbe transmitted toot her devices for remote monitoring via a wire less communication module. Algorithms were developed to analyze and display critical information about turbine performance, including anomaly detection and failure prediction. System testing was carried out on a small-scale PLTB model, and the results showed that this system was able to monitor changes in electrical power with high accuracy and fast response time. Implementation of this Arduino Uno-based monitoring system also enable searly detection of potential problems, so that preventive action can be taken to reduce downtime and increase operational efficiency. The results of this research showthat using Arduino Uno as a platform for the electrical power monitoring system at PLTBis not only effective in collecting andanalyzing data,butis also economical andeasy to implement.This system canbeeasily adaptedand expanded to suit the specific needs of various types of PLTB. In conclusion, the Arduino Uno-based monitoring system offers an innovative and practical solution to improve the performance and reliability of PLTB.

AN IOT-BASED FRAMEWORK FOR EFFICIENT SOLAR POWER GENERATION AND INTEGRATION IN AUTOMOTIVE

Objective: We suggest an Internet of Things (IoT)-based system that uses edge intelligence to anticipate power production effectively and monitors electricity substations and smart solar installations. It ensures dependable and effective power distribution inside industrial Internet of things settings, improving sustainability, safety, and energy management in smart buildings. It also improves decision-making and reduces volatility. Method: Create and execute an IoT-enabled power monitoring system for smart solar panels and substations that incorporates edge intelligence for instantaneous prediction and decision-making. Deploy an IoT-enabled solar charging station for smart homes and Industry 4.0 applications, and use the cloud for sensor data analysis and control. Findings: In order to effectively manage load for commercial, electric, residential, and industrial vehicles, the suggested framework improves the efficiency and dependability of power production and distribution in industrial IoT contexts. The system increases overall efficiency via the mitigation of power fluctuations and eventualities. Furthermore, IoT integration enhances smart building energy management safety and sustainability of energy resources as well as reduced the overall cost by 95% when comparing to the traditional devices. Novelty: For smart solar systems and substations, a novel framework combines edge intelligence with IoT. It includes a sophisticated IoT-based control system that improves power distribution network decision-making. In addition to taking an integrated strategy to energy management and enabling real-time monitoring and prediction of power production in industrial IoT contexts, it emphasizes sustainability, safety, recycling, and reuse in smart buildings.

Fukushima Daiichi Nuclear Power Station Accident and Monitoring Post Data —The Role of Monitoring Posts in the Early Stages of a Nuclear Power Plant Accident

Fukushima Daiichi Nuclear Power Station Accident and Monitoring Post Data —The Role of Monitoring Posts in the Early Stages of a Nuclear Power Plant Accident

ANALISIS PENGUKURAN DAN PERHITUNGAN DAYA LISTRIK DENGAN SISTEM MONITORING BERBASIS WEB DI AREA INDUSTRI

The use of electricity must be paid more attention so that there is no overrun of electricity costs. Because of these problems, the author took the title Analysis of electrical power measurement and calculation with web-based system monitoring in industrial areas. This study uses a calculation and measurement research methodology. After measurements and calculations are carried out with system monitoring, the results of the calculation of electrical power measurements and observations are obtained by analyzing the percentage of error values from the comparison of values obtained from the measurement results using the energy monitoring system with the value of the measurement results using the Power monitoring sensor (PMS) and calculations using formulas. The final result of this study was obtained from the results of monitoring voltage, current, power, and power factor. For the results of the analysis of the percentage of electrical power error values, it was carried out by comparing the calculation of Power Monitoring Sensor (PMS) with Energy Monitoring System (EMS) with a monitoring time of 3 months, the first month had an error value of 0.6%, the second month had an error value of 0.2%, and the second month had an error value of 0.1%. Thus, the difference in error values is quite small, it can be concluded that this tool is said to be quite good in monitoring the use of electrical power in industrial areas.

Comprehensive Analysis of Cloud Computing Performance Factors: Investigating the Impact of Response Time, Load Balancing and Service Broker Policies on Cloud Service Efficiency Using CloudSim Simulation

Cloud performance refers to the efficiency and effectiveness with which a cloud system operates delivering hosted services over the internet. As cloud computing continues to offer flexibility, scalability and computational power monitoring and improving cloud performance is essential. Performance optimization is influenced by factors such as load balancing and service broker policies which impact system response times and overall user experience. This paper provides an in-depth review of key publications and real-time cloud performance tools identifying critical performance factors that affect cloud efficiency. Notably, response time emerged as a fundamental metric for cloud service quality. Using CloudSim simulation we examine cloud performance evaluation criteria and experimentally assess the impact of response time dependencies on broker policies, load balancing techniques and data center distribution. This study offers a framework for understanding cloud performance evaluation and highlights strategies to enhance user experience in diverse cloud environments.

IoT enabled implementation of a smart energy management system for real-time monitoring and controlling

The Pakistan power system faces a severe energy deficiency due to population growth. The Pakistan power system structure necessitates a smart load management system to address the mismanagement of load distribution that results in load shedding. A continuous power supply is essential for industrial and domestic sectors to enhance Pakistan's economy. Imported fuel presents a significant economic challenge in Pakistan. This system is designed to control and monitor load management utilizing Internet of Things (IoT)-based technology. The implementation of an IoT-based approach enhances system performance by upgrading conventional load management. This study analyzed effective energy management during peak hours. This system employs a smart energy management system to improve load shedding patterns. The system components include a power monitoring module, current sensor, transformer, Wi-Fi module, and relay module integrated with an Arduino-based microcontroller for monitoring and controlling load management. To model a virtual analysis a simulation based circuit diagram is design using Proteus Software. The implementation of this study improves energy transactions between utilities and consumers. In this study, a prototype was designed and implemented using Arduino, and sensors were employed to control and observe a smart load management system. This system utilizes a hypertext preprocessor for login webpage arrangement for IoT control and monitoring. The results indicate that the cumulative analysis for the implementation of this system automatically deactivates unnecessary loads during peak hours. This system can conserve energy during peak-hour intervals. The load parameters for current, voltage, and power were displayed on the LCD screen. The system automatically deactivates the load during harmonics in the power supply until normal conditions are restored. As compared to existing studies this system detects and switch the system during on and off peak hours without any human interaction.