Search Results (21)

Electrical power systems are crucial, yet vulnerable, due to their complex and interconnected nature, necessitating effective fault detection and diagnostics to ensure stability and prevent disruptions. Advances in artificial intelligence (AI) and the Internet of Things (IoT) have transformed the ability to identify and resolve electrical system problems efficiently. Electrical systems operate at various scales, ranging from individual households to large-scale regional grids. In this study, we focus on medium-scale urban infrastructures. These environments present unique electrical challenges, such as phase imbalances and transient voltage fluctuations, which require robust fault detection mechanisms. This work investigates the use of AI with dynamic programming and a support vector machine (SVM) to improve fault detection. The data collected from voltage measurements in urban office buildings with smart meters over a period of six weeks was used to develop an AI model, demonstrating its applicability to similar urban infrastructures. This model achieved high accuracy in detecting system failures, identifying them with a performance greater than 99%, highlighting the potential of smart sensing technologies combined with AI to improve urban infrastructure management. The integration of smart sensors and advanced data analytics significantly increases the reliability and efficiency of energy systems, promoting sustainable and resilient urban environments. Full article

Together with the rapidly growing world population and increasing usage of electrical equipment, the demand for electrical energy has continuously increased the demand for electrical energy. For this reason, especially considering the increasing inflation rates around the world, using an electricity energy meter, which works with the least operating error, has great economic importance. In this study, an artificial neural network (ANN)-based prediction methodology is presented to estimate an active electricity meter’s combined maximum error rate by using variable factors such as current, voltage, temperature, and power factor that affect the maximum permissible error. The estimation results obtained with the developed ANN model are evaluated statistically, and then the suitability and accuracy of the presented approach are tested. At the end of this research, it is understood that the obtained results can be used by high accuracy rate to estimate the combined maximum working error of an active electricity energy meter with the help of a suitable ANN model based on the internal variable factors. Full article

At present, the error calibration of electricity meters in operation generally adopts an off-site method; that is, the electricity meter is taken out of operation and then calibrated in the laboratory. Off-site calibration, while beneficial, may not fully capture the operational error of the electricity meter due to potential differences in environmental conditions. An on-site calibration device for electricity meters based on pulse detection is designed, which obtains the error of the electricity meter under calibration by comparing the energy pulses of the standard electricity meter with those of the electricity meter under calibration. High-precision voltage and current sampling channels are designed, with a voltage measurement error of less than 0.02% and a current measurement error of less than 0.03%. In response to the non-synchronous sampling problem caused by frequency fluctuations in the on-site verification environment, a fast optimal frequency estimation algorithm is applied to accurately calculate the signal frequency within two cycles. The sampling time interval is adjusted to achieve lock-frequency synchronous sampling, and ensure the accurate calculation of electrical parameters. In order to reduce the complexity of the device circuit structure and equipment cost, a standard electric energy pulses generation method based on digital integration-to-frequency is proposed, which uses software to generate electric energy pulses, with a maximum output frequency of up to 10 kHz. Tests conducted in the laboratory on the developed on-site calibration device for electricity meters show that its accuracy is better than the 0.05 accuracy class, meeting the application requirements for on-site verification of electricity energy meters. Full article

Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this gap by proposing a novel method that employs a fully connected neural network (FCNN) to predict household phases from energy consumption data. The research utilizes the IEEE European Low Voltage Testing Feeder dataset, which includes one-minute energy consumption readings for 55 households over a full day. The methodology involves data cleaning, preprocessing, and feature extraction through recursive feature elimination (RFE), along with splitting the data into training and testing sets. To enhance performance, training data are augmented using a generative adversarial network (GAN), achieving an accuracy of 91.81% via 10-fold cross-validation. Additional experiments assess the model’s performance across extended sampling intervals of 5, 10, 15, and 30 min. The proposed model demonstrates superior performance compared to existing classification, clustering, and AI methods, highlighting its robustness and adaptability to varying sampling durations and providing valuable insights for improving grid management strategies. Full article

