An IOT-Enabled Generator for Power Monitoring and Load Management with Power Factor Improvement

The quality of power supply and reliability play a vital role in the smooth operation and maintenance of commercial use. These requirements have significant applications when dealing with residential areas, hospitals, industries, educational sectors, banks and airports, etc. In this regard, backup diesel generators are considered the most important source for an uninterrupted supply of electricity. However, there is an emergent need to avoid sudden shutdown of generators in the events of overload, shortage of fuel flow, service interval and lagging of power factor. These common problems can be addressed through monitoring of power generator parameters, for instance, real time remote monitoring to measure the health of the generator, the problem of load management due to high demand of power during peak hours and power factor improvement due to exceeding inductive load. In this paper, our proposed architecture—based on an IOT solution—consists of different sensors, namely a current transformer for measuring load, fuel gauge for fuel level monitoring, and temperature measurement with the energy module to determine the power factor of the system. Our proposed system is operated and tested on a real trolley-mounted 25 KVA generator.

Influence of Fuel Level on Properties, Productivity, and Mineralogy of Russian Vanadiferous Titanomagnetite Sinter

The influence of fuel level on Russian vanadiferous titanomagnetite sinter properties, productivity, and mineralogy are researched by sintering pot testing, metallographic microscopy, scanning electron microscopy analysis, and energy dispersive spectrometer (SEM-EDS) analysis. A comprehensive index is evaluated in conjunction with the same indexes and significance coefficient as that in the Panzhihua Iron and Steel Group. Results show that with the increasing fuel level from 3.5% to 6.0%, flame front speed, yield, tumbling test index (TI), and productivity, all first increase and then decrease. The low temperature reduction degradation index (RDI+3.15) and softening zone (ΔT) gradually increase while the RI and starting temperature of softening (T10), and ending temperature of softening (T40) decrease with increasing fuel levels from 3.5% to 6.0%. With the increase of fuel level from 3.5% to 6.0%, the content of FeO, SiO2, and MgO increase, while TiO2 shows a decrease. For the same increase in fuel level, the number of pores and calcium ferrite and hematite decrease but the silicate increases. In addition, in the fuel level range of 3.5% to 5.5%, magnetite correspondingly increases but then shows a drop after 5.5%. Moreover, when the fuel level increases to greater than 5.0%, FeOx and fayalite quickly increase and a small amount of metallic iron appears under the fuel level of 6.0%. Overall, the optimal fuel level under current production conditions and indicator selection is 4.0%.

Compensation of the error of automotive capacitive fuel level sensors when using them on special self-propelled rolling stock

Consumption of diesel fuel by the special rolling stock of Russian Railways per year amounts to tens of thousands of tons, and the issue of reliable accounting and control of its consumption is quite urgent. Currently, part of the special self-propelled rolling stock is equipped with on-board systems for measuring fuel consumption, however, in many units of this equipment, fuel control and accounting is carried out in manual mode. Massive introduction of on-board fuel consumption measurement systems on special self-propelled rolling stock is constrained, on the one hand, by the rather high cost of fuel sensors used on locomotives, on the other hand, by the increased error of relatively inexpensive automotive capacitive fuel level sensors. As part of the laboratory tests of such sensors, it was determined that when they operate on fuel of the same grade, the error corresponds to the passport and is at the level of 1 %, and when operating on fuel of different grades without additional recalibration, the error can reach 4 % or more. This is largely due to the simplified technology for measuring the amount of fuel in units of volume and insufficient compensation for changes in the density of diesel fuel. To solve this problem, an alternative to standard technology for determining the amount of fuel using automotive capacitive fuel level sensors is proposed, in which the dependence of the readings of these sensors on the fuel density at a standard temperature, once obtained in laboratory conditions, is used. Proposed technology of using automotive capacitive fuel level sensors on a special self-propelled rolling stock will allow keeping its relative reduced error at the level of 1 % and will provide measurement of the amount of fuel in units of mass.

Fuel Allocation and Lockout System

Fuel theft from vehicles is one of the major problems, the world is facing today. The reason for fuel theft is steady increase in the price. Hence fuel theft is a major concern for everyone, especially the logistics and fuel transport companies. These companies are facing significant losses due fuel theft from their fleet of vehicles which usually include heavy vehicles like trucks, lorries etc. There are several solutions which are used by these companies to prevent or minimize fuel theft which include monitoring cameras, additional security, GPS tracking of vehicles etc. We have come up with this project to prevent fuel theft, especially in fuel transport vehicles. Our proposed system detects any change in fuel level of a fuel tank using ultrasonic sensor and with the integration of GSM module, the message regarding the change in fuel level and the location of the vehicle (detected using GPS) is sent to the owner or the management. The additional feature of our project is, we can lock the fuel tank remotely if needed by the owner.

