Power Monitoring Research Articles

Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining

In underground mines, physical layer security (PLS) technology is a promising method for the effective and secure communication to monitor the mining process. Therefore, in this paper, we investigate the PLS of an amplify-and-forward relay-aided system in power monitoring and communication systems for mining, with the consideration of multiple eavesdroppers. Explicitly, we propose a PLS scheme of cooperative jamming and precoding for a full-duplex system considering imperfect channel state information. To maximize the secrecy rate of the communications, an effective block coordinate descent algorithm is used to design the precoding and jamming matrix at both the source and the relay. Furthermore, the effectiveness and convergence of the proposed scheme with high channel state information uncertainty have been proven.

An improved ELM-WOA–based fault diagnosis for electric power

Due to its fast learning speed, the extreme learning machine (ELM) plays a very important role in the real-time monitoring of electric power. However, the initial weights and thresholds of the ELM are randomly selected, therefore it is difficult to achieve an optimal network performance; in addition, there is a lack of distance selection when detecting faults using artificial intelligence algorithms. To solve the abovementioned problem, we present a fault diagnosis method for microgrids on the basis of the whale algorithm optimization–extreme learning machine (WOA-ELM). First, the wavelet packet decomposition is used to analyze the three-phase fault voltage, and the energy entropy of the wavelet packet is calculated to form the eigenvector as the data sample; then, we use the original ELM model coupled with the theory of distance selection to locate faults and compared it with the SVM method; finally, the whale algorithm is used to optimize the input weight and hidden layer neuron threshold of the ELM, i.e., the WOA-ELM model, which solves the problem of the random initialization of the input weight and hidden layer neuron threshold that easily affects the network performance, further improves the learning speed and generalization ability of the network, and is conducive to the overall optimization. The results show that 1) the accuracy of selecting the data according to the fault distance is twice that of not selecting data according to it; 2) compared with the BP neural network, RBF neural network, and ELM, the fault diagnosis model based on the WOA-ELM has a faster learning speed, stronger generalization ability, and higher recognition accuracy; and 3) after optimization of the WOA, the WOA-ELM can improve 22.5% accuracy in fault detection when compared to the traditional ELM method. Our results are of great significance in improving the security of smart grid.

Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges

In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market sentiment, closing price and firm governance. Additionally, 15 indicators are identified for detecting manipulation in the power market, utilizing the perspectives of market power (also known as pricing power or market structure), market conduct and market performance. Finally, the study elaborates on the current challenges in the regulation of stock and power markets in terms of parameter performance, data availability and technical requirements.

Research on the Measurement Method of Feeding Rate in Silage Harvester Based on Components Power Data

For existing problems, such as the complex interactions between a crop and a machine, the measuring difficulty and the limited measurement precision of the feeding quantity within the corn silage harvester, a method of feeding rate measurement based on key conditions data, working data cleaning, and multiple variate regression is proposed. Non-destructive rotation speed, rotation torque, and power consumption sensors are designed for the key mechanical components. The data conditions, such as rotating speed, rotating torque, power consumption, hydraulic pressure, and hydraulic flow for the key operation of parts including cutting, feeding, shredding, and throwing are monitored and collected in real-time during field harvesting. The working data are screened and preprocessed, and the Mann-Kendall boundary extraction algorithm is applied, as is multiple component time lag correction analysis, and the Grubbs exception detection method. Based on a Pearson correlation analysis results, one-factor and multiple-factor regression models are respectively developed to achieve an accurate measurement of the corn feeding rate. The field validation tests show that the working data boundary extraction results among the load-stabilizing components such as shredding roller and throwing blower are highly reliable, with a correct rate of 100%. The power monitoring data of the shredding roller and throwing blowers are significantly correlated with the crop feeding rate, with a max correlation coefficient of 0.97. The determination coefficient of the single-factor feeding rate model based on the shredding roller reaches 0.94, and the maximum absolute error of the multi-factor feeding rate model is 0.58 kg/s. The maximum relative error is ±5.84%, providing technical and data support for the automatic measuring and intelligent tuning of the feeding quantity in a silage harvester.

Interdigitated electrodes enhanced photosensitive monolayer WS2 field effect transistor on glass substrate

Recently, 2D-material-based optoelectronic devices are integrated on optical-fiber as a substrate. However, silicon substrate, usually for optoelectronic device integration, impedes fiber-optic integration due to its much larger refractive index. Here, we fabricated a highly transparent photosensitive field effect transistor (FET) using monolayer WS2 on a fiber-compatible glass substrate with interdigitated electrodes. Experiments shows that the current switching ratio for the FET reach up to 105. Additionally, the interdigitated electrodes could improve photocurrent collection capability, leading to a relative high responsivity of 1.48 mA/W. Meanwhile, the difference in the photocurrent induced by light illumination from the front and back to the device is only 1.25%. This indicates light beam can pass through the device, facilitating online monitoring of optical power. Moreover, it is also shown that the transmitted light beam could be reflected to retransmit thought the device, thus increasing photocurrent by 6.8%. Such glass-substrate provide a simple and feasible strategy for future incorporation of optical-fiber with on-chip device.

AI-Assisted Failure Location Platform for Optical Network

In the paper, we applied the customized AI module to the OTDR device and, combined with the optical power monitoring module, realized the AI-assisted optical network fault location mechanism for the high-density interconnection scenario of data centers. The mechanism can make full use of the data from optical links. Based on the link data, the AI module can predict the links that may fail, and then the target links will be monitored by the optical power module. The mechanism can quickly locate and respond to faulty links. Through the test, the introduction of an AI model can improve the average fault detection efficiency of the link by 98.41%.

Construction and Test of Baler Feed Rate Detection Model Based on Power Monitoring

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection model of a baler based on power monitoring of the pickup platform. Through the dynamic analysis of the pickup platform, the functional relationship between the working power of the pickup platform and the feed rate was constructed. A power monitoring system of the pickup platform was developed, and the model construction experiment of the working power and the feed rate was performed. The influence mechanism of different running speeds on the torque noise signal of the power input shaft of the pickup platform was explored. The frequency of the noise signal was mainly concentrated at 0.5–6 Hz and 9–13 Hz employing a fast Fourier transform, and the noise signal was eliminated by the frequency-domain-filtering method. The function model of working power and feed rate of the pickup platform was established based on signal processing, and the determination coefficient R2 of the model was 0.9796. The field experiment results show that when the feed rate of the baler is between 1.6 and 4.88 kg/s, the determination coefficient R2 and RMSE between the actual and predicted feed rate are 0.989 and 0.2, respectively. The relative error range of feed-rate prediction is −9.37–8.77%, which indicates that the model has high detection accuracy and good stability and meets the requirements of feed-rate monitoring of a baler in field operation.

Multi-exposure electric power monitoring image fusion method without ghosting based on exposure fusion framework and color dissimilarity feature.

To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation.

Improved Energy Efficiency of Laser-Enhanced Nanoparticle Deposition System Analyzed with a Smart Power Monitoring Device

Improved Energy Efficiency of Laser-Enhanced Nanoparticle Deposition System Analyzed with a Smart Power Monitoring Device

Power monitoring in a feedforward photonic network using two output detectors.

Programmable feedforward photonic meshes of Mach-Zehnder interferometers are computational optical circuits that have many classical and quantum computing applications including machine learning, sensing, and telecommunications. Such devices can form the basis of energy-efficient photonic neural networks, which solve complex tasks using photonics-accelerated matrix multiplication on a chip, and which may require calibration and training mechanisms. Such training can benefit from internal optical power monitoring and physical gradient measurement for optimizing controllable phase shifts to maximize some task merit function. Here, we design and experimentally verify a new architecture capable of power monitoring any waveguide segment in a feedforward photonic circuit. Our scheme is experimentally realized by modulating phase shifters in a 6 × 6 triangular mesh silicon photonic chip, which can non-invasively (i.e., without any internal "power taps") resolve optical powers in a 3 × 3 triangular mesh based on response measurements in only two output detectors. We measure roughly 3% average error over 1000 trials in the presence of systematic manufacturing and environmental drift errors and verify scalability of our procedure to more modes via simulation.

