Data Acquisition and Linearization of Sensors: Greenhouse Case Study

This work presents an overview of data acquisition, data logging and supervisory control of different parameters in a greenhouse. Raw measurement data from various parameters (surrounding temperature, pH of liquid, CO2 gas concentration) are acquired using DAQ and logged in a database for further analysis and supervisory control. For sensing the physical parameters, LM 35, pH probe, CO2 gas sensors are used. These sensors and DAQ needs uninterrupted power supply. For this purpose renewable energy is used to generate clean energy. Solar radiation can be used to generate electricity using PV (photo voltaic) cell and power conditioning circuit. This thesis is used to study the electrical characteristics of PV cell, which can be used to generate electricity from solar radiation for greenhouse purpose. Simulation studies have been carried out to know the electrical characteristics of PV cell for various irradiation levels. The sensors, which are mentioned above are mostly linear sensors. To use a nonlinear sensor suitably for data acquisition purpose, first of all the sensor linearization is done. In this work a thermistor is considered and its nonlinear characteristics are linearized using two methods (curve fitting method, Steinhart-Hart equation)

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Integrated PV Performance Monitoring System

The main aim of this research work is to design an accurate and reliable monitoring system to be integrated with solar electricity generating system. The amount of solar energy received on the surface of the earth varies due to meteorological conditions and apparent trajectory of the sun. Due to this, the availability of sunlight is an average of 5-6 hours per day throughout the year in Malaysia. The performance monitoring system is required to ensure that the PV based solar electricity generating system is operating at an optimum level. The PV monitoring system is able to measure all the important parameters that determine an optimum performance. The measured values are recorded continuously, as the data acquisition system is connected to a computer, and data is stored at fixed intervals. The hardware is fully supported by software designed to give full flexibility in terms of data retrieval and processing. The data can be locally used and can be transmitted via internet for monitoring purposes. The data that appears directly on the local monitoring system is displayed via graphical user interface that was created by using Visualbasic.net. The Apache software was used to retrieve data from the internet. The transmitted data received by the remote terminal can be viewed by using any internet browser

SCADA and related technologies

Presented at SCADA and related technologies for irrigation district modernization, II: a USCID water management conference held on June 6-9, 2007 in Denver, Colorado.SCADA systems in irrigation districts have focused on remote monitoring and remote control. In many districts, the remote control is manual, but in others the automation of structures is enabled through the usage of distributed control for the automation of individual structures. This paper presents the concept of an expanded, "umbrella" SCADA system that will perform the standard functions of remote control and remote monitoring, and will also incorporate information flow in the field for operators. The umbrella SCADA system will mesh the equipment-equipment information into an equipment-program-personnel network

SCADA and related technologies

Presented at SCADA and related technologies for irrigation district modernization, II: a USCID water management conference held on June 6-9, 2007 in Denver, Colorado.Northern Water (Northern Colorado Water Conservancy District) conducted field demonstrations and comparisons of flow monitoring equipment at 18 canal and ditch sites in the lower South Platter River Basin during the 2006 irrigation season. Equipment included data loggers from 8 different manufacturers, 16 different models of water level sensors from 12 manufacturers, and 4 different types of telemetry from 7 manufacturers. The data loggers that were demonstrated included four models of single-sensor with integrated data logger, four models of programmable multi-sensor data logger, and one model of basic, low-cost data logger without telemetry. Relative equipment costs for each data logger system are summarized in Table 6. The water level sensors tested included submersible pressure transducers, optical shaft encoders, ultrasonic distance sensors, bubbler level sensor, float and pulley with potentiometer, buoyancy sensor, and a laser distance sensor. Bench checks of sensor calibrations were accomplished by Northern Water staff before field installation, and again at the end of the irrigation season. Observed sensor accuracy was compared to that expected from manufacturer specifications. The telemetry systems tested in the field included license-free spread-spectrum radios from four manufacturers, licensed radio modems in the 450 MHz range, satellite radio modems to a web server, and cdma modems with static IP addresses. Increased mast height and high gain directional antenna improved radio telemetry as expected. Additionally, operational files were utilized to document telemetry performance when available. The purpose and intent of the equipment demonstration and comparison was not to identify a single best data logger, sensor, and/or telemetry system. Each has different features and strengths, as well as varying costs. For each specific flow monitoring application, different equipment may be preferred or better suited than other equipment. However, the 2006 demonstration and comparison should provide a reference point for those seeking to become more knowledgeable in equipment selection while avoiding unpleasant surprises

Generic sensor network architecture for wireless automation (GENSEN)

fi=vertaisarvioimaton|en=nonPeerReviewed

Development of Microgrid Test Bed for Testing Energy Management System

Today the world population has reached 7.5 billion, and this number is expected to grow at the rate of 1.13% every year [1]. With this increase in population, the total demand for electricity has also increased. More people means the need for more power: electricity to power homes, schools, industries, hospitals, and so on. In today’s world, where most of the daily activities are dependent on electricity, demand for electricity, therefore, continues to rise. Currently, managing this growing need for electricity is one of the challenges the world is facing. In addition to this, approximately 1.2 billion people live in remote parts of the world where the electricity supply is either limited or non-existent [2]. Providing an affordable and easily available source of electricity to this population is another challenge. In response to these challenges, a significant number of countries are investing in the integration of renewable resources for energy production. Renewable resources such as the sun, wind, and water are free, clean, and readily available. Remote and poor parts of the world can also benefit by utilizing these available energy sources for electricity generation. The use of renewables helps to decrease the overall cost of electricity generation as well. This need for clean and safe energy has contributed to creating and promoting the concept of microgrids around the world. Microgrids are defined as small-scale power distribution networks with distributed energy sources, loads, and storage. They can operate in either grid-connected or islanded mode. Renewable sources are intermittent in nature, and uncertainties are always present in the microgrid operation when using these resources. The Energy Management technique is required for the coordination of these resources in order to mitigate the potential risks. Some studies have been conducted in the area of microgrid operation, stability, and control, and various types of laboratory-based microgrid test beds have been developed. A microgrid test bed allows testing of scaled down systems in order to test and simulate large real-world microgrid projects. The objective of this study is to develop a reconfigurable microgrid test bed. This test bed is created on a laboratory scale and is capable of testing energy management algorithms to validate real-time operation. A novel approach to automatic microgrid operation is proposed with the use of commercial off-the-shelf equipment and the Controller Area Network (CAN) protocol. The OPAL-RT 5600 real-time simulator is used as a central controller for controlling and scheduling microgrid sources to supply the load, charge the battery and, read a state of charge values. The CAN communication protocol is used by the controller to control and coordinate different components. Different cases are studied in order to support the reconfigurability, automatic operation, and energy management in the microgrid test bed using the CAN bus

General Catalog 2002-2004

Contains course descriptions, University college calendar, and college administrationhttps://digitalcommons.usu.edu/universitycatalogs/1123/thumbnail.jp