Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Monitoring of a micro-smart grid: Power consumption data of some machineries of an agro-industrial test site.

ABSTRACT: For the management of a (micro)-smart grid it is important to know the patters of the load profiles and of the generators. In this article the power consumption data obtained through a monitoring activity developed on a micro-smart grid in an agro-industrial test-site are presented. In particular, this reports the synthesis of the monitoring results of 5 loads (5 industrial machineries for crop micronization, corncob crashing and other similar processes). How these data were used within a monitoring and managing scheme of a micro-smart grid can be found in (E. Fabrizio, V. Branciforti, A. Costantino, M. Filippi, S. Barbero, G. Tecco, P. Mollo, A. Molino, 2017) [1]. The data can be useful for other researchers in order to create benchmarks of energy use input appropriate energy demand values in optimization tools for the industrial sector.

SUBMITTER: Fabrizio E

PROVIDER: S-EPMC5219600 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Monitoring of a micro-smart grid: Power consumption data of some machineries of an agro-industrial test site.

Fabrizio Enrico E Biglia Alessandro A Branciforti Valeria V Filippi Marco M Barbero Silvia S Tecco Giuseppe G Mollo Paolo P Molino Andrea A

Data in brief 20161221

For the management of a (micro)-smart grid it is important to know the patters of the load profiles and of the generators. In this article the power consumption data obtained through a monitoring activity developed on a micro-smart grid in an agro-industrial test-site are presented. In particular, this reports the synthesis of the monitoring results of 5 loads (5 industrial machineries for crop micronization, corncob crashing and other similar processes). How these data were used within a monito ...[more]

PMID: 28070547

Similar Datasets

Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption Data.

Project description:Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.

| S-EPMC7570659 | biostudies-literature

Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0.

Project description:Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyber-physical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) requirements in the smart grid. In this context, this paper describes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assignment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid.

| S-EPMC7896142 | biostudies-literature

Electricity production and consumption data from Danish power grid and governmental office buildings.

Project description:This data article is the second component of the two-part study on the effects of high-resolution energy data on building life cycle assessments (LCAs) (Environmental performance assessment of the use stage of buildings using dynamic high-resolution energy consumption and data on grid composition, in press) [1]. The first part being the article serving as the main platform for the communication of the conclusions of the study, and this data article serving as Supporting information, as well as a means to present any results and analysis not included in the article. The focus is to present additional information strengthening the findings of the article, by showing data, graphs, and tables not included in the research article due to size limitations.

| S-EPMC6369333 | biostudies-literature

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm.

Project description:With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

| S-EPMC10703079 | biostudies-literature

Game Theoretic Honeypot Deployment in Smart Grid.

Project description:The smart grid provides advanced functionalities, including real-time monitoring, dynamic energy management, advanced pricing mechanisms, and self-healing, by enabling the two-way flow of power and data, as well as the use of Internet of Things (IoT) technologies and devices. However, converting the traditional power grids to smart grids poses severe security challenges and makes their components and services prone to cyber attacks. To this end, advanced techniques are required to mitigate the impact of the potential attacks. In this paper, we investigate the use of honeypots, which are considered to mimic the common services of the smart grid and are able to detect unauthorized accesses, collect evidence, and help hide the real devices. More specifically, the interaction of an attacker and a defender is considered, who both optimize the number of attacks and the defending system configuration, i.e., the number of real devices and honeypots, respectively, with the aim to maximize their individual payoffs. To solve this problem, game theoretic tools are used, considering an one-shot game and a repeated game with uncertainty about the payoff of the attacker, where the Nash Equilibrium (NE) and the Bayesian NE are derived, respectively. Finally, simulation results are provided, which illustrate the effectiveness of the proposed framework.

| S-EPMC7435919 | biostudies-literature

Microorganisms in agro-industrial waste compost

Project description:Management of starting feedstock material to obtain suppressive compost. Deeping on microbiological characteristics and the relationship with chemical composition during composting process

| PRJEB44354 | ENA

An efficient heterogeneous signcryption for smart grid.

Project description:A smart grid, considered the next-generation type of power grid, combines a traditional power grid with information and communication technologies to effectively facilitate power generation and ensure transmission security and reliability in real-time. Only authorized consumers should be able to access the smart grid because the information gathered by smart meters includes users' private information. However, smart grid security is still a challenge. Motivated by this challenge, in this paper, we propose a heterogeneous signcryption (HSC) scheme for secure communication between smart meters and the utility. We demonstrate that this scheme is indistinguishable against adaptive chosen-ciphertext attacks (IND-CCA2), existentially unforgeable against adaptive chosen-message attacks (EUF-CMA) and ciphertext-anonymous against adaptive chosen ciphertext attacks (ANON-CCA2) under the computational Diffie-Hellman (CDH) problem in the random oracle model. Our scheme simultaneously achieves confidentiality, integrity, authentication, non-repudiation and ciphertext anonymity in a single logical step. It supports heterogeneous systems, allowing a meter in an identity-based cryptography (IBC) environment to transmit electrical usage data to a utility in a public key infrastructure (PKI) environment. Compared with other existing related schemes, our scheme has the lowest communication overhead and energy consumption for the smart grid. Based on these features, our scheme is highly suitable for secure power transmissions in a smart grid.

| S-EPMC6298654 | biostudies-other

Authentication of smart grid communications using quantum key distribution.

Project description:Smart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid's reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authentication of smart grid communications. In particular, we make such demonstration on a deployed electric utility fiber network. The developed method was prototyped in a software package to manage and utilize cryptographic keys to authenticate machine-to-machine communications used for supervisory control and data acquisition (SCADA). This demonstration showcases the feasibility of using QKD to improve the security of critical infrastructure, including future distributed energy resources (DERs), such as energy storage.

| S-EPMC9325752 | biostudies-literature

Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems.

Project description:The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning.

| S-EPMC10951540 | biostudies-literature

Power Grid Protection of the Muscle Mitochondrial Reticulum.

Project description:Mitochondrial network connectivity enables rapid communication and distribution of potential energy throughout the cell. However, this connectivity puts the energy conversion system at risk, because damaged elements could jeopardize the entire network. Here, we demonstrate the mechanisms for mitochondrial network protection in heart and skeletal muscle (SKM). We find that the cardiac mitochondrial reticulum is segmented into subnetworks comprising many mitochondria linked through abundant contact sites at highly specific intermitochondrial junctions (IMJs). In both cardiac and SKM subnetworks, a rapid electrical and physical separation of malfunctioning mitochondria occurs, consistent with detachment of IMJs and retraction of elongated mitochondria into condensed structures. Regional mitochondrial subnetworks limit the cellular impact of local dysfunction while the dynamic disconnection of damaged mitochondria allows the remaining mitochondria to resume normal function within seconds. Thus, mitochondrial network security is comprised of both proactive and reactive mechanisms in striated muscle cells.

| S-EPMC5490369 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data