Johnson A. Asumadu

Precision Battery Management System

The paper presents a new battery management system for a lithium ion battery pack for more efficient operation and sturdy. The new system contains an embedded microcontroller to track the energy content of cell battery, optimize the output current, and to provide extensive feedback of all the measurements taken. This system sends all data to a telemetry system so that the data can be relayed to a laptop via wireless signal. Two unique advance features of the BMS are its ability to optimize the battery pack energy and also to provide cell equalization. Since the BMS is used in an electric vehicle, very low power consumption is essential

Wind-solar hybrid electrical power production to support national grid: