Power Meter Research Articles

Performance of Heat Pipe with Different Working Fluid on Harvesting Wasted Heat Energy at Air Cooled Split Unit

These days, life would not be comfortable without air conditioning which leads to high power consumption due to air conditioning and subsequently increases the carbon footprint. In this research, verification was done on the capability of increasing superheat to improve the performance of the air-cooled split unit and the effect of power consumed by the air conditioning system. The superheat was increased by the heat harvested from the condensing unit of the air-cooled split unit by using a heat pipe. The heat pipe was charged with water, R134a, and R600a with a 100% filling ratio and 90 and 70 inclination angles. K-Type thermocouples, pressure gauges, and power meter were used in data collection. According to data analysis, water with a 90 inclination angle of heat pipe showed the best performance compared to other fluids because of its ability to increase superheat by approximately 32% compared to R600a which increased only approximately 6.5% compared to normal conventional air conditioning. However, it demonstrated that a rise in the superheat at the suction line and a rise in the Coefficient of Performance cannot guarantee a fall in energy usage. This was due to an increase of superheat by the water heat pipe at 90 angle inclination indicating approximately a 28.8% increase in power consumption.

Evaluation of laser power stability of repeatedly used SubCyclo probe in micropulse transscleral cyclophotocoagulation for glaucoma: A step towards sustainable ophthalmic surgery

Purpose To evaluate the laser power stability of the SubCyclo probe for micropulse transscleral cyclophotocoagulation after repeated use. Materials and methods This experimental study involved 6 new probes. Each probe was connected to the SubCyclo mode (2,000 mW power, 31.3% duty cycle, and 100 seconds duration) of the Vitra 810 laser delivery system (Quantel Medical, France). Laser power measurements were taken using a calibrated laser power meter (Nova, Ophir Optronics Solutions, Israel) every 10 seconds from 10 to 90 seconds during each of the 40 cycles. Intra-rater reliability was assessed using intraclass correlation (ICC). A linear mixed model for repeated measures and pairwise comparisons with Bonferroni adjustment were used for the analysis. Results The mean (SD) power outputs of all probes for the first cycle and all cycles were 421.9 (19.7) mW and 436.7 (16.1) mW, respectively. During the first cycle, the mean (SD) laser power gradually decreased from 444.3 (13.4) mW at 10 seconds to 407.3 (17.0) mW at 90 seconds (Fig 3). For all cycles, the power was 446.0 (13.6) mW at 10 seconds and gradually declined to 426.8 (21.0) mW at 90 seconds. Pairwise comparisons revealed significant differences in mean laser power outputs after 16 cycles of repeated use compared to the first cycle. The ICC estimate (95% CI) for intra-rater reliability was 0.96 (0.89, 0.99). Conclusions The SubCyclo probe maintains stable laser power outputs throughout repeated use for up to 16 cycles, with a significant increase observed after 16 cycles.

Fault Status Monitoring of Power Metering and Detection Equipment Based on BiLSTM

The power metering and detection equipment is a device used to measure the power consumption of users and energy loss of power lines. It is of great significance to timely identify and handle operational anomalies of electric power metering and detection equipment to ensure stable and reliable operation of the metering device. In response to the problems of low accuracy and poor robustness in existing fault diagnosis methods for electric power metering and detection equipment, this paper proposes an intelligent fault diagnosis method based on deep learning. The paper combines the characteristics of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) in extracting local and global features, and combines them with self-attention (SA) mechanism to propose a hybrid network model SA-CNN-BiLSTM for monitoring the status of electric power metering and detection equipment. This model first extracts local information through the CNN network. At the same time, BiLSTM can extract global features, and then the feature fusion layer combines these two different features. Then, the self-attention mechanism weight adjustment layer is used to calculate and adjust the weights of the fused feature vectors. Finally, the fused feature vectors are input into the fully connected layer, and fault classification predictions are obtained through a softmax classifier. System experiments have demonstrated the correctness and effectiveness of this method.

Design and Implementation of IoT based PV Integrated Power Monitoring System

The awareness of usage of power and effective utilization of power is based on intelligent decision, which can help the user as well as supplier. In this paper the design and implementation of IOT based PV integrated system is presented. A hardware prototype is developed with PV module connected to boost converter, Arduino Uno controller, ESP 8266 Wi-Fi module, and Thing speak to monitor a real-time power. The Arduino Uno serves as the central controller, gathering data from smart power meters and PV modules for solar energy generation. The collected data is wirelessly delivered to the ThingSpeak, which provides cloud-based storage, data analysis, and visualization. With these users may remotely monitor usage of power, track solar energy generation, and get notifications for energy utilization.

A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage.

Anomaly Diagnosis Method and Condition Assessment of Power Metering Device Based on SSD Algorithm

The advancement of anomaly diagnosis methods plays a crucial role in classifying and analyzing data, particularly in distinguishing between normal and abnormal patterns. This study explores the utilization of Support Vector Machine (SVM) techniques to facilitate the selection of pertinent data, thus enhancing the accuracy of anomaly detection. Furthermore, this research delves into the condition assessment process, which offers valuable insights into the real-time state of entities passing through power metering devices. Drawing upon a wealth of secondary data sources, this study employs the SSD algorithm to gain a comprehensive understanding of power metering devices and their interrelated aspects. The SSD algorithm, with its diverse anchor points, is revealed as a powerful tool for quantifying the energy flow passing through the power metering device. This approach not only aids in precise energy measurement but also provides essential insights into the functioning of power metering systems. By combining anomaly diagnosis, SVM techniques, and the SSD algorithm, this study contributes to a deeper comprehension of power metering devices' performance and their capacity to accurately measure energy. These insights have significant implications for improving the overall reliability and efficiency of power metering in various applications.

The Weekly Periodization of Top 5 Tour de France General Classification Finishers: A Multiple Case Study.

The aim of this study was to describe individual training characteristics, racing strategies, and periodization in preparation for the Tour de France in 2 world-class road cyclists finishing in the top 5 of the general classification. Week-by-week power meter training and racing data of 2 (A and B) road cyclists (age: 29 and 23y; maximum oxygen consumption: 83 and 81mL·min-1·kg-1; and relative 20-min record power output: 6.9 and 6.5W·kg-1) in the preparation phase (December-July/August) leading up to the Tour de France were retrospectively analyzed. Weekly volume and intensity distribution in power zones were considered. Cyclists A and B completed 46 and 19 races, 22.5 (6.3) and 18.2 (5.1)h·wk-1, with a pyramidal intensity distribution of 81.0%-13.3%-5.7%, and 88.8%-7.9%-3.3% in zone 1-zone 2-zone 3. Cyclist B spent 14days at altitude. Increased high-intensity volume and polarization index occurred during race weeks. During periods without racing, training intensity progressively increased. Strength training was performed during November and December but not during the following months. During tapering, total exercise volume and time at high intensity decreased. These data provide novel insights into the periodization of world-class road cyclists in advance of a top 5 placing in the Tour de France general classification.

Analisis Beberapa Jenis PLTS di Khatulistiwa Menggunakan Prototype alat ukur PV

Photovoltaic is employed to support the national electricity energy blending policy as an additional component of renewable energy. Efficient energy monitoring is utilized to understand the efficiency, energy requirements, and performance of Photovoltaic. This research aims to analyze the performance of a standalone Photovoltaic system. The study considers various parameters such as PV size, load, and charge controller. Additionally, it involves designing a monitoring tool used for the analysis of Photovoltaic performance. Performance measurements are conducted by comparing Photovoltaic systems of different sizes. Furthermore, performance measurements are compared with different types of charge controllers. To assess performance, solar energy is measured at various times using a solar power meter. The solar power meter is then used to analyze the research's performance. Energy monitoring involves measuring voltage, current, power, and energy using Atmega328p. The measurement data is utilized to evaluate system efficiency and design future systems. Monitoring is carried out directly on Photovoltaic systems in the equatorial region. This research measures solar energy, energy produced by Photovoltaic systems, and energy consumed by loads. Energy monitoring focuses on observing solar and Photovoltaic energy during sunlight hours (morning-evening). The analysis results can be utilized for designing efficient Photovoltaic systems in the future..

Analysis of fluid flow in a cylindrical tube using fiber Bragg grating

Fiber Bragg grating (FBG) is an optical sensor component that has better performance than other optical and electronic components. FBG has a very high sensitivity to changes in temperature and strain, is small in size, and is resistant to electromagnetic wave interference and multiplexing. In this study, FBG is used to monitor the fluid flow rate in a pipe by analyzing changes in the output power generated by the FBG. This research was designed by varying the pipe diameter, measurement position, and fluid flow rate. The diameters of the pipes used are 5.95, 7.01, 8.79, and 10.32 mm. The fluid flow rate that passes through each pipe is also varied. The measuring position is placed at 1/3 2/3, and 3/3 pipe diameter. The fluid flow rate and the output power generated by this FBG are measured using an anemometer and optical power meter respectively. The FBG used in this study has a center wavelength of 1310 nm and 1550 nm. In this study, the value of power generated from pipe 1 of position 1 FBG with a wavelength of 1550 nm is 15.1 dBm, while at a wavelength of 1310 nm is -26.23 dBm which indicates that the power generated from FBG with a wavelength of 1310 is the biggest. The speeds obtained at pipe position 1 from the anemometer measurements are 0.79, 1, 1.82, and 2.22 m/s which are directly proportional to the power generated at the 1550 wavelength FBG, -15.1, -15.12, -15.47, and -15.43 dBm, respectively. So it can be concluded that the greater the speed generated by the fluid flow, the greater the power generated.

PENGARUH TEMPERATUR PERMUKAAN PANEL SURYA TERHADAP KAPASITAS DAYA YANG DIHASILKAN

Solar panels are a semiconductor component that is capable of converting solar energy into electrical energy through the photovoltaic principle. Fluctuations greatly influence the electrical energy produced by solar panels in surface temperature. Therefore, this research aims to assess the impact of solar panel surface temperature and compare the power produced by two types of solar panels, namely polycrystalline and monocrystalline panels. The method used in this research is direct measurement using a temperature measurement device with an infrared thermometer and a solar power meter. The results of measurements and analysis show that the higher the surface temperature of the solar panel, the less power produced will decrease. In addition, in a comparison of the output power between polycrystal and monocrystal-type solar panels, it was found that the power of the monocrystal type was higher than the polycrystal type.

Vampire: A smart energy meter for synchronous monitoring in a distributed computer system

This paper presents the design and implementation of a low-cost system oriented to the synchronised and real-time surveillance and monitoring of electrical parameters of different computer devices. To measure energy consumption in a computer system, it is proposed to use, instead of a general-purpose wattmeter, one designed ad-hoc and synchronously collects the energy consumption of its various nodes or devices. The implementation of the devised system is based on the confluence of several technologies or tools widely used in the Internet of Things. Thus, this article the intelligent objects are the power meters, whose connections are based on the low-cost ESP32 microcontroller. The message transmission between devices is carried out with the standard message queuing telemetry transport (MQTT) protocol, the measurements are grouped in a database on an InfluxDB server that store the sensor data as time series, and Grafana is used as a graphical user interface. The efficiency of the proposed energy monitoring system is demonstrated by the experimental results of a real application that successfully and synchronously records the voltage, current, active power and cumulative energy consumption of a distributed cluster that includes a total of 60 cores.

Impact of dust particles on the output power of photovoltaic modules in Calabar, Cross River State

During the dry season, dust particles on photovoltaic modules (PV modules) are a regular occurrence in Calabar, Cross River State. This dusty condition or environment is caused by the migration of dust particles from the Sahara region via the east-west wind. The concentration of dust on PV modules has an impact on the overall performance of solar energy systems. This research looks into the effect of dust particles on the output power of solar modules. This investigation was carried out by subjecting 80-watt monocrystalline PV modules to an outdoor experiment and analyzing the changes in current and voltage from the control and dusty PV modules with a digital multimeter. Other environmental factors such as relative humidity, ambient temperature, and solar radiation were also measured with a hygrometer, thermometer, and solar power meter respectively. Appropriate formulars were employed to calculate output power and efficiency. Dust particles on the surface of PV modules dramatically reduces the quantity of sunlight that penetrate the panel by 21.10%, resulting in a drop in the system's overall output power. The study emphasizes on the importance of frequent cleaning and maintenance of PV systems.

