Wide Area Monitoring Research Articles

High-Precision Analysis Using μPMU Data for Smart Substations

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships.

SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters.

Assessing the phenological state of evergreen conifers using hyperspectral imaging time series

Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: Thuja occidentalis, Platycladus orientalis, Pinus sylvestris and P. nigra subsp. pallasiana. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R2 exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.

Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4 %), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes. Data availabilityThe data used for this paper are available at: https://zenodo.org/records/12549244. The code created for this paper are available at: https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020.

Fully autonomous water monitoring by plant-inspired robots

Developing countries struggle with water quality management owing to poor infrastructure, limited expertise, and financial constraints. Traditional water testing, relying on periodic site visits and manual sampling, is impractical for continuous wide-area monitoring and fails to detect sudden heavy metal contamination. To address this, plant-inspired robots capable of fully autonomous water quality monitoring are proposed. Constructed from paper, the robot absorbs surrounding water through its roots. This paper robot is controlled by paper-based microfluidic logic that sends absorbed water to petal-shaped actuators only when the water is polluted by heavy metals. This triggers the actuators to swell and bend like a blooming flower, visually signaling contamination to local residents. In tests with copper-contaminated water, the robotic flower’s diameter increased from 4.69 cm to 14.89 cm, a more than threefold expansion (217.25 %). This significant blooming movement serves as a highly visible and easily recognizable indicator of water pollution, even for the public. Furthermore, the paper robot can be mass-produced at a low cost (∼$0.2 per unit) and deployed over large areas. Once installed, the paper robot operates autonomously using surrounding water as a power source, eliminating the need for external electrical infrastructure and expert intervention. Therefore, this autonomous robot offers a new approach to water quality monitoring suitable for resource-limited environments, such as Sub-Saharan Africa.

Non-Tectonic Geohazards of Guangdong Province, China, Monitored Using Sentinel-1A/B from 2015 to 2022.

Guangdong Province, home to 21 cities and a permanent population of 127.06 million people, boasts the largest provincial economy in China, contributing 11.76% to the national GDP in 2023. However, it is prone to geological hazards due to its geological conditions, extreme weather, and extensive human activities. Geohazards not only endanger lives but also hinder regional economic development. Monitoring surface deformation regularly can promptly detect geological hazards and allow for effective mitigation strategies. Traditional ground subsidence monitoring methods are insufficient for comprehensive surveys and rapid monitoring of geological hazards in the whole province. Interferometric Synthetic Aperture Radar (InSAR) technology using satellite images can achieve wide-area geohazard monitoring. However, current geological hazard monitoring in Guangdong Province based on InSAR technology lacks regional analysis and statistics of surface deformation across the entire province. Furthermore, such monitoring fails to analyze the spatial-temporal characteristics of surface deformation and disaster evolution mechanisms by considering the local geological features. To address these issues, current work utilizes Sentinel-1A/B satellite data covering Guangdong Province from 2015 to 2022 to obtain the wide-area surface deformation in the whole province using the multi-temporal (MT) InSAR technology. Based on the deformation results, a wide-area deformation region automatic identification method is used to identify the surface deformation regions and count the deformation area in each city of Guangdong Province. By analyzing the results, we obtained the following findings: (1) Using the automatic identification algorithm we identified 2394 deformation regions. (2) Surface subsidence is concentrated in the delta regions and reclamation areas; over a 4 cm/year subsidence rate is observed in the hilly regions of northern Guangdong, particularly in mining areas. (3) Surface deformation is closely related to geological structures and human activities. (4) Sentinel-1 satellite C-band imagery is highly effective for wide-area geological hazard monitoring, but has limitations in monitoring small-area geological hazards. In the future, combining the high-spatial-temporal-resolution L-band imagery from the NISAR satellite with Sentinel-1 imagery will allow for comprehensive monitoring and early warning of geological hazards, achieving multiple geometric and platform perspectives for geological hazard monitoring and management in Guangdong Province. The findings of this study have significant reference value for the monitoring and management of geological disasters in Guangdong Province.

LiVeR: Lightweight Vehicle Detection and Classification in Real-Time

Detection of vehicles and their classification is a significant component of wide-area monitoring and surveillance, as well as intelligent- transportation . Existing solutions tend to employ heavy-weight infrastructure and costly equipment, as well as largely depend on constant support from the cloud through round-the-clock internet connectivity and uninterrupted power supply. Moreover, existing works mainly concentrate on localized measurement and do not discuss their efficient integration to address the problem over a wide area. For practical use in an outdoor environment, apart from being technically sound and accurate, a solution also needs to be cost-effective , lightweight , easy to install , flexible , low overhead , and easily maintainable , as well as self-sufficient as much as possible. However, fulfilling all these goals together is a challenging task. In this work, we propose an IoT-assisted strategy, LiVeR , to accomplish it. For self-sufficient on-the-fly classification in resource-constrained low-power IoT devices, LiVeR minimizes not only the computational requirements but also the energy consumption, which enables sustained operation in a hostile outdoor environment for a considerably long time solely based on battery power. Through extensive studies based on outdoor measurement and trace-based simulation on empirical data, we demonstrate that LiVeR classifies vehicles of small, medium, and large size with an accuracy of 91.3% up to 98.8%, 92.3% up to 98.5%, and 93.8% up to 98.8%, respectively, for single-lane traffic. We also demonstrate that LiVeR spends only about one-third of the number of RF packets to achieve vehicle detection and classification compared to the state-of-the-art RF-based solution, considerably extending the lifetime of the system.

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to -70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

A Priori Estimation of Radar Satellite Interferometry’s Sensitivity for Landslide Monitoring in the Italian Emilia-Romagna Region

The InSAR technique is known to be a powerful tool for precise monitoring of wide areas in terms of displacements. It is conceivable to also use this technique to monitor landslide areas, but geometrical distortions due to ground morphology and land cover could make InSAR processing ineffective for such applications. Because of the computational burden of InSAR processing, it is important to have preliminary knowledge about the possible suitability of the technique for the inspected area before acquiring and processing the data. This paper aims to perform a preliminary analysis of the InSAR sensitivity for the specific case of landslide monitoring. A new approach is proposed considering aspects specific to landslide displacements, which are basically tangent to the slope direction. Pre-processed coherence maps were used to account for the impact of land cover. The whole analysis can be carried out without acquiring cumbersome SAR datasets and can be used as a preliminary step. The Italian Emilia-Romagna region has been considered as the study area, with landslide areas accounting for more than 12% of its territory. The outcomes show that the inspected area has favourable morphological conditions, mainly thanks to its mild slopes and the limited number of landslides facing north, but the land cover has a strong negative impact on the InSAR sensitivity. Nevertheless, 7.5% of the landslide areas have promising conditions for monitoring using radar interferometry.

Comparison of anomalous displacement detectability of bridges between X and C band InSAR data: Case study on a collapse of water pipe bridge

This paper presents a feasibility of detecting anomalous displacements on bridges where collapses have occurred using C-band satellite synthetic aperture radar (SAR). Recently, the number of aging bridges due to damage and corrosion has been increased in many countries. Although there are various maintenance and management systems, the authors have focused on satellite SAR, one of the remote sensing technologies, to establish a wide-area multiple bridge monitoring technology. In a previous study, the authors analyzed high-resolution X-band interferometric SAR (InSAR) data acquired by the Italian satellite COSMO-SkyMed during the two years before the accident for the MUSOTA water pipe bridge in Japan, which collapsed on October 2021. As a result, it was shown that there was an anomalous displacement about one year before the accident, which seemed to be a sign of the collapse. On the other hand, the utilization of Sentinel-1 (C-band) data, which has lower resolution than X-band data, but is freely available, covers a wide area, and has dense time-series SAR images, would be beneficial for the practical application of this technology. In this study, InSAR data acquired by Sentinel-1 for the MUSOTA water pipe bridge from 2016 to 2022 are analyzed. Then, we compare the line-of-sight (LOS) displacement anomaly before the collapse with the results analyzed by the X-band satellite. As a result of statistical analysis of the displacement between the collapsed span and the adjacent spans with similar structural type assumed to be intact, it is shown that Sentinel-1 also captured differences which seemed to be the signs of the collapse about one year before the accident. This result supports the analysis of the X-band satellite data and shows potential for utilization in bridge anomaly detection systems, even for low-resolution C-band data.

Wide-Area Subsidence Monitoring and Analysis Using Time-Series InSAR Technology: A Case Study of the Turpan Basin

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Exploring the abundance and characteristics of litter in Lithuanian riversides: a citizen science approach.

To gain a better understanding of the sources and ecological effects of plastic contamination in Lithuanian rivers, as well as to provide guidance for mitigation, monitoring is necessary. This is a logistically and financially demanding endeavor, particularly in the case of microplastics. Citizen science provides a viable option for sampling sites that are accessible, thus enabling the monitoring of wide areas. In Lithuania, a citizen science approach was employed, with schoolchildren examining litter at riversides and identifying potential sources at 24 sampling sites in Autumn 2022 and 32 in Spring 2023, covering both large and small rivers. The maximum amount of 220 items per location was registered in Autumn 2022, whereas 111 items per location were registered in Spring 2023. The two main types of microlitter discovered were plastic (34-42%) and cigarette butts (17-22%), with glass, paper, and metal also present, which suggests that recreational visitors are the main source of litter. By K-means clustering analysis, all locations were divided into four clusters according to litter composition. To sum up, the findings of this study illustrate the importance of citizen science in providing insight into the contamination of Lithuanian rivers, which can be used to inform the development of conservation strategies.

Calculating Global Minimum Points to Binary Polynomial Optimization Problem: Optimizing the Optimal PMU Localization Problem as a Case-Study

State estimation (SE) is an algorithmic function of an energy management system (EMS). SE provides an actual-time monitoring and control of modern electrical power grids. State Estimation can be worked with sufficiency using Phasor Measurement Units optimally placed within a power grid. This paper concerns the implementation of proper algorithms embedded in optimization solvers to the optimal PMU localization problem solving globally. The optimization model is formulated as a 0 - 1 nonlinear minimization problem. The problem is transformed to a polyhedron using linearization methods and B&B tree. In this model, we use a linear cost function under polynomial constraints and binary restrictions on the design variables in a symbolic format. This mathematical model is programmed in the YALMIP environment which is fully compatible with MATLAB. The 0 - 1 Nonlinear Programming (NLP) model is suitable for getting concisely global optimal solutions. The optimal solution is given by a wrapped optimization engine including a local optimizer routine performing together with a mixed-Integer-Linear Programming routine. The solution is achieved within a zero-gap precisely encountered during the iterative process. This tolerance criterion is a necessity for a successful implementation of the B&B tree because it ensures global optimality with an acceptance relative gap. The minimization model is implemented in a YALMIP code fully compatible with MATLAB in two stages. Initially, an objective function with one term is minimized to discover a number of sensors for wide-area monitoring, control and state estimator applications. Then, an extra product is considered in the objective to suffice maximum reliability for observing the network buses. The numerical minimization models are applied to standard power networks in the direction to be solved globally.

Recent Phenomenal and Investigational Subsurface Landslide Monitoring Techniques: A Mixed Review

Landslides are a common and challenging geohazard that may be caused by earthquakes, rainfall, or manmade activity. Various monitoring strategies are used in order to safeguard populations at risk from landslides. This task frequently depends on the utilization of remote sensing methods, which include the observation of Earth from space, laser scanning, and ground-based interferometry. In recent years, there have been notable advancements in technologies utilized for monitoring landslides. The literature lacks a comprehensive study of subsurface monitoring systems using a mixed review approach that combines systematic and scientometric methods. In this study, scientometric and systematic analysis was used to perform a mixed review. An in-depth analysis of existing research on landslide-monitoring techniques was conducted. Surface-monitoring methods for large-scale landslides are given first. Next, local-scale landslide subsurface monitoring methods (movement, forces and stresses, water, temperature, and warning signs) were examined. Next, data-gathering techniques are shown. Finally, the physical modeling and prototype field systems are highlighted. Consequently, key findings about landslide monitoring are reviewed. While the monitoring technique selection is mainly controlled by the initial conditions of the case study, the superior monitoring technique is determined by the measurement accuracy, spatiotemporal resolution, measuring range, cost, durability, and applicability for field deployment. Finally, research suggestions are proposed, where developing a superior distributed subsurface monitoring system for wide-area monitoring is still challenging. Interpolating the complex nonlinear relationship between subsurface monitoring readings is a clear gap to overcome. Warning sign systems are still under development.

Measurement Platform for Latency Characterization of Wide Area Monitoring, Protection and Control Systems

Measurement Platform for Latency Characterization of Wide Area Monitoring, Protection and Control Systems

Long range acoustic detection of gas bubbles in a shallow water coastal environment