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities. Full article

This study investigated a hybrid membrane and electro-membrane separation process for producing demineralized water from tertiary petrochemical effluent, reusing it as feeding water for high-pressure boilers for steam generation. The effluents were treated in a pilot plant with a 1 m³ h⁻¹ capacity by using a hybrid process of ultrafiltration (UF), reverse osmosis (RO), and electrodeionization (EDI). The physicochemical parameters of interest and maximum limits in industrial water were pre-determined by the industries. Operating parameters such as flow rate, pressure, percentage of recovery, and electric current were monitored, along with the frequency of chemical cleaning. The UF and RO systems operated with average permeate fluxes of 17 ± 4.06 L h⁻¹ m⁻² and 20.1 ± 1.9 L h⁻¹ m⁻², respectively. Under optimal operating conditions (flow rate of 600 L h⁻¹, voltage of 22.2 ± 0.7 V, and electric current of 1.3 A), EDI produced high-quality water with an average electrical conductivity of 0.22 μS cm⁻¹. Thus, the industrial water produced reached the quality required for reuse as make-up water for high-pressure boilers in the petrochemical industry. In addition, the specific energy consumption; the use of chemicals, spare materials, equipment; and labor costs were determined to support the technical feasibility study for implementing an industrial plant with a 90 m³ h⁻¹ producing capacity. This resulted in a cost of USD 0.64 per cubic meter of demineralized water produced, a cost similar to values reported in the literature. 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

To address the issues of low efficiency, poor security, insufficient compatibility, and difficulties in traceability associated with high-voltage electric energy metering (HVEEM) device verification methods, this paper proposes a design scheme for a remote verification system (RVS) of such devices based on a power cloud platform (PCP). The system adopts the concept of “high-precision local sampling + remote cloud verification” and develops a local acquisition device with compatibility and high precision to achieve fast acquisition of local electrical parameters. The IEC 61850 communication modeling is utilized to establish unified communication standards between the local device and the PCP. The PCP provides two verification methods: physical error verification based on a multi-channel standard and digital verification based on an improved Backpropagation (BP) neural network simulation model. Leveraging the advantages of power cloud technology, the system enables functions such as electrical energy calculation, remote intelligent error verification, cloud storage, condition monitoring, and early warning. Through testing and application, it has been demonstrated that the system achieves an integration accuracy level better than 0.02. It also exhibits good security, compatibility, and traceability of measurement values while attaining a high level of informatization and intelligence. Particularly, the system shows promising prospects for the remote and efficient verification of large-scale and multi-type high-voltage metering devices. Full article

Cyperus esculentus is an invasive perennial sedge that can cause huge losses in arable crops. Current control strategies are based on combinations of cultural, mechanical, and chemical measures, repeated over years. Recent commercial releases of safe innovative electric weeders, offer promising alternative opportunities for controlling perennial weeds with high energy/high frequency electricity. To evaluate the effect of a single electrocution application on the efficacy of C. esculentus control, field experiments were performed in two locations in Belgium. Two electric weeding devices were evaluated: Zasso XP300, delivering a high-frequency, phased direct current (maximum voltage of 7000 V and maximum power output of 2000 W per square meter of green biomass, driving speeds between 1.1 and 3.0 km·h⁻¹), and Rootwave Pro, delivering high-frequency alternating current (maximum voltage of 5000 V and power output between 7.5 and 10 kVA, treatment duration of 2 s). The impact of various technical (driving speed and voltage), biotic (clone and growth stage), and abiotic parameters on electrocution efficacy was evaluated. Plant responses to electrocution were evaluated by examining the vitality of treated C. esculentus mother tubers and shoots. Both devices were ineffective at mother tuber control, regardless of their burial depth (−5 cm to −15 cm), but were highly effective against aboveground shoots with reductions of vitality of up to 91% and 100% after a single pass with Zasso XP300 and Rootwave Pro, respectively. Maximum reductions were obtained when electricity was delivered at low speed (1.1 to 1.5 km·h⁻¹) and on 5-leaf shoots without heat or water stress. Remarkably, the lowest efficacies were found on water-stressed soils at the time of application. Voltage had no effect on the degree of C. esculentus control. The efficacy of electricity was not affected by clone, irrespective of electric weeding device. Electrocution is a useful and effective control method within any integrated control strategy for controlling emerged shoots. However, as C. esculentus mother tubers are not affected by a single treatment, season-long repeated treatments are needed to exhaust the mother tubers. Full article

