Search Results (28)

In intelligent meter reading and other applications, power line communication can use relay technology to solve the problem of cross-station or long-distance reliable communication. This study investigates the combined impact of the physical and Media Access Control (MAC) layers on power line relay communication system performance. To this end, cross-layer modeling, optimization, and simulation analysis integrating both layers are conducted. Based on the CSMA algorithm of IEEE 1901 protocol, a cross-layer performance analysis model of two-hop relay power line communication system is established considering the influence of non-ideal channel transmission at physical layer and competitive access at MAC layer on system performance. In order to reduce the high collision probability caused by two competitions of packets in the above scheme, an improved two-hop transmission algorithm based on CSMA-TDMA is proposed. The cross-layer performance of the system under different single-hop and two-hop schemes is compared, and the mechanism of how parameters such as the MAC layer and the physical layer affect the cross-layer performance of the power line communication system is analyzed. And the optimal power allocation factor is obtained by using the sequential quadratic programming method for the joint system throughput and packet loss rate optimization model with the two-hop power constraint. Simulation results show that the two-hop transmission scheme based on CSMA-TDMA can avoid the second-hop competition and backoff process, and has better performance in terms of throughput, packet loss rate, and delay. Full article

In high-voltage direct-current (HVDC) transmission and large-scale power-system operation, DC-bias effects can drive current-transformer (CT) cores into premature saturation, distorting the secondary current and seriously jeopardizing the reliability of protective relaying and metering. To address the limited fitting capability and robustness of conventional compensation approaches in the presence of nonlinear distortion, this paper proposes a convolutional neural network (CNN)-based inversion method for CT saturation current. First, a simulation model is built on the PSCAD/EMTDC platform to generate a dataset of saturated, distorted currents covering DC components from −50 A to +50 A. Then, a CNN with a three-layer one-dimensional convolutional architecture is designed; leveraging local convolutions and parameter sharing, it extracts features from current sequences and reconstructs the true primary current. Simulation results show that the proposed method accurately recovers the primary-current waveform under mild-to-severe saturation, with errors within 2%, and exhibits strong adaptability and robustness with respect to both the polarity and magnitude of the DC component. These findings verify the effectiveness of CNNs for CT-saturation compensation. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

Unmanned aerial vehicles (UAVs) offer solutions to issues like traffic congestion and labor shortages. We developed a distributed UAV management system inspired by virtual circuit and datagram methods in packet-switching networks. By installing houses with wireless terminals, UAVs navigate routes in a multi-hop network, communicating with ground nodes. UAVs are treated as network packets, ground devices are treated as routers, and their connections are treated as links. Activating all nodes as relays increases control message traffic and node load. To optimize connectivity, we minimize relay nodes, connecting non-relay nodes to the nearest relay. This study proposes four relay node selection methods: random selection, two adjacency-based methods, and our innovative approach using Multipoint Relay (MPR) from the Optimized Link State Routing Protocol (OLSR). We evaluated these methods according to their route construction success rates, relay node counts, route lengths, and so on. The MPR-based method proved most effective for UAV route construction. However, fewer relay nodes increase link collisions, and we identify the minimum relay density needed to balance efficiency and conflict reduction. Full article

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments. Full article

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. Full article

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article

The Matsu Islands Tern Refuge comprises eight reefs located at a relay station on the East Asian bird migration route, and it attracts many transiting, wintering, or breeding birds to inhabit and live on the reefs every year. In order to understand the compositions of plant communities as a breeding habitat for seabirds, we investigated the plant communities of the eight reefs. A total of 130 plots of 10 × 10 square meters were established, from which we found 107 species of plants in 102 genera and 51 families. Among this, we found one critically endangered (CR) species, four vulnerable (VU) species, and three near-threatened (NT) species. The result of two-way indicator species analysis (TWINSPAN) and indicator value (IndVal) showed 130 samples were divided into 11 vegetation types; most of the vegetation types had significant indicator species. We also use the two-way to present the plot of detrended correspondence analysis (DCA) by vegetation types and reefs. Moreover, this result reveals that these samples were more clearly cluster divided by islands. Our results reveal that the compositions and characteristics of plant communities were related clearly to the environmental factors for each reef in the Matsu Islands Tern Refuge. Canonical correspondence analysis (CCA) indicated that species composition of vegetation yielded high correlation with soil property, especially with soil pH. In addition, we found that the traces of bird activity is relevant to the characteristics and structures of plant communities. We found that the plant communities comprising low-grass shrubs would provide relatively soft nesting materials and sheltering effects for eggs or hatchlings for terns. Compared to low-grass shrubs, the traits of high-grass shrubs would not be beneficial to nest for breeding of terns on the ground, and no nested trace was found in these plant communities. Full article

