Distributed Monitoring System Research Articles

Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses.

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 μm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.

Estimation of Temperature Distribution in the Brain Using the Temperatures of Blood Inflow and Outflow for Brain Temperature Control

Brain cells can be damaged by higher temperature in the brain, and brain temperature distribution monitoring system is required from the perspective of brain preservation. Brain tissue can be further damaged by direct measurement of temperature using some sensors. Therefore, indirect methods of measuring temperature are required. In this study, we propose a method for estimating the average metabolic rate of the entire brain based on the difference between the observed and estimated blood flow temperatures that can be measured during selective brain hypothermia. Our mathematical simulator can calculate the brain temperature distribution based on the estimated average metabolic rate, taking into account other parameters like heat transfer, metabolic heat of individual tissues, heat‐washout by blood flow, and environmental temperature. The algorithm for estimating the metabolic rate of mathematical master model substituted as a human head is confirmed using the mathematical slave model on the simulator. It is possible to present the temperature distribution by estimating metabolic heat production in the brain, considering the equilibrium between heat wash‐out by the blood flow and heat transfer. In addition, an appropriate initial value of brain temperature enables the accurate temperature management enough for clinical use even in the process of temperature estimation. The development of our proposed method will make it possible to construct a non‐invasive monitoring system that presents the estimated temperature distribution in the brain instead of the conventional invasive method. The new system is expected to be utilized in a various treatments related to some brain disorders. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

Advance dry and wet waste distribution monitoring and tracking system

Advance dry and wet waste distribution monitoring and tracking system

Smart distribution network voltage estimation using PMU technology considering zero injection constraints.

To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.

Research on distributed strain monitoring of a bridge based on a strained optical cable with weak fiber Bragg grating array.

The foundation of an intelligent highway network is the construction of a high-density distributed strain monitoring system, which is based on sensing elements that can sensitively capture external information. In this research, the development and application for the structure of a novel strained optical fiber cable based on the weak fiber Bragg grating (wFBG) arrays are discussed. A modulation and demodulation solution of wavelength division multiplexing combined with time division multiplexing is developed by utilizing the property by which the wavelength of the strained optical fiber cable is periodically switched. Further, the strain transfer model of the optical cable is analyzed hierarchically using the theory of elasticity. The strain transfer coefficients of the overhanging region and the gluing region are combined to deduce the sensitivity model of the strained optical fiber cable. Moreover, the finite element technique is integrated to optimize the structural parameters of the optical cable for high-sensitivity or large-scale range. The strained optical fiber cable based on wFBG arrays is applied to a steel-concrete composite bridge. The static and dynamic loading tests show that the sensing optical cable can be monitored for strain variation in order to realize the functions of lane identification, weighing vehicle tonnage as well as velocity discrimination.

Designing the architecture of a distributed system for information monitoring of IoT and IIoT infrastructures traffic

The article considers an approach to designing the architecture of a distributed information monitoring and quality management system for communication services provided by the Internet of Things and Industrial Internet of Things infrastructures based on solutions supporting machine-to-machine and human-machine interaction. The choice of a neural network with long Short-Term memory (LSTM) is justified as a theoretical basis for modeling the active node of an information monitoring system. The developed structure of the LSTM network is presented, taking into account the spatial and temporal correlation of the obtained indicators at various observation points of the distributed information monitoring system. The criteria for evaluating the effectiveness of the proposed model for the conditions of information monitoring of non-stationary load in a mode close to real time are substantiated.