At present, a worldwide paradigm shift has become apparent, with more and more consumers consuming the energy generated by renewable energy sources (RES) systems, such as wind or photovoltaic (PV) energy, sometimes benefiting from appropriate incentives by individual governments. Consequently, it is necessary to carry out technical–economic assessments to understand the evolution of the viability of RES investments. Within the framework of an intelligent network control environment, the smart grid (SG) concept is associated with this model, and is an important tool in the management of energy distribution networks. This article aims to make a further contribution to this issue by analyzing the economic feasibility of investing in residential consumers, considering different RES configurations. Scenarios covered in this study include: “inject all on the low voltage network/consume all on the low voltage network”, self-consumption, net-metering, and storage systems. The economic study results in this article show that self-consumption with and without the injection of excess electricity into the grid is quite attractive. The bi-hourly tariff was found to be more profitable than other tariffs. Variable tariffs (bi or tri-hourly) are more profitable than fixed tariffs. It is also concluded that investment in storage systems is not yet an economically viable solution due to the high price of energy storage. Full article

Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) intelligence in the energy workflow is gaining importance. All these approaches have a common need: data. Data that should be collected and provided in a reliable, accurate, secure, and efficient way. For this purpose, sensing technologies that enable ubiquitous data acquisition and the new communication infrastructure that ensure low latency and high density are the key. This article presents a sensing solution devoted to the precise gathering of energy parameters such as voltage, current, active power, and power factor for server farms and datacenters, computing infrastructures that are growing meaningfully to meet the demand for network applications. The designed system enables disaggregated acquisition of energy data from a large number of devices and characterization of their consumption behavior, both in real time. In this work, the creation of a complete multiport power meter system is detailed. The study reports all the steps needed to create the prototype, from the analysis of electronic components, the selection of sensors, the design of the Printed Circuit Board (PCB), the configuration and calibration of the hardware and embedded system, and the implementation of the software layer. The power meter application is geared toward data centers and server farms and has been tested by connecting it to a laboratory server rack, although its designs can be easily adapted to other scenarios where gathering the energy consumption information was needed. The novelty of the system is based on high scalability built upon two factors. Firstly, the one-on-one approach followed to acquire the data from each power source, even if they belong to the same physical equipment, so the system can correlate extremely well the execution of processes with the energy data. Thus, the potential of data to develop tailored solutions rises. Second, the use of temporal multiplexing to keep the real-time data delivery even for a very high number of sources. All these ensure compatibility with standard IoT networks and applications, as the data markup language is used (enabling database storage and computing system processing) and the interconnection is done by well-known protocols. Full article

The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed. Full article

Enabling vehicles to draw energy from an electric road system (ERS) significantly reduces the need for battery capacity on board the vehicle. It is not necessary, nor realistic, to cover every meter of every stretch of road with ERS. The question then arises how and where the ERS sections should be placed. One way of doing it is to place equally long sections of ERS with a certain separating distance. Another way is to place the sections where the highest amount of traction power of the vehicles is required. This paper presents a performance evaluation of both these methods from an energy consumption and battery degradation point of view. This study assumes a conductive ERS which allows for high power transfer. Being conductive, galvanic isolation between the energy source (the ERS) and the on board traction voltage system (TVS) is needed for electric safety reasons. In addition to the two alternative methods for location of ERS segments, three different powertrains, each with a different approach to galvanic isolation and charging, are evaluated. It is discovered that the method for location of the ERS can in fact affect both energy consumption and battery degradation depending on powertrain and driving scenario. Full article

Depletion of fossil fuel deposits is the main current issue related to the world’s power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development. Full article

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes. Full article