Protection relays are important equipment used for protection, control, and metering functions in the power grid. These relays are used to protect critical and difficult-to-replace equipment, like generators, transformers, and capacitor banks. Once the protection devices are disturbed or damaged, a high risk of power generation interruption occurs. Therefore, it is important and necessary to study the relay’s immunity to electromagnetic pulse (EMP) events. As a preliminary step toward empirical experimentation on actual equipment, this manuscript outlines an economical and efficient methodology for evaluating the impact of an EMP. An impedance measurement strategy was employed to model the equipment, setting the stage for subsequent immunity analyses. These analyses included the pulse current injection (PCI) method, which utilized an injecting probe to introduce the transient, and frequency domain electromagnetic (FEKO) simulation, which integrated electromagnetic coupling effects into the transient simulation. The impedance measurement and simulation results in this paper provide a reliable basis for gauging equipment performance in the face of HEMP threats. The results obtained using the PCI and FEKO simulations demonstrated the performance of different port responses under a high-altitude EMP, indicating the requirement for some protection to ensure the reliable operation of relays. Full article

Magnetic resonance wireless power transmission consists of a source coil and relay coil (transmission coil (Tx-coil), receiving coil (Rx-coil)). The relay coil is designed with windings and a series capacitor, which are resonant with the input voltage frequency. Magnetic resonant wireless power transmission by a relay coil enables the transmission of power from a few centimeters to several meters. Recently, research has been conducted on the shape and material of each coil to increase the transmission distance. However, limitations remain with respect to increasing the transmission distance. Specifically, the optimization of the electrical characteristics of the relay coil is necessary to increase the transmission distance and improve efficiency. In this study, we configured the inductance of the relay coil to be approximately 95 μH, 270 μH, and 630 μH. Accordingly, we designed the series capacitors to have the same resonant frequency and analyzed the transmission characteristics of each relay coil. We confirmed that as the inductance increased, the transmission efficiency increased by up to 10%. The relay coil was designed to have an inductance of approximately three to six times that of the source coil (load coil). Thus, the optimal design of the relay coil is believed to be the most efficient and economical coil design. Full article

Long-Range Wide-Area Networks (LoRaWAN) allow the transmission of data via radio link from sensors, which are potentially isolated or difficult to access, to gateways and servers that are connected to cellular networks for data processing, exchange, or relay, with low transmission power. This concept employs Long-Range (LoRa) modulation and has led to the emergence of many applications for the monitoring and tracking of objects. However, due to its characteristic of a low data rate for low-power communication, the transmission of information with LoRa technology is not suitable for the fast real-time monitoring of data. Additionally, due to its narrow bandwidth, an attempt to perform localization through the LoRa modulation technique will result in very limited accuracy because of its inability to resolve multipath problems. Thus, in this paper, we propose a multi-standard Ultra-Wide Bandwidth (UWB) and LoRa end-device that is capable of measuring location with high accuracy using UWB technology and then transmitting the location information through LoRa method to gateways and the Internet of Things Network. The results of measurements in indoor and outdoor scenarios show a UWB localization accuracy that is of sub-meter level, being between 10 and 33 cm, and a UWB range of 124 m in Line-of-Sight (LOS) and 55 m in Non-Line-of-Sight (NLOS) applications, respectively. Full article

Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation. Full article

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. Full article

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small-measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible troubleshooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources. Full article

The built-in relay in a meter is a key control component of a smart meter, and its reliability determines whether the user can use electricity safely and smoothly. In this paper, the degradation characteristics of the arc-burning energy are enhanced by the method of K-means clustering to replace degradation data, such as the overtravel time, release time, and other data. In existing methods, the meter needs to be disassembled to describe the degradation trend of the meter relay. The proposed method is combined with a bidirectional long short-term memory (Bi-LSTM) neural network to predict the degradation trend of the relay’s performance. In this paper, K-means clustering is used to enhance the extraction of arc energy data features, and then the arc energy data obtained from the reliability lifetime test is assessed to predict the degradation trend of the meter relay by means of a bidirectional LSTM. Full article