Malfunction diagnosis of main station of power metering system using LSTM-ResNet with SMOTE method

The power metering system is an important part of the smart grid for data acquisition and analysis. The fault state of the main station directly affects the stable and safe operation of the power metering system. Hinged on the real-world data supplied by the monitoring platform of the Metrology Center of Guangdong Power Grid Co., Ltd., we present a novel malfunction diagnosis method for the main station of the power metering system. The proposed method utilizes the synthetic mi-nority over-sampling technique (SMOTE) and designs a combined model of long short-term memory (LSTM) network and ResNet. SMOTE solves the sample imbalance problem. Furthermore, the combined LSTM-ResNet model employs LSTM to extract the time-dependent signal feature and exploits ResNet to optimize data flow. Consequently, the proposed LSTM-ResNet model improves training efficiency and malfunction diagnosis accuracy. The proposed diagnosis mthod is verifird on the real-world data, which proves the proposed method’s surpass traditional methods. A specific analysis of results and the practical application of the proposed method is also elaborated.

Running Functional Threshold versus Critical Power: Same Concept but Different Values.

The aims of this study were (i) to estimate the functional threshold power (FTP) and critical power (CP) from single shorter time trials (TTs) (i. e. 10, 20 and 30 minutes) and (ii) to assess their location in the power-duration curve. Fifteen highly trained athletes randomly performed ten TTs (i. e. 1, 2, 3, 4, 5, 10, 20, 30, 50 and 60 minutes). FTP was determined as the mean power output developed in the 60-min TT, while CP was estimated in the running power meter platform according to the manufacturer's recommendations. The linear regression analysis revealed an acceptable FTP estimate for the 10, 20 and 30-min TTs (SEE≤12.27 W) corresponding to a correction factor of 85, 90 and 95%, respectively. An acceptable CP estimate was only observed for the 20-min TT (SEE=6.67 W) corresponding to a correction factor of 95%. The CP was located at the 30-min power output (1.0 [-5.1 to 7.1] W), which was over FTP (14 [7.0 to 21] W). Therefore, athletes and practitioners concerned with determining FTP and CP through a feasible testing protocol are encouraged to perform a 20-min TT and apply a correction factor of 90 and 95%, respectively.

Design and research of wireless optical power meter based on IoT big data and physical quantity

In response to the problems of low accuracy, high radiation, and high power consumption in industrial UV power detection, the author proposes a design scheme based on a low-power microcontroller MSP430 as the core controller, two UV photoelectric sensors as detectors, and wireless LoRa as the data transmission method. This scheme adopts dual sensors and achieves photoelectric conversion and ambient light filtering through a subtractive amplification circuit. It also uses high-precision programmable AD acquisition chips to achieve controllable amplification of the signal and convert analog signals into digital quantities for transmission to the main controller MSP430. The improved sliding average filtering method is used to perform digital filtering on the collected data and calculate the corresponding power value, data is transmitted to the upper computer for real-time display through wireless Lora. Research and experiments have shown that the dual sensors have a good agreement with the standard values. The difference between the measured values after digital filtering and the standard values is small, and the linearity of digital filtering is good. It can be seen from the two figures that the higher the power value, the higher the lighting temperature. It has been proven that the entire system operates stably, with high accuracy, low power consumption, and can remotely and real-time detect changes in ultraviolet light power. It can be widely used in the field of industrial ultraviolet light detection.

WattScope: Non-intrusive application-level power disaggregation in datacenters

Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters’ increasing energy consumption and carbon footprint. The most basic prerequisite for optimizing a datacenter’s energy- and carbon-efficiency is accurately monitoring and attributing energy consumption to specific users and applications. Since datacenter servers tend to be multi-tenant, i.e., they host many applications, server- and rack-level power monitoring alone does not provide insight into the energy usage and carbon emissions of their resident applications. At the same time, current application-level energy monitoring and attribution techniques are intrusive: they require privileged access to servers and necessitate coordinated support in hardware and software, neither of which is always possible in cloud environments. To address the problem, we design WattScope, a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server’s aggregate power usage and without requiring direct access to the server’s operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server’s total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power meter measurements that are already available in data centers. We evaluate WattScope’s accuracy on a production workload and show that it yields high accuracy, e.g., often <∼10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage.

