scholarly journals Investigation of a Dynamic Power Line Rating Concept for Improved Wind Energy Integration Over Complex Terrain

2014 ◽  
Author(s):  
Tyler B. Phillips ◽  
Inanc Senocak ◽  
Jake P. Gentle ◽  
Kurt S. Myers ◽  
Phil Anderson
Keyword(s):  
Steady State ◽  
Real Time ◽  
Weather Conditions ◽  
Power Line ◽  
Transmission Capacity ◽  
Weather Data ◽  
Energy Integration ◽  
Test Bed ◽  
Worst Case ◽  
Conductor Temperature

Dynamic Line Rating (DLR) is a smart grid technology that allows the rating of power line conductor to be based on its real-time temperature. Currently, conductors are generally given a conservative static rating based on near worst case weather conditions. Using historical weather data collected over a test bed area in Idaho, we demonstrate there is often additional transmission capacity not being utilized under the current static rating practice. We investigate a DLR method that employs computational fluid dynamics (CFD) to determine wind conditions along transmission lines in dense intervals. Simulated wind conditions are then used to calculate real-time conductor temperature under changing weather conditions. In calculating the conductor temperature and then inferring the ampacity, we use both a steady-state and transient calculation procedure. Under low wind conditions, the steady-state assumption predicts higher conductor temperatures which could lead to unnecessary curtailments, whereas the transient calculations produce temperatures that can be significantly lower, implying the availability of additional transmission capacity. Equally important, we demonstrate that capturing the wind direction variability in the simulations is critical in estimating conductor temperatures accurately.

scholarly journals An IoT based Low-cost Weather Monitoring System for Smart Farming

2021 ◽  
Author(s):  
L.P.S.S.K. Dayananda ◽  
A. Narmilan ◽  
P. Pirapuraj
Keyword(s):  
Liquid Crystal ◽  
Mobile Phone ◽  
Real Time ◽  
Monitoring System ◽  
Liquid Crystal Display ◽  
Low Cost ◽  
Weather Conditions ◽  
Weather Data ◽  
The Internet ◽  
Arduino Uno

Background: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity. Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature, moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet, weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions. Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2 level, wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the Arduino Uno software. Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile phone. This smart weather station was used to monitor real-time weather conditions while sending weather information to the farmer’s mobile phone, allowing him to make better decisions to increase yield.

scholarly journals An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

2021 ◽  
Vol 13 (21) ◽  
pp. 11893
Author(s):  
Abdul Rauf Bhatti ◽  
Ahmed Bilal Awan ◽  
Walied Alharbi ◽  
Zainal Salam ◽  
Abdullah S. Bin Humayd ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Energy System ◽  
Weather Data ◽  
Training Performance ◽  
Proposed Model ◽  
Artificial Neural Network Ann

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

scholarly journals Heterogeneous (ITS-G5 and 5G) Vehicular Pilot Road Weather Service Platform in a Realistic Operational Environment

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1676
Author(s):  
Muhammad Naeem Tahir ◽  
Marcos Katz
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Field Measurement ◽  
Road Traffic ◽  
Research Work ◽  
Ieee 802.11P ◽  
Weather Data ◽  
Test Bed ◽  
Operational Environment ◽  
Measurement Results

VANETs (Vehicular Ad hoc Networks) operating in conjunction with road-side infrastructure connecting road-side infrastructure are an emerging field of wireless communication technology in the vehicular communication’s domain. For VANETs, the IEEE 802.11p-based ITS-G5 is one of the key standards for communication globally. This research work integrates the ITS-G5 with a cellular-based 5G Test Network (5GTN). The resulting advanced heterogeneous Vehicular Network (VN) test-bed works as an effective platform for traffic safety between vehicles and road-side-infrastructure. This test-bed network provides a flexible framework to exploit vehicle-based weather data and road observation information, creating a service architecture for VANETs that supports real-time intelligent traffic services. The network studied in this paper aims to deliver improved road safety by providing real-time weather forecast, road friction information and road traffic related services. This article presents the implementation of a realistic test-bed in Northern Finland and the field measurement results of the heterogeneous VANETs considering the speed of vehicle, latency, good-put time and throughput. The field measurement results have been obtained in a state-of-the-art hybrid VANET system supporting special road weather services. Based on field measurement results, we suggest an efficient solution for a comprehensive hybrid vehicular networking infrastructure exploiting road weather information.

scholarly journals Iot Based Weather Station Using Raspberry Pi 3

2018 ◽  
Vol 7 (4.30) ◽  
pp. 145 ◽  
Author(s):  
P Y Muck ◽  
M J Homam
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Weather Data ◽  
Raspberry Pi ◽  
Weather Station ◽  
Media Platform ◽  
Weather Parameters ◽  
Graphical Presentation

Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.

scholarly journals PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3181
Author(s):  
Petros Karvelis ◽  
Daniele Mazzei ◽  
Matteo Biviano ◽  
Chrysostomos Stylios
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Weather Data ◽  
Learning Approaches ◽  
New Era ◽  
Wide Range ◽  
Industrial Iot

Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics.

REAL CONDITION AND PROSPECTS OF DEVELOPMENT OF THE RADAR STATIONS OF THE COUNTER BATTERY FIGHTINGV

2019 ◽  
Vol 1 (12) ◽  
pp. 30-40
Author(s):  
V. Нolovan ◽  
V. Gerasimov ◽  
А. Нolovan ◽  
N. Maslich
Keyword(s):  
Comparative Analysis ◽  
Real Time ◽  
Weather Conditions ◽  
Time Of Day ◽  
Current Position ◽  
Real Condition ◽  
Leading Role ◽  
Artillery Shell

Fighting in the Donbas, which has been going on for more than five years, shows that a skillful counter-battery fight is an important factor in achieving success in wars of this kind. Especially in conditions where for the known reasons the use of combat aviation is minimized. With the development of technical warfare, the task of servicing the counter-battery fight began to rely on radar stations (radar) to reconnaissance the positions of artillery, which in modern terms are called counter-battery radar. The principle of counter-battery radar is based on the detection of a target (artillery shell, mortar mine or rocket) in flight at an earlier stage and making several measurements of the coordinates of the current position of the ammunition. According to these data, the trajectory of the projectile's flight is calculated and, on the basis of its prolongation and extrapolation of measurements, the probable coordinates of the artillery, as well as the places of ammunition falling, are determined. In addition, the technical capabilities of radars of this class allow you to recognize the types and caliber of artillery systems, as well as to adjust the fire of your artillery. The main advantages of these radars are:  mobility (transportability);  inspection of large tracts of terrain over long distances;  the ability to obtain target's data in near real-time;  independence from time of day and weather conditions;  relatively high fighting efficiency. The purpose of the article is to determine the leading role and place of the counter-battery radar among other artillery instrumental reconnaissance tools, to compare the combat capabilities of modern counter-battery radars, armed with Ukrainian troops and some leading countries (USA, China, Russia), and are being developed and tested in Ukraine. The method of achieving this goal is a comparative analysis of the features of construction and combat capabilities of modern models of counter-battery radar in Ukraine and in other countries. As a result of the conducted analysis, the directions of further improvement of the radar armament, increasing the capabilities of existing and promising counter-battery radar samples were determined.

Multi-Component Liquid Analyzer During Steady-State and Dynamic CO2 Capture Operations: Near Real-Time Feedback in Industrialized Environment

2019 ◽  
Author(s):  
Chuck Panaccione ◽  
Greg Staab ◽  
Andy Awtry ◽  
Rene Kupfer ◽  
Tyler Silverman ◽  
...  
Keyword(s):  
Steady State ◽  
Real Time ◽  
Co2 Capture

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

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