Power Monitoring Research Articles

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 validated 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.

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

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.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Machine learning for base transceiver stations power failure prediction: A multivariate approach

The widespread deployment of cellular networks has improved communication access, driving economic growth and enhancing social connections across diverse regions. Base Transceiver Stations (BTSs), are foundational to mobile networks but are vulnerable to power failures, disrupting service delivery and causing user inconvenience. This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. We employ a combination of deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, to achieve accurate and timely predictions of BTS power failures. CNNs were selected for extracting dependencies among features of a multivariate time-series data, while LSTMs effectively capture temporal dependencies, making them suitable for predicting power failures.The proposed models exhibit noteworthy predictive performance, with the LSTM network emerging as the most accurate model (MSE: 0.001, MAPE: 2.528), followed by the hybrid CNN-LSTM (MSE: 0.001, MAPE: 2.843) and the CNN (MSE: 0.223, MAPE: 2.843). This work demonstrates deep learning’s effectiveness in preemptive BTS failure prediction, enabling proactive maintenance and improved network resilience.

Evolution of water technology from a structural perspective

Rivers are cradles of human civilisations and continual innovations in water technologies are the key to sustainable human development. Yet, limited research has explored the interactive dynamics among different technologies, hindering our ability to effectively manage technological transition. This paper presents a framework that conceptualizes water technology as a complex adaptive system containing three interrelated sub-systems: water demand, water supply, and water management. The interactions among these sub-systems were measured using three network-based metrics: intensity, brokerage, and efficiency. Patents registered in the World Intellectual Property Organization from 1863 to 2020 were used as the data source. It was found that 40,304 patents from 44 countries were registered, with 40% of them belonging to the water management sub-system, followed by water supply (35%), and water demand (25%). Technological development in the three sub-systems presented linear growth over the past 160 years, focusing on water treatment, hydroelectric power, hydraulic engineering and water monitoring. The water-demand sub-system was identified as the structural “bottleneck” with the highest brokerage value, which was considered crucial for knowledge transfer between the other two sub-systems. Overall, the water technology system is characterized by slow development, skewed spatial coverage, categorical homogeneity, and structural imbalances, limiting our ability to address the escalating global water threats.

Generating Payloads of Power Monitoring Systems Compliant with Power Network Protocols Using Generative Adversarial Networks

In the network environment of power systems, payload generation is used to construct data packets, which are used to obtain data for the security management of network assets. Payloads generated by existing methods cannot satisfy the specifications of the protocols in power systems, resulting in low efficiency and information errors. In this paper, a payload generation model, LoadGAN, is proposed by using generative adversarial networks (GANs). Firstly, we find segmentation points to cut payloads into different segment sequences using sliding window schema based on Bayesian optimization. Then, we use different payload segments to train several child generators to generate corresponding parts of a whole payload. Segment sequences generated by these generators are assembled to form a whole new payload that is compliant with the specifications of the original network protocol. Experiments on the Mozi botnet dataset show that LoadGAN achieves precise payload segmentation while maintaining a high payload effectiveness of 85.5%, which is a 40% improvement compared to existing methods.

Si-waveguide-based optical power monitoring of a 2 × 2 Mach-Zehnder interferometer based on a InGaAsP/Si hybrid MOS optical phase shifter.

Transparent in-line optical power monitoring in Si programmable photonic integrated circuits (PICs) is indispensable for calibrating integrated optical devices such as optical switches and resonators. A Si waveguide (WG) photodetector (PD) based on defect-mediated photodetection is a promising candidate for a transparent in-line optical power monitor, owing to its simplicity and ease of integration with a fully complementary metal-oxide-semiconductor (CMOS)-compatible process. Here, we propose a simple optical power monitoring scheme for a 2 × 2 Mach-Zehnder interferometer (MZI) optical switch based on InGaAsP/Si hybrid MOS optical phase shifters. In the proposed scheme, a low-doped p-type Si WG PD with a response time of microseconds is utilized as a transparent in-line optical power monitor, and the ground terminal of the MOS optical phase shifter is shared with that of the Si WG PD to enable the simple monitoring of the output optical power of the MZI. Based on this scheme, we experimentally demonstrate that the output optical power of a 2 × 2 MZI can be simply monitored by applying a bias voltage to the Si slabs formed at the output WGs of the MZI without excess optical insertion loss.

Impact of mechanical power on ICU mortality in ventilated critically ill patients: a retrospective study with continuous real-life data

BackgroundOver the past decade, numerous studies on potential factors contributing to ventilation-induced lung injury have been carried out. Mechanical power has been pointed out as the parameter that encloses all ventilation-induced lung injury-contributing factors. However, studies conducted to date provide data regarding mechanical power during the early hours of mechanical ventilation that may not accurately reflect the impact of power throughout the period of mechanical ventilatory support on intensive care unit mortality.MethodsRetrospective observational study conducted at a single center in Spain. Patients admitted to the intensive care unit, > o = 18 years of age, and ventilated for over 24 h were included. We extracted the mechanical power values throughout the entire mechanical ventilation in controlled modes period from the clinical information system every 2 min. First, we calculate the cutoff-point for mechanical power beyond which there was a greater change in the probability of death. After, the sum of time values above the safe cut-off point was calculated to obtain the value in hours. We analyzed if the number of hours the patient was under ventilation with a mechanical power above the safe threshold was associated with intensive care unit mortality, invasive mechanical ventilation days, and intensive care unit length of stay. We repeated the analysis in different subgroups based on the degree of hypoxemia and in patients with SARS CoV-2 pneumonia.ResultsThe cut-off point of mechanical power at with there is a higher increase in intensive care unit mortality was 18 J/min. The greater the number of hours patients were under mechanical power > 18 J/min the higher the intensive care unit mortality in all the study population, in patients with SARS CoV-2 pneumonia and in mild to moderate hypoxemic respiratory failure. The risk of death in the intensive care unit increases 0.1% for each hour with mechanical power exceeding 18 J/min. The number of hours with mechanical power > 18 J/min also affected the days of invasive mechanical ventilation and intensive care unit length of stay.ConclusionsThe number of hours with mechanical power > 18 J/min is associated with mortality in the intensive care unit in critically ill patients. Continuous monitoring of mechanical power in controlled modes using an automated clinical information system could alert the clinician to this risk.

Continuous High-Temperature Thermoelectric Power Monitoring of Thermal Embrittlement

Continuous High-Temperature Thermoelectric Power Monitoring of Thermal Embrittlement

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

Overview of the neutron diagnostic systems for the SPARC tokamak.

Neutron measurement is the primary tool in the SPARC tokamak for fusion power (Pfus) monitoring, research on the physics of burning plasmas, validation of the neutronics simulation workflows, and providing feedback for machine protection. A demanding target uncertainty (10% for Pfus) and coverage of a wide dynamic range (>8 orders of magnitude going up to 5 × 1019 n/s), coupled with a fast-track timeline for design and deployment, make the development of the SPARC neutron diagnostics challenging. Four subsystems are under design that exploit the high flux of direct DT and DD plasma neutrons emanating from a shielded opening in a midplane diagnostic port. The systems comprise a set of ∼15 flux monitors, mainly ionization chambers and proportional counters for measurement of the neutron yield rate, two independent foil activation systems for measurement of the neutron fluence, a spectrometric radial neutron camera for poloidal profiling of the plasma emissivity, and a high-resolution magnetic proton recoil spectrometer for measurement of the core neutron spectrum. Together, the four systems ensure redundancy of sensors and methods and aim to provide high resolutions of time (10ms), space (∼7cm), and energy (<2% at 14MeV). This paper presents the broader objectives behind the preliminary design of the SPARC neutron diagnostics and discusses the ongoing studies on neutronics, detector comparisons, prototyping, and integration with the unique infrastructure of SPARC. Engineering details of the four subsystems and the concepts for in situ neutron calibration are also highlighted.

Empowering energy conservation: A prototype of household energy consumption monitoring based on Internet of Things

Abstract One of the obstacles to control household energy consumption is unavailability electricity instruments/tools to monitor electricity energy consumed by appliances causing a lack of awareness of household energy saving. Energy meter installed on buildings only show electricity consumption in total. This study aims to design and create a prototype system that can monitor and control the electrical appliances usage utilizing the Internet of Things (IoT)-based electronic devices where electricity usage data is stored in Firebase real-time database. The methods carried out are arranged in several stages, starting from data collection, design, configuration, and implementation. The tools used in this research will utilize the ESP8266 as the microcontroller, the PZEM-004T as current sensor and the Blynk as the web server. The tool will send current voltage (Volt (V)) and capacity (Watt (W)) data and then converted to energy and also the costs incurred from electricity usage based on the basic electricity tariff of National Electricity Company (PLN) and the results can be seen directly through IoT (i.e. smartphone, PC, laptop). The accuracy of power measurement by the system is 97.4% compared to measurement used by watt-checker. The results of this study show that an electrical power monitoring system makes people easier to monitor electrical power consumption by appliances used. The pilot measurement shows that a unit system can monitor electricity usage for four units of electronic equipment i.e. water pump, Television, fridge, electrical iron, rice cooker, etc. precisely and succeeded to control and increase energy saving awareness to reduce electricity consumption at the end.

