International Journal of Advances in Applied Sciences
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Evaluation of shale volume and effective porosity using larionov and archie equations from wire-line logs, Niger delta Nigeria
Get PDFIn Niger Delta region of Nigeria, reservoirs are mostly loose and unstratified sands to hold fluids. In this paper, three different wells in central Niger Delta were assessed for shale volume and actual porosity. The results of the analysis delineate the presence of sand, sand-shale and shale formations. Hydrocarbon prospecting was found to be strong in sand, moderate in sandshale and shallow in shale respectively. However, existent of shale lessens effective porosity and water saturation of the rock formations. The extent of the formation extends from 1300 to 2500 m. Shale volume and actual porosity values extend from 0.00 to 0.302 dec and 0.047 to 0.302 dec which decrease with increasing depth. Comparably, the water saturation and water resistivity extend from 0.432 to 0.779 dec and 0.106 to 2.918 Ohm respectively. These values of actual porosity are strong in sand, moderate in sand-shale and shallow in shale formations. The results from this assessment proof well log a vital and easier tool in assessing of reservoir properties
Statistical analysis of night radiance RH using VIIRS day/night band satellite time series data
Get PDFAmount of night lights in an area is a proxy indicator of electricity consumption. This is interlinked to indicators of economic growth such as socio-economic activities, urban population size, physical capital, incidence of poverty. These night lights are generated by renewable and non renewable energy source. In this paper the behavior of night radiance RH data was minutely analyzed over a period of 28 hour; Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation data were used. These 28 hours and 8936 observations time series data is from 2 September 2018 to 4 September 2018. The behavior of night radiance RH data over 122 time intervals was analyzed using box plots. It was seen that the arithmetic mean of RH data is more sensitive than the arithmetic mean of first order difference of RH data. The first order difference of night radiance RH was regressed on night radiance over 110 intervals of time. The box plot of slope and intercept of this linear regression showed the behavior of these regression parameters over 110 intervals of time. It is seen that the data are more scattered with respect to slope than with respect to intercept.
KED-AES algorithm: combined key encryption decryption and advance encryption standard algorithm
Get PDFTwo existing cryptosystems are being combined and proposed. It is the enhanced combination of KED (Key Encryption Decryption), a cryptosystem that uses modulo 69 and the AES (Advance Encryption Standard) cryptography. The strength of the KED is that the keys are being used by the sender and the receiving end. The AES is easy to implement and has good defense against various attacks such as hacking
Active power loss reduction by opposition based kidney search algorithm
Get PDFIn this work Opposition based Kidney Search Algorithm (OKS) is used to solve the optimal reactive power problem. Kidney search algorithm imitates the various sequences of functions done by biological kidney. Opposition based learning (OBL) stratagem is engaged to commence the algorithm. This is to make certain high-quality of preliminary population and to expand the exploration steps in case of stagnation of the most excellent solutions. Opposition based learning (OBL) is one of the influential optimization tools to boost the convergence speed of different optimization techniques. The thriving implementation of the OBL engages evaluation of opposite population and existing population in the similar generation to discover the superior candidate solution of a given reactive power problem. Proposed Opposition based Kidney Search Algorithm (OKS) has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the proposed algorithm reduced the real power loss efficiently
Theory and development of magnetic flux leakage sensor for flaws detection: A review
Get PDFThis paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.
Optimization of transmission signal by artificial intelligent
Get PDFIn this method, radial basis function network RBFNN is an artificial intelligent which is used to identify and classify the communication system performance. RBFNN is one type of neural network which has activation functions. It consists of three layer input layer, hidden layer and output linear combination. One of the main problems of communication system is that it causes slow response for sending signal via the transmission devices. Therefore, the artificial intelligent by RBFNN is used to optimize the transmission signal. The input signal is trained and testing by neurons with weight and this lead to provide linear output. The simulation results have the optimization specifics over the traditional communication transmission devices
Frequency control of islanded microgrid using fuzzy-PI and autotuned controllers
Get PDFAny mismatch between generation and demand causes frequency to deviate from nominal value which affects the microgrid operation and reliability of power flow. The load frequency changes abnormally, which is fuzzy in nature, due to low system inertia and unpredictable variation in wind and solar irradiance level. So a frequency controller is needed to solve this problem meeting generation and demand of an islanded microgrid system considering the fuzziness in frequency fluctuation. This paper presents a case study of a hybrid microgrid system consisting of PV system, wind turbine generator set, diesel generator set along with storage facility and equipped with a proposed fuzzy-PI controller for frequency control under islanded condition. This controller shows satisfactory steady-state response. Further, performance of the proposed fuzzy-PI controller is verified with that of an autotuned PI controller to get faster response. The change in frequency is found minimum in case of autotuned PI controller as compared to fuzzy-PI controller. The proposed fuzzy-PI controller is validated based on ITAE (4-7%) which is higher than that attained form autotuned-PI controller. The developed model is simulated in Matlab/Simulink environment in this case study
Cache optimization cloud scheduling (COCS) algorithm based on last level caches
Get PDFRecently, the utilization of cloud services like storage, various software, networking resources has extremely enhanced due to widespread demand of these cloud services all over the world. On the other hand, it requires huge amount of storage and resource management to accurately cope up with ever-increasing demand. The high demand of these cloud services can lead to high amount of energy consumption in these cloud centers. Therefore, to eliminate these drawbacks and improve energy consumption and storage enhancement in real time for cloud computing devices, we have presented Cache Optimization Cloud Scheduling (COCS) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment which rely upon Dynamic Voltage and Frequency Scaling (DVFS). The proposed COCS technique helps to reduce last level cache failures and the latencies of average memory in cloud computing multi-processor devices. This proposed COCS technique provides an efficient mathematical modelling to minimize energy consumption. We have tested our experiment on Cybershake scientific dataset and the experimental results are compared with different conventional techniques in terms of time taken to accomplish task, power consumed in the VMs and average power required to handle tasks
Solar panel monitoring and energy prediction for smart solar system
Get PDFSolar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation
Real time simulation of sensorless control based on back-EMF of PMSM on RT-Lab/ARTEMIS real-time digital simulator
Get PDFReal-time simulation (RT) is very useful for rapid prototyping of complex and expensive systems using the high performance of a multiprocessor system. It has many applications in the field of testing controllers and protection systems under real conditions. In this article, Real-time simulations results of sensorless control of permanent magnet synchronous motor (PMSM) are presented. This simulator consists of two major subsystems, software with a Matlab / Simulink and hardware including FPGA boards for data acquisition, control boards and sensors. The two subsystems were coordinated together to achieve the simulation RT. To estimate the rotor position, a sliding mode observer (SMO) based on back emfs of the motor was implemented. The stability of the proposed method was verified using the concept of Lyapunov. A real-time system based on FPGA, is used for implementing and testing the algorithm for rotor position estimation based on back-emf tracking