Real-Time Data Acquisition and Monitoring of Three Phase Load Balance in Electricity Distribution

A real-time monitoring mechanism for three-phase electrical power distribution panels is essentially needed to ensure that the power is evenly distributed across all phases, so that the efficiency of energy usage could be maintained. This paper presents monitoring and data acquisition system for three-phase balance distribution. The system ensures balanced distribution of electrical power in order to enhance the efficiency and reliability of power systems. It leverages advanced technologies to collect, analyze, and report data, providing valuable insights into the performance of the power distribution network. Data acquisition is established by using sensor PZEM004T to measure the electricity parameters which is integrated with Arduino microcontroller. In addition, real-time monitoring system is visualized through dashboard data management for more informative data acquisition and analytic purposes. The performance is evaluated by testing the instrument and application at a distribution panel within a span of five days to monitor electrical parameters. The results show that real-time monitoring system for three phase balance works perfectly to provide actual data and phase balance condition. The phase imbalance values are higher during the peak working hours where the loads are dynamics and extensively used.

Experimental realization on stress distribution monitoring during 3D concrete printing

Stress distribution in 3D printed concrete (3DPC) plays a crucial role in monitoring filament deformation and buildability evaluation. However, real-time detection methods for assessing stress distribution are currently lacking. This study proposes a novel stress distribution monitoring system for 3DPC, which enables the visual display of the two-dimensional visual stress distribution of printed samples. By analyzing the stress distribution map provided by the system, an uneven distribution of stress and an increase of 95%-163% in cumulative stress were observed during the printing process. In addition, a 25% reduction in filament deformation by lateral constraints was also observed, where the accuracy of the monitoring system was further verified by studying the deformation of samples with varying filament amounts per layer and lateral constraint conditions, thereby offering new insights for predicting the buildability of 3DPC.

Dynamic Queries through Augmented Reality for Intelligent Video Systems

Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is to obtain information from large areas, it can become complicated if it is necessary to track an object of interest, such as people or vehicles, due to the vision space that a single camera can cover; this opens the way to distributed zone monitoring systems made up of a set of cameras that aim to cover a larger area. Distributed zone monitoring systems add great versatility, becoming more complex in terms of the complexity of information analysis, communication, interoperability, and heterogeneity in the interpretation of information. In the literature, the development of distributed schemes has focused on representing data communication and sharing challenges. Currently, there are no specific criteria for information exchange and analysis in a distributed system; hence, different models and architectures have been proposed. In this work, the authors present a framework to provide homogeneity in a distributed monitoring system. The information is obtained from different cameras, where a global reference system is defined for generated trajectories, which are mapped independently of the model used to obtain the dynamics of the movement of people within the vision area of a distributed system, thus allowing for its use in works where there is a large amount of information from heterogeneous sources. Furthermore, we propose a novel similarity metric that allows for information queries from heterogeneous sources. Finally, to evaluate the proposed performance, the authors developed several distributed query applications in an augmented reality system based on realistic environments and historical data retrieval using a client–server model.

Multi-Objective Optimization Driven Application of Micro Phasor Measurement Units for Efficient Monitoring of Power Distribution System

Multi-Objective Optimization Driven Application of Micro Phasor Measurement Units for Efficient Monitoring of Power Distribution System

Water Quality Assessment of Lower Usuma Dam Water from Treatment plant to points of Consumption in Federal Capital Territory, Nigeria

This study investigated and compared the bacteriological, physicochemical and heavy metals concentrations of drinking water quality of Lower Usuma Dam from treatment plant to the points of consumption in three Area Councils (Bwari, Abuja Municipal and Gwagwalada) of the Federal Capital Territory, Abuja, Nigeria. Most probable number technique was employed for coliform detection. Molecular characterization of isolated bacteria was carried out. Seven out of the 26 water samples analyzed in this study were contaminated in the range of 7 MPN/100ml to >23 MPN/100ml. Physicochemical parameters such as colour, pH, temperature, turbidity, total dissolved solids (TDS), total hardness, electrical conductivity, total alkalinity and seven heavy metals were also determined using Atomic absorption spectrophotometry. Results showed that Bwari had the highest value for colour, 16.67 Pt.Co while AMAC had the least with 1.00 Pt. Co, Gwagwalada had the highest pH value (7.33±0.12) and Bwari the lowest pH of 6.77±0.06. Bwari also had highest temperature of 30.23±2.80oC while AMAC recorded the least (27.29±0.26oC). Turbidity ranged from 1.86 to 2.83 NTU. The highest level of the other parameters [(TDS 54.27 mg/L, conductivity 90.10 µs/cm, total hardness 34.00 mg/L, total alkalinity 28.00 mg/L, Cl– 20.82 mg/L)] was observed in water samples from Gwagwalada Area Council. Gwagwalada recorded the highest values for iron and zinc at 0.09mg/l and 0.003mg/l respectively. Copper concentration was highest in AMAC (0.043mg/l) and lowest in Bwari area council (0.007mg/l). There was no significant difference (p>0.05) in the concentration of Cr detected in all the water samples. There is need for provision of adequate sanitation facilities and improvement on monitoring of distribution system in order to maintain drinking water quality and prevent water borne disease outbreaks.