Development of a micromirror based laster vector scanning automotive HUD

Head-Up-Display (HUD) for automobiles is a system that displays the driving information such as the speed, fuel level, turning signal, GPS, etc on the windshield or on the road through a virtual image. With HUD, the driver does not need to lower his head to check the front panel for driving information and thus the driver can have a longer eyes-on-road time to improve the driving safety and comfort. LCD (Liquid Crystal Display) and VFD (Vacuum Fluorescent Display) based HUDs dominate today's automotive HUD market. In this thesis, a novel micromirror laser vector scanning HUD is developed for automobiles, which has the advantages over existing technologies including: 1) Higher brightness and contrast; 2) Wider angle of view; 3) Smaller size; and 4) Lower cost.

Development of a micromirror based laster vector scanning automotive HUD

Head-Up-Display (HUD) for automobiles is a system that displays the driving information such as the speed, fuel level, turning signal, GPS, etc on the windshield or on the road through a virtual image. With HUD, the driver does not need to lower his head to check the front panel for driving information and thus the driver can have a longer eyes-on-road time to improve the driving safety and comfort. LCD (Liquid Crystal Display) and VFD (Vacuum Fluorescent Display) based HUDs dominate today's automotive HUD market. In this thesis, a novel micromirror laser vector scanning HUD is developed for automobiles, which has the advantages over existing technologies including: 1) Higher brightness and contrast; 2) Wider angle of view; 3) Smaller size; and 4) Lower cost.

Design of a Fuel Sensor Noise Reduction System Using Kalman Filter

In the field of transportation, telematics is used to obtain vehicle information using Global Positioning System (GPS) technology which is integrated with sensors so that vehicle information can be monitored. One of them is fuel monitoring. The fuel sensor has good accuracy in stationary conditions, but the tability of the data is disturbed when the vehicle is running on an uneven road and causes the tank to shake. This study discusses a fuel sensor noise reduction system using a Kalman filter to overcome the problem of data instability due to shocks. This research aims to reduce noise so that the filter results are closer to the actual result. Filtering is done by changing the process error covariance (Q) and measurement error (R) in the Kalman filter. The fuel sensor noise is simulated using a simulator tank driven by an actuator that can tilt towards the x-axis and the y-axis to resemble the behavior of a vehicle. The fuel level data from the sensor readings are sent by GPS via the cellular network to a server which is then filtered using a web application. From the test results obtained the best filter with (Q) equals 0.1^3 and (R) equals 0.1^3. The average error of the best filter results is 4.73% where this value is 1.92% smaller than the average error of sensor data before filtering, which is 6.65%. Therefore, this proves that the system can reduce noise that occurs in the fuel sensor with the Kalman filter.

Neural network training for vehicle fuel level control

The goal of the paper is to create a training model based on real raw noisy data and train a neural network to determine the behavior of the fuel level, namely, to determine the time and volume of vehicle refueling, fuel consumption / excessive consumption / drainage.Various algorithms and data processing methods are used in fuel control and metering systems to get rid of noise. In some systems, primary filtering is used by excluding readings that are out of range, sharp jumps and deviations, and averaging over a sliding window. Research is being carried out on the use of more complex filters than simple averaging – by example, the Kalman filter for data processing.When measuring the fuel level using various fuel level sensor the data is influenced by many external factors that can interfere with the measurement and distort the real fuel level. Since these interferences are random and have a different structure, it is very difficult to completely remove them using classical noise suppression algorithms. Therefore, we use artificial intelligence, namely a neural network, to find patterns, detect noise and correct distorted data. To correct distorted data, you first need to determine which data is distorted, classify the data.In the course of the work, the raw data on the fuel level were transformed for use in the neural network training model. To describe the behavior of the fuel level, we use 4 possible classes: fuel consumption is observed, the vehicle is refueled, the fuel level does not change (the vehicle is idle), the data is distorted by noise. Also, in the process of work, additional tools of the DeepLearning4 library were used to load data training and training a neural network. A multilayer neural network model is used, namely a three-layer neural network, as well as used various training parameters provided by the DeepLearning4j library, which were obtained because of experiments.After training the neural network was used on test data, because of which the Confusion Matrix and Evaluation Metrics were obtained.In conclusion, finding a good model takes a lot of ideas and a lot of experimentation, also need to correctly process and transform the raw data to get the correct data for training. So far, a neural network has been trained to determine the state of the fuel level at a point in time and classify the behavior into four main labels (classes). Although we have not reduced the error in determining the behavior of the fuel level to zero, we have saved the states of the neural network, and in the future we will be able to retrain and evolve our neural network to obtain better results.