Effect of Conducting Materials on UV-Vis Spectral Response Characteristics

Spectroscopic analysis is recently being employed to measure furan concentration in transformer oil. Light absorbance and spectral bandwidth are used as indicators to estimate furan concentration level in transformer oil. However, the possibility of dust and other conducting materials that might exist in transformer oil sample need to be considered as it may influence the spectroscopic performance. In this paper, series of tests are carried out to investigate the impact of dust on the transformer oil spectral response characteristics. Oil sample with various furan concentrations along with certain amount of copper powder are examined. Experimental results show that the spectral response characteristics only affected by dust at the beginning of mix process. However, the characteristic returns to the original one within a few minutes

High Voltage Power Transformer Dissolved Gas Analysis, Measurement and Interpretation Techniques

Dissolved Gas Analysis (DGA) is widely accepted powerful tool in diagnosing power transformer health condition. Several DGA measurement techniques have been developed since 1950’s, from limited laboratory testing up to site online monitoring. Gas Chromatography (GC), Hydrogen On-line Monitor and Photo-Acoustic Spectroscopy (PAS) are most popular among existing DGA techniques. Basic operating principle, advantages and disadvantages of GC, Hydrogen On-line Monitor and PAS are reviewed and discussed in this paper. Several DGA interpretation techniques such as Key Gas Method, Doernenburg, Rogers, IEC ratio methods and Duval Triangle method are also reviewed in this paper

A Novel Method of Measuring Transformer Oil Interfacial Tension Using UV-Vis Spectroscopy

In this article we propose an alternative method of measuring the IFT of transformer oil, using ultraviolet-to-visible (UV-Vis) the IFT of an oil sample and its spectral response parameters (bandwidth and peak absorbance)

A review on chemical diagnosis techniques for transformer paper insulation degradation

Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper

A New Technique to Measure Interfacial Tension of Transformer Oil using UV-Vis Spectroscopy

Interfacial tension (IFT) and acid numbers of insulating oil are correlated with the number of years that a transformer has been in service and are used as a signal for transformer oil reclamation. Oil sampling for IFT measurement calls for extra precautions due to its high sensitivity to various oil parameters and environmental conditions. The current used technique to measure IFT of transformer oil is relatively expensive, requires an expert to conduct the test and it takes long time since the extraction of oil sample, sending it to external laboratory and getting the results back. This paper introduces a new technique to estimate the IFT of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. UV-Vis spectral response of transformer oil can be measured instantly with relatively cheap equipment, does not need an expert person to conduct the test and has the potential to be implemented online. Results show that there is a good correlation between oil spectral response and its IFT value. Artificial neural network (ANN) approach is proposed to model this correlation

A smart traffic light using a microcontroller based on the fuzzy logic

Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fuzzy-logic-based microcontroller for a greater adaptability of the traffic light to the dynamics of the vehicles that are to cross the intersection. The ATMega2560 microcontroller-based smart traffic light is designed to create a breakthrough in the breakdown of congestions at road junctions, thereby optimizing the real-time happenings in the road. Ultrasonic, infrared, and light sensors are used in this smart traffic light, resulting in the smart traffic light’s effectiveness in parsing jams. The four sets of sensors that are placed in four sections determine the traffic light timing process. When the length of vehicle queue reaches the sensor, a signal is sent as the microcontroller’s digital input. Ultrasonic and infrared sensors can reduce congestions at traffic lights by giving a green light time when one or all of the sensors are active so that the vehicle congestions can be relieved

Development of mini scale compressed air energy storage system

Nowadays, we know about the problem of decreasing the source of natural gas fuel, makes the higher fuel cost for Gas Turbine power plant usage. Because of that, the new technology called the Compressed Air Energy Storage system is created. The main concept of this system is use otT-peak power to pressurize air into an underground reservoir, which is then released during peak daytime hour to power Gas Turbine for power production. This project is to design in small scale system where it can use otT-peak electricity to switch on the air compressor to compressed air. Then the compressed air produced will store in high pressure cylinder tank replace the airtight underground caverns. When the air is released from the high pressure tank, the air expands through a micro-turbine which connected single shaft with generator rotor. Then the micro turbine run and rotate generator rotor that convert mechanical energy to electrical energy. Output voltage then will convert from dc to ac voltag

Malaysian Vehicle License Plate Recognition Using Double Edge Detection

Vehicle plate number is a unique combination of characters and numbers. Hence, it has been used in various application as personal identification such as for parking system identification, security monitoring system and etc. This paper illustrated the double edge detection technique in order to enhance the vehicle plate image, before character recognition process. Firstly, the vehicle image is captured, and then it will be re-sized and cropped until the resolution of image is 300×300. After the re-sized process, First Edge detection is applied to the image. Threshold of black and white are 59 and 60 respectively used to change the image into black and white colour only. Next, Second Edge detection is used to remove the unwanted image and only remain the plate number in white colour. MATLAB software is used in this experiment

Laboratory prediction energy control system based on artificial intelligence network

The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly

COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND ANFIS TO IMPROVE EEVE LABORATORY ENERGY USE PREDICTION PERFORMANCE

The energy use that is in excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neurofuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration