3,975 research outputs found
Faulty sensor detection using data correlation of multivariant sensor reading in smart agriculture with IOT
The Internet of Things (IoT), the idea of getting real-world objects connected with each other, will change the ways we organize, obtain and consume information radically. Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. On the other hand, Sensor fault is critical in smart grids, where controllers rely on healthy measurements from different sensors to determine all kinds of operations. However, when sensor fault happens, missing data and/or bad data can flow into control and management systems, which may lead to potential malfunction or even system failures. This brings the need for Sensor Fault Detection and eliminate this potential fault. This thesis proposes to design a Faulty Sensor Detection Mechanism using the data correlation method of multivariate sensors. This method will be applied to the smart agriculture which uses multi-variate sensors such as moisture sensor, temperature sensor and water sensor in IoT. The data are collected and received by a microcontroller which also can be linked to the internet. According to the algorithm, which applied on the smart agriculture, in case, the system gives No FAULT when the correlation value between (temperature, moisture) and (temperature, water) are negative and positive for (Water, moisture). In other cases. The system has a fault in a sensor when the correlation values between sensors are changed. Also, when the sensor gives a constant reading for a long time the system has got a fault in this sensor. The system got No FAULT when was different in sensors reading and the correlation value between (temperature, moisture) is (-0.33), between (temperature, water) is (-0.16) and (moisture, water) is (0.36). In addition, this system will be connected to the internet through the ESP8266 module. In order to surveillance the system at anytime and anywhere, this system is connected with the cloud (Things board) by using an ESP8266 WiFi network connection. This would allow the system to be more efficient and more reliable in detecting and monitoring the system’s parameters such as the state of sensors. The accuracy of the algorithm for data
correlation may be changing depending on the application that wants to detect the faulty sensor in the system and according to how many data that income to the microcontroller per minute and how many data should take to calculate the correlation coefficient. Therefore, for the smart agriculture which it's used in this project, the period is adjusted to give a good diagnose for the sensor as soon as possible
An overview of biodiesel energy
Over the last ten years’ attention to biofuels production has increased dramatically and become crucial. One of the main factors is the rise in world oil prices, coupled with the abatement of greenhouse gas emissions and concerns about energy security. Biodiesel is diesel fuel extracted and made out of animal fats, vegetable oils, or recycled restaurant greases. It is harmless, biodegradable, and generate fewer air pollutants than conventional-based diesel. This paper will be summarized the overview of biodiesel including. competitiveness of biodiesel, ii. biodiesel production and iii. engine performance using biodiesel
Parametric and nonparametric identification of shell and tube heat exchanger mathematical model
Parametric and nonparametric models of a shell and tube heat exchanger are studied.
Such models are very important because they provide information about controlling a
system operation. Without the model, the control task would be difficult for tuning of
controller. For many years, researchers have studied these models; however, their
models are still less satisfactory since they are not in general form. This problem is
caused by two key issues, namely, multiple unknown parameters and highly
nonlinear structures. Energy balances have been set-up for condition of unknown
parameters which involved, among others, temperature, flow rate, density and heat
capacity. The identification process produces a dynamic model of the heat exchanger
which is developed based on a lumped parameter system. The model developed is
single input single output whereas input signal is hot water flow rate and the output is
cold water temperature. The general form of the model obtained could have
parametric model structures such as auto regressive with external input, average auto
regressive moves with external input, output error or box-jenkins. The study in this
thesis aims to solve the general form through parametric and nonparametric models
which has been proposed as candidate models. Both candidate models have been
implemented and tested by applying several data sets constructed in lab experiments.
The first finding is the derivation of the dynamic model in the general form of the
transfer function in s domain, and it has been proven that it has parametric model
structure. The second finding is the first order without delay time transfer function of
the nonparametric model where they have gain is 35.20C and time constant 7200s.
These have proven to fulfill that the measured experimental data contains calculated
error that is no than more 2%. The third finding is the parametric model obtained has
proven that the measured experimental data contains calculated error level that is
very satisfactory, i.e. less than 1%. This error has been determined based on the final
prediction error for each model structure used. The best model has been chosen, i.e.
bj31131. It has the smallest values of the loss function and final prediction error of
0.0023, and it has high values of the best fits, i.e. 96.84%. Parameter optimization
has been calculated to determine minimization or maximization of functions which
involved the parameter studied. It is used to find a set of design parameters that can
in some way be defined as optimal. The first until the third findings are minor
contribution while the parameter optimization has been a major contribution
Conceptual design of a fixed-pitch wind turbine generator system rated at 400 kilowatts
The design and cost aspects of a fixed pitch, 400 kW Wind Turbine Generator (WTG) concept are presented. Improvements in reliability and cost reductions were achieved with fixed pitch operation and by incorporating recent advances in WTG technology. The specifications for this WTG concept were as follows: (1) A fixed pitch, continuous wooden rotor was to be provided by the Gougeon Bros. Co. (2) An 8 leg hyperboloid tower that showed promise as a low cost structure was to be used. (3) Only commercially available components and parts that could be easily fabricated were to be considered. (4) Design features deemed desirable based on recent NASA research efforts were to be incorporated. Detailed costs and weight estimates were prepared for the second machine and a wind farm of 12 WTG's. The calculated cost of energy for the fixed pitch, twelve unit windfarm is 11.5 cents/kW hr not including the cost of land and access roads. The study shows feasibility of fixed pitch, intermediate power WTG operation
Specification, siting and selection of large WECS prototypes
The development of large-scale windpowered systems is outlined. Topics discussed include: prototype specifications development, site selection process, and selection of prototype contractor
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