Cluster-based unsupervised method for eavesdropping detection and localization in WDM systems

Ensuring the secure and reliable operation of optical networks is crucial for various societal functions. However, optical network infrastructures are susceptible to unauthorized interception, posing a significant security risk at the physical layer. This necessitates the development of effective detection and localization methods of eavesdropping events. To address this challenge, we present a clustering-based method and a comprehensive eavesdropping diagnosis framework tailored for wavelength division multiplexing (WDM) systems. The framework is designed to handle diverse eavesdropping scenarios, including dynamic detection, classification, and localization of eavesdropping events. To mitigate the data dependency issue while detecting and localizing eavesdropping events, we propose a clustering algorithm utilizing basic optical performance monitoring (OPM) data, thus eliminating the need for sophisticated measurement equipment. A coarse localization requires only the OPM data from the receiver, while a finer localization requires the power monitoring data at all nodes as the input. The feasibility of the proposed scheme is validated using simulation-generated data, in which single and multiple eavesdropping can be detected and localized with a 100% label matching rate. Single-point eavesdropping detection and localization are experimentally validated with data collected from a fiber transmission system comprising three spans of 40 km each. Coarse localization with a 99.79% label matching rate and fine localization with 100% accuracy is achieved. As expected, experimental data shows a less concentrated distribution than the simulated data, which leads to inferior clustering results.

A Novel Lock-In Amplification-Based Frequency Component Extraction Method for Performance Analysis and Power Monitoring of Grid-Connected Systems

A Novel Lock-In Amplification-Based Frequency Component Extraction Method for Performance Analysis and Power Monitoring of Grid-Connected Systems

A new approach for GNSS spoofing detection using power and signal quality monitoring

Abstract Global Navigation Satellite System (GNSS) signals are highly susceptible to spoofing attacks. Signal Quality Monitoring (SQM) methods are simple and easy to detect spoofing. However, traditional SQM methods are only effective for matched-power cases and exhibit high detection probability only for a short time. This study introduces a new approach, exploiting anomalies in receiver correlation outputs during spoofing. It efficiently detects signal amplitude fluctuations and correlation peak distortions. The Texas Spoofing Test Battery (TEXBAT) dataset is used to evaluate the detection performance of the proposed approach. The results show that the proposed approach has excellent detection performance in various spoofing cases, including matched-power, overpowered, static, and dynamic cases. This approach surpasses traditional SQM metrics in detection probability and sensitivity to spoofing stages. Importantly, the proposed approach detects spoofing early.

Redesigning automated market power mitigation in electricity markets

Electricity markets are prone to the abuse of market power. Several U.S. markets employ algorithms to monitor and mitigate market power abuse in real-time. The performance of automated mitigation procedures is contingent on precise estimates of firms' marginal production costs. Currently, marginal cost is inferred from the past offers of a plant. We present new estimation approaches and compare them to the currently applied benchmark method. We test the performance of all approaches on auction data from the Iberian power market. The results show that our novel approaches outperform the benchmark approach significantly, reducing the mean (median) absolute estimation error from 11.53 (6.08) €/MWh in the benchmark to 4.03 (2.64) €/MWh for our preferred approach. This approach also performs best in our subsequent simulation of mitigation procedures. Here we find large welfare transfers from supplier to buyer surplus as well as a robust overall welfare gain, stemming from both productive and allocative efficiency gains. Our research contributes to accurate monitoring of market power and improved automated mitigation. Although we focus on power markets, our findings are applicable to monitoring of renewable energy tenders or market power surveillance in rail and air traffic.

A 0.01–50 GHz power detector with temperature compensation and wideband DC‐blocking capacitor

AbstractThis letter presents a 0.01–50‐GHz resistive matching power detector implemented in a commercial 0.15‐ GaAs pseudomorphic high electron mobility transistor technology. An analytical expression is derived for the voltage responsivity of the detector as a function of temperature. To compensate for the temperature dependence of the detector, bias diode topology and mesa resistor load are employed. For an input power of −20 dBm at 1 GHz, the maximum variation of the detector output voltage is less than 0.5 dB over the temperature from C to C. The detector's S11 is less than −8 dB, the dynamic range (DR) is 55 dB, and the maximum voltage responsivity is 700 V/W. An on‐chip wideband capacitor with a bent‐strip shape is designed for direct current blocking. The detector can be used for wideband power monitoring and power amplifier control loop for its high DR and temperature stability.

Multistream Power Monitoring for Energy Systems

Multistream Power Monitoring for Energy Systems