A novel distributing–collecting on-line insulation monitoring system and line selection technology for the DC supply system

At present, the power supply system of 5G base stations is a micro smart grid, it generally uses 240 V DC power supply with multiple branches, and leakage accidents will threaten personal and property safety, so it is vital to identify the fault line accurately and remove the faults rapidly. In this paper, the leakage phenomenon of transmission lines in the HVDC power supply system of a 5G communication base station is studied. To address the issue of multi-branch line leakage diagnosis and line selection in the 240 V DC system, a new distributed DC insulation monitoring and fault line selection technology and system are proposed. The distributed DC insulation monitoring line selection technology is used to collect the leakage current of each branch by setting a unique single-core DC leakage transformer and process the signal primarily. In addition, a high-resistance bridge switchgear is added to the bus bar. According to different leakage currents caused by the imbalance of the bridge before and after the switch, the insulation resistance of electrodes to the ground is calculated accurately, which solves the problem that the current technology cannot judge the fault of the positive and negative electrodes to the ground at the same time. Through both simulation and prototype experiments, the feasibility of this technology and system device in line insulation monitoring and line selection of the HVDC communication base station power supply system is verified.

Intelligent Prediction and Real-time Monitoring System for Gas Flow Distribution at the Top of Blast Furnace

Intelligent Prediction and Real-time Monitoring System for Gas Flow Distribution at the Top of Blast Furnace

Flow forecasting for leakage burst prediction in water distribution systems using long short-term memory neural networks and Kalman filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset of over 2,000 DMAs in North Yorkshire, UK, containing flow time series recorded at 15-minute intervals for a period of one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are cross referenced with entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in water Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filtering. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decision-making for any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakages and disruptions. The FLUIDS framework is statistically assessed and compared against the state-of-practice minimum night flow (MNF) methodology. Based on the statistical analyses, it is concluded that the proposed framework performs well on the unobserved test dataset for both regular and leakage water flows.

Radio frequency based distributed system for noncooperative UAV classification and positioning

With the increasing popularity of civilian unmanned aerial vehicles (UAVs), safety issues arising from unsafe operations and terrorist activities have received growing attention. To address this problem, an accurate classification and positioning system is needed. Considering that UAVs usually use radio frequency (RF) signals for video transmission, in this paper, we design a passive distributed monitoring system that can classify and locate UAVs according to their RF signals. Specifically, three passive receivers are arranged in different locations to receive RF signals. Due to the noncooperation between a UAV and receivers, it is necessary to detect whether there is a UAV signal from the received signals. Hence, convolutional neural network (CNN) is proposed to not only detect the presence of the UAV, but also classify its type. After the UAV signal is detected, the time difference of arrival (TDOA) of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference. Finally, the Chan algorithm is used to calculate the location of the UAV. We deploy a distributed system constructed by three software defined radio (SDR) receivers on the campus playground, and conduct extensive experiments in a real wireless environment. The experimental results have successfully validated the proposed system.

Machine-Learning-Based Health Monitoring and Leakage Management of Water Distribution Systems

The reduction of pipe leakage is one of the top priorities for water companies, with many investing in greater sensor coverage to improve the forecasting of flow and detection of leaks. The majority of research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), with the aim of identifying bursts after occurrence. This study is a step towards development of ‘self-healing’ water infrastructure systems. In particular, the concepts of machine-learning (ML) and deep-learning (DL) are applied to the forecasting of water flow in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset for ~2500 DMAs in Yorkshire, containing flow time-series recorded at every 15-minute interval over the period of a year. Firstly, the method of isolation forests is used to identify anomalies in the dataset which are verified as corresponding to entries in water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid framework of DL models - such as recurrent neural networks (RNNs) and transformer neural networks) - and state-space ML algorithms - such as Kalman filter and autoregressive integrated moving average (ARIMA). The ML algorithms are trained to forecast the stationary component of the expected flow patterns in real-time, which is then combined (through Bayesian updating) with the non-stationary component obtained from DL models. As well as providing expected day-to-day flow demands, this framework aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decisions to be made regarding any necessary interventions. This can inform targeted repair strategies which best utilise resources to minimise leakage and disruptions by addressing both detected and predicted burst events.

Anomaly Detection in Microservice-Based Systems

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.

Smarthub for supervising system for resource exploration and pollution control in deep-water and coastal areas based on ICT technologies

PurposeThis study aims to explore the need for highly technological complexes for control and monitoring, as well as, new concepts and methodologies for maritime resource exploration and exploitation, which are in great demand nowadays.Design/methodology/approachThis paper provides an analysis of demand, means of creation and development of the methodology and infrastructure for global monitoring, pollution control and supervision of smart systems for activities in exploration, future resource exploitation in deep-water and coastal areas based on Smarthub architecture, Unmanned Aircraft System (UAS), Continuous Acquisition and Life-Cycle Support (CALS) and Blockchain technologies.FindingsObservational, experimental, simulation, derivational, hybrid descriptive and analytical models, as well as, surrogate models were created, analyzed and implemented for assigned tasks realization. Concept of distributed system for marine environmental monitoring, control and supervising as pilot technology in the context of Technology Readiness Levels (TRL) 3–5 was designed and evaluated.Originality/valueThe activities described in this article should be realized in the design and development of a complex, reliable, robust and sustainable monitoring and inspection system for the control and evaluation of the impact and risk assessment of the exploration and future exploitation of maritime resources.

Optimal location of micro‐phasor measurement units in distribution system control, monitoring, and protection using hybrid technique

AbstractThis manuscript proposes a hybrid technique for the optimum location of micro‐phasor measurement units (μ‐PMUs) in the distribution system (DS).The proposed hybrid approach is the wrapper of dwarf mongoose optimizer (DMO) and pelican optimization algorithm (POA). The updating behavior of DMO is improved by the POA; therefore, it is called DMO‐POA approach. The main aim of the proposed approach is to avoid discontinuity of supply and minimizes the count of PMDs to reduce cost and attain the maximal count of measurement‐redundancy that is limited to full‐system observability for IEEE bus‐system in zero injection nodes (ZINs) and tie‐switches. The proposed approach considered the optimum count of μ‐PMUs is needed for the thorough distribution system economy, observability, and the reliability measure. The proposed approach considers the distribution of PMUs in the reconfiguration of smart‐distribution‐grids, ZINs and communication system requirements. Here, the objective‐function is aiming at minimizing the costs of reliability and capital. The proposed method uses the infrastructure of communication cost to simultaneously co‐optimize the system switching strategy and find the optimal sites for PMUs. The performance of the proposed technique is tested in different test‐systems, like IEEE‐14 bus, 13, 34, 37, and 123 buses and the performance is executed by comparing it with the existing approaches. Furthermore, the proposed system is effectual to find an optimal solution with less computation and decreases the complexity of the algorithm.