As the world economy moves to net zero carbon dioxide (CO2) emissions, carbon capture and storage (CCS) is recognised a mitigation mechanism in the suite of technology options. Liquified CO2 injection into offshore subsea reservoirs will be one of the CCS strategies used. Despite being improbable, CO2 leaks could occur and impact the effectiveness of this CO2 mitigation option and undermine confidence in its use as part of the suite of tools to combat climate change. To satisfy public safety expectations and environmental regulatory requirements, long-term measurement, monitoring, and verification (MM&V) plans that are affordable will be needed as part of any commercial CCS operation. Active acoustic methods are a well-suited technique as they can detect gas bubbles in the water column at extremely low flow rates. A bespoke detection system, the Echosounder Detection of Gas Events (EDGE) was developed with the goals of long-duration monitoring and detection of low flow gas bubble releases at long ranges; the literature suggests flow rates of <2 L min−1 will be readily detectable using active acoustics, well below the upper limits of a release of 1 % of stored CO2 over a 1000 year timeframe proposed by IPCC (2005). The EDGE system was housed on a seafloor lander with a battery powered scientific echosounder connected to a narrow-beam horizontally orientated transducer mounted on a turret that rotates through 360 deg. The effectiveness of the EDGE system was tested in a range of weather conditions during two six-month deployments in ∼ 20 m depth at a proposed CCS storage site, CarbonNet, situated off the coast of Victoria, Australia. Separate experiments with controlled gas releases were conducted at a local site at Hobart, Australia to establish the detection capability at range for low flow rates. These experiments found very small gas-releases (< 2 L min−1) could be detected at ranges of up to 378 m. The ability of long-term deployment of the EDGE at the proposed CCS site and the local experiments establishing range-detection ability have demonstrated that active acoustic systems can meet the dual objectives of sustained and wide-area monitoring as a key part of an aquatic based MM&V framework.

Development and implementation of a MATLAB-based phasor data concentrator for synchrophasor applications

This work presents the development of MatPDC, an IEEE Std. C37.118.2-2011 compliant Phasor Data Concentrator (PDC) implemented in the Matlab environment. MatPDC enables the integration of wide-area monitoring, protection and control system synchrophasor data into the Matlab platform, offering real-time access to synchronized data streams from multiple Phasor Measurement Units (PMUs). The key features of MatPDC include real-time data access, data integrity verification, data aggregation and alignment, compliant data communication, and latency calculation. These features facilitate efficient processing and analysis of synchronized data, reducing complexity and improving latency between data sources and applications. The development of MatPDC addresses the need for accessing and analyzing real-time synchrophasor data within the Matlab environment, providing researchers and engineers with a powerful tool for power system analysis and experimentation. By developing the PDC and synchrophasor applications on the same platform, MatPDC reduces complexity and latency between data sources and applications. It offers real-time access to synchronized data streams, ensuring data integrity through verification mechanisms and aggregating the data into a unified dataset based on time tags. The implementation of MatPDC opens up opportunities for researchers to work with real-time synchrophasor data. It facilitates the development of advanced algorithms, real-time simulations, and the verification of control strategies. The integration and evaluation of MatPDC demonstrate its effectiveness and potential in power system analysis, providing researchers and engineers with a valuable tool for their research and development activities.

Frontiers of NIR Imaging

After a brief introduction to the technique, this review will discuss the state-of-the-art NIR imaging instrumentation and its applications, including applications of an ordinary NIR imaging system to solvent diffusion into a polymer. Other imaging applications discussed include the use of a portable NIR imaging system in the pharmaceutical industry, high-speed and wide-area monitoring of polymers, and imaging for fish embryos development research. Also discussed is an imaging-type two-dimensional Fourier spectroscopy (ITFS) system and its application to medaka (Japanese rice fish, Oryzias latipes) egg development. Finally, general perspectives on NIR imaging will be provided.

A cyber-layer based on weighted average consensus in blockchain environment for accurate sharing of power systems’ dynamic states

Sharing the dynamic states among power system generators is crucial for wide-area monitoring, decentralized control, and protection of the prospective smart grid. However, this sharing process is subject to cyber–physical contingency conditions, which limits its accuracy. This paper proposes a framework that incorporates a cyber-layer with the power system to ensure accurate sharing of the generators’ dynamic states. The proposed framework utilizes a weighted average consensus mechanism within a blockchain environment. The consensus ensures that the communication links/data among generator buses are reliable in both directions and that any link/data failure is detected to guarantee the validity, and hence accuracy of the shared generators’ states. Moreover, a smart contract at each generator applies the proposed consensus mechanism and credits the validator generator bus that creates the next block in the states’ blockchain after achieving the consensus. Finally, the proposed framework is tested on the standard IEEE 3-generator 9-bus power system under data contingency conditions. The generators’ speed states are tested, while the other states can be verified in a similar manner. Simulation results show the ability of the proposed framework to ensure accurate sharing of the generators’ speed states and to detect possible cyber-attacks, compared to the case without using the proposed framework.