Behavioural Pattern of Solar Filter Enhanced Photovoltaics in Mangrove Swamp

Not all wavelengths incident on a solar cell enhances the efficiency of the cell. Some wavelengths only cause electrons to twist and vibrate in their bonds which heats up the cell and inevitably hinder its efficiency. To tackle this problem, solar cells need to be exposed to distinct wavelengths of light so as to ascertain those wavelengths that adversely affect its efficiency. This study is aimed at experimentally investigating the effect of solar power and solar flux on photovoltaic (PV) modules enhanced with solar filters. The objective includes to ascertain which filter triggered higher power and efficiency from PV modules. In achieving the objectives, color filters were employed for wavelength selection, an intelligent photovoltaic maximum power point tracker was employed to determine the maximum voltage, current and efficiency of the PV module at a particular level of solar power and solar flux, while solar power and solar flux meters were employed to track the amount of the transmitted solar power and solar flux reaching the surface of the PV module. The experimental measurements were conducted in outdoor real-time conditions with varying levels of solar power and solar flux. The results revealed points of voltage increase and points of voltage stability, and also revealed filters that attained voltage stability with relatively lower levels of solar power (400 W/m2 for orange and blue filter) and solar flux (45 Klx for lemon filter). The results also reveal the module attaining a higher efficiency under the red filter as solar flux increases. In terms of solar power, the red filter and the natural spectrum led in power generation, but the natural spectrum triggered a higher efficiency from the module as solar power increases. This study proved that the nature and wavelength of the radiation reaching a photovoltaic module influences its behavior and efficiency.

Determining critical power and W′ in running: Accuracy of different two-point models using the power metric

This study aims to determine the validity of the critical power (CP) and the work capacity over CP (W′) obtained from different two-time trial combinations with respect a five-point model. In a 3-week training period, 15 athletes (age: 23 ± 5 years; height: 166 ± 6 cm; body mass: 58 ± 8 kg; 5 km season-best: 15:29 ± 00:53 mm:ss) performed five time-trials (i.e. 3, 4, 5, 10, 20 min) on a 400 m track, from which the mean power outputs were obtained through the Stryd Power Meter. An acceptable level of agreement was considered if the following criteria were met: low bias and standard error of the estimate (SEE) (&lt;14 W [values corresponding to the ±5% of the mean CP]; W′: &lt;2.0 kJ [values corresponding to the ±10% of the mean W′]), R2 &gt; 0.90, and ICC &gt; 0.81. The CP presented an acceptable SEE for CPwork (1.3 ± 0.5%) and CP1/time (2.7 ± 1.1%) when using the five time-trials. For both CP models, the 3–10 min was the shortest valid combination, whereas the 3–20, 4–20, and 5–20 min showed the greatest level of agreement. The W′ presented a high SEE for CPwork (14.1 ± 5.2%) and CP1/time (13.8 ± 6.2%) when using the five time-trials, therefore, none of the two time-trials combinations were considered. The CP parameter can be accurately estimated from different two time-trial combinations, whereas none reached an acceptable level of accuracy for the determination of W′.

Retracted: Power Metering Automation System Based on Internet of Things

Retracted: Power Metering Automation System Based on Internet of Things

Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation

Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of energy systems and limit the usefulness of the data for decision-making and research. To address this issue, past studies have focused on imputing missing gaps in energy data, including random and continuous gaps. One of the main challenges in this area is the lack of validation on a benchmark dataset with various building and meter types, making it difficult to accurately evaluate the performance of different imputation methods. Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data. Contemporary image-inpainting methods, such as Partial Convolution (PConv), have been widely used in the computer vision domain and have demonstrated their effectiveness in dealing with complex missing patterns. Given that energy data often exhibits regular daily or weekly patterns, such methods could be leveraged to exploit the regularity of the data to learn underlying patterns and generate more accurate predictions for missing values. To study whether energy data imputation can benefit from the image-based deep learning method, this study compared PConv, Convolutional neural networks (CNNs), and weekly persistence method using one of the biggest publicly available whole building energy datasets, consisting of 1479 power meters worldwide, as the benchmark. The results show that, compared to the CNN with the raw time series (1D-CNN) and the weekly persistence method, neural network models with reshaped energy data with two dimensions reduced the Mean Squared Error (MSE) by 10% to 30%. The advanced deep learning method, Partial convolution (PConv), has further reduced the MSE by 20%–30% than 2D-CNN and stands out among all models. Based on these results, this study demonstrates the potential applicability of time-series imaging in imputing energy data. The proposed imputation model has also been tested on a benchmark dataset with a range of meter types and sources, demonstrating its generalizability to include additional accessible energy datasets. This offers a scalable and effective solution for filling in missing energy data in both academic and industrial contexts.