Multivariate Analysis for Fault Diagnosis System

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher’s Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA

Frequency Control of Microgrid with Renewable Generation using PID Controller based Krill Herd

The main of this paper is to provide optimal control of a state microgrid system. The proposed configuration composes of renewable generation systems such as solar photovoltaic system and wind turbine generator with a Diesel Engine Generator and Fuel-Cell. An Aqua electrolyzer and other energy storage systems such as battery and flywheel are also used in the proposed microgrid. A standard PID (Proportional Integral Derivative) controller scheme is introduced whose its parameters are determined using different optimizations algorithm such as Algorithm Genetic, Particle Swarm Optimization, and Krill Herd algorithm for minimizing frequency and power deviations, in order to enhance the operation of this system. The PID controller gains are optimized by resolving an objective function. The simulation results are shown, and given that the Krill Herd algorithm improves the performance of the system in comparison with GA and PSO based on PID. The efficiency of the system is improved

A Forecasting Decision Support System

Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters’, simple exponential smoothing, and regression statistical analysis with a new proposed genetic algorithm to generate operational forecast. A case study is presented using real industrial demand data from different products types to show the improved demand forecasting accuracy for the proposed system over individual statistical techniques for all time series types

Optimal operation and battery management in a grid-connected microgrid

Recently, Smart grid is one the important concepts that improve the energy future. This paper proposes an economic optimization technique for battery management to reduce the operating cost of a grid-connected microgrid. The proposed microgrid includes photovoltaic, wind power, and battery storage system. The proposed objective concerns with determination of the optimal hourly management for the operating power system to meet the required profile of load demand. In addition to, the excess/lack amount of electricity can be sold to/purchased from the utility grid. Interior Search Algorithm (ISA), an efficient heuristic algorithm, is applied to solve the problem with an objective of total operational cost minimization. The optimization technique is applied to a typical microgrid system with different loading conditions as a case study to test its effectiveness. The proposed platform can be considered as a significant part of comprehensive energy management system (EMS). The simulation results indicate high potential savings for the total operating cost

X-Pinch Plasma Generation Testing for Neutron Source Development and Nuclear Fusion

Nuclear fusion is a sought-out technology in which two light elements are fused together to create a heavier element and releases energy. Two primary nuclear fusion technologies are being researched today: magnetic and inertial confinement. However, a new type of nuclear fusion technology is currently being research: multi-pinch plasma beams. At the University of Ontario Institute of Technology, there is research on multi-pinch plasma beam technology as an alternative to nuclear fusion. The objective is to intersect two plasma arcs at the center of the chamber. This is a precursor of nuclear fusion using multi-pinch. The innovation portion of the students’ work is the miniaturization of this concept using high energy electrical DC pulses. The experiment achieved the temperature of 2300 K at the intersection. In comparison to the simulation data, the temperature from the simulation is 7000 K at the intersection. Additionally, energy harvesting devices, both photovoltaics and a thermoelectric generator, were placed in the chamber to observe the viable energy extraction