Enhanced Methodology to Characterize 3-D Power Monitoring and Control Features for 5G NR Systems Embedding Multi-User MIMO Antennas

Enhanced Methodology to Characterize 3-D Power Monitoring and Control Features for 5G NR Systems Embedding Multi-User MIMO Antennas

Visual Analytics Platform for Centralized Covid-19 Digital Contact Tracing.

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

Data maps for material identification in helical milling by spindle power monitoring

Hole making on stacked aerospace materials is a major operation during aircraft assembly which poses significant challenges during manufacturing because of different material machinability. Strategies involving smart machining including adapting proper cutting conditions using real time monitoring can lead to significant improvements. This paper is a continuation of our research that uses data map methodology characterized by different specific force regions for workpiece material identification. In this article, spindle power is monitored utilizing CNC machine internal sensor during helical milling of Aluminium and Titanium alloys in a aerospace stack for estimating cutting coefficients. Our previous research addressed the material detection in circular milling of Aluminium and Titanium alloys independently using a force dynamometer. The result shows the applicability of data map technique consisting of axial force coefficients for material identification in helical milling and highlights the significance of stack sequence .

An end-to-end real-time pollutants spilling recognition in wastewater based on the IoT-ready SENSIPLUS platform

The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widespread manner. Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. The proposed system, organized in sensing and data processing modules, can recognize and distinguish contaminants from unknown substances typically present in wastewater. This is particularly important in the classification stage since distinguishing between background (not of interest) and foreground (of interest) substances drastically improves the classification performance, especially in terms of false positive rates. The measurement system, i.e., the sensing part, is represented by the so-called Smart Cable Water based on the SENSIPLUS chip, which integrates an array of sensors detecting various water-soluble substances through impedance spectroscopy. The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. The corresponding dataset has been made publicly available for download. The obtained results demonstrate an excellent classification ability, achieving an accuracy of more than 95% on average, and are a reliable “proof of concept” of a pervasive IoT system for distributed monitoring.

Design of Stray Radiation Sensor for ITER ECE Diagnostic

The Electron Cyclotron Emission (ECE) diagnostic has a primary role in the measurement of electron temperature profile and electron temperature fluctuations in ITER. This diagnostic shall be exposed to significant power due to unabsorbed Electron Cyclotron Heating (ECH) power in the plasma. The expected stray power loads could be a few tens of watts, and therefore, the protection of millimetre wave components is one of the design challenges of ITER ECE diagnostic. This protection system includes sensors, a band stop notch filter, and a shutter to stop the RF stray radiation from being incident on the sensitive components. The sensors will be positioned along the ECE transmission line, and shall be used for real-time power monitoring of the stray radiation. Here, we describe a novel design of a sensor for monitoring the stray radiation power. This sensor is a Schottky Diode rectenna, known for high-power and high-speed millimetre wave detection capability. It consists of a 2x2 microstrip patch antenna array, a matching circuit, a diode, and a low pass filter. The antenna array is designed analytically and optimized in CST Microwave Studio, for wide reception angle, high gain, and low side lobe levels. Furthermore, the rectifying circuit is optimized using Agilent Advanced Design System (ADS) software to get better rectification and impedance matching of the signal, thereby improving its detection sensitivity. The ADS simulation results show that the detection sensitivity is about 1000V/W for input power of -30 dBm at 170 GHz, thereby achieving the required performance of the sensor.

Nonintrusive Motor Current Signature Analysis

Motor current signature analysis (MCSA) techniques measure slip-related current signals in the supply lines of a squirrel-cage induction machine (SCIM) for slip estimation, fault detection, and motor diagnostics. In a traditional setup for MCSA, current sensors directly measure the machine currents and are usually installed physically close to the machine of interest, e.g., current transducers clamped about the machine stator leads. For grid-connected SCIMs, the slip-related current signals propagate through the power system. Thus, a single aggregate power monitor can potentially perform MCSA for a collection of SCIMs powered by a common electrical service, referred to in this article as nonintrusive MCSA. Current division and system impedances (source impedance, load impedances, and machine parameters) determine the applicability of nonintrusive MCSA. This article demonstrates measurement hardware, modeling, and experimental results for nonintrusive MCSA. Analysis and results investigate the current-source model of a grid-connected SCIM and its interaction with the system impedances. Applied to a distribution system with multiple machines, the techniques inform when nonintrusive MCSA is possible for collections of SCIMs and equivalent to traditional MCSA.

Design and implementation of real-time laser power monitoring system in laser ranging

Design and implementation of real-time laser power monitoring system in laser ranging

User Station Security Protection Method Based on Random Domain Name Detection and Active Defense

The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.

Development of IoT-Based Portable Power Quality Monitoring on Microgrids by Enhancing Protection Features

The need for small-scale renewable energy generation is predicted to increase. Distributed energy production, in general, can be more profitable due to the cost of distribution and use of energy storage, especially from the microgrid. However, the utility and consumers face difficulties maintaining demand imbalance, frequent load-shedding, and a drop in power quality. To address this issue, a portable power quality meter and protection based on the Internet of Things (IoT) for low-voltage distribution are proposed. The proposed prototype has several advantages. First, the procedure for implementing a portable IoT-based power meter with power quality and protection features for residential networks. Compared to other devices, this type has the advantages of power quality measurement, such as power factor, frequency, and harmonics. Second, an approach is proposed device with a parallel function in maintaining network security and quality. This increases the advantage of monitoring loads connected to the grid. Third, perform IoT power monitoring devices that provide notifications in real-time. Fourth, an experiment using a power meter on a microgrid connected to renewable energy combines a LiFePO4 battery and methanol to get the maximum benefit from green energy. The test results found that the IoT model can work reliably, where access to monitoring can be done via the website. The smart meter consists of a voltage transformer, current transformer, and microcontroller unit with an embedded communication module. The existence of more affordable monitoring and protection tools can increase the user’s opportunity to gain profitability.

Prototype of Electric Power Monitoring System and Load Control in Residential Houses Using IoT-Based ESP8266

The use of the Internet of Think (IoT) for a system that can monitor the use of load and electrical power in a residential house will be very necessary. This is because homeowners who often go out and leave their homes with loads that are still active, for example, house lights that are always turned on will be a waste of electrical energy. The purpose of making this article is to create a system that can control electricity use automatically using the Internet Of Think (IoT) and monitor the electricity data in real time, calculate the amount of current, voltage, and power in an entire load. Calculating the comparison of sensor and manual values by looking at the percentage of errors on the sensor, and analyzing whether the sensor is suitable for use or not. The systems used are: Pzem 004t as an overall reader of electrical power, LDR sensor as light input to turn on the lights automatically when it is dark, and nodemcu esp8266 as input/output process and sends data to the internet via a wifi network which will be monitored through the application. Blynk Iot 2.0 ABSTRAKPenggunaan Internet of Think (IoT) untuk sistem yang dapat memonitoring penggunaan beban dan daya listrik  pada sebuah rumah tinggal akan sangat diperlukan. Hal tersebut karena pemilik rumah yang sering berpergian keluar dan meninggalkan rumahnya dengan beban yang masih aktif contohnya lampu rumah yang selalu dihidupkan akan menjadi pemborosan energi listrik tersebut. Tujuan dari pembuatan artikel ini adalah yaitu membuat sistem yang dapat mengontrol penggunaan listrik secara otomatis menggunakan Internet Of Think (IoT) serta memonitoring data listrik tersebut secara realtime, menghitung jumlah arus, tegangan, dan daya pada suatu keseluruhan beban yang ada. Menghitung perbandingan nilai sensor dan manual dengan cara melihat persentase kesalahan pada sensor tersebut, dan menganalisa pada sensor tersebut layak digunakan atau tidak. Adapun sistem yang digunakan yaitu: Pzem 004t sebagai pembaca keseluruhan daya listrik, sensor LDR sebagai input cahaya untuk menghidupkan lampu secara otomatis ketika hari sudah gelap, dan nodemcu esp8266 sebagai proses input/ouput dan pengirim data ke internet melalui jaringan wifi yang akan dimonitoring lewat aplikasi Blynk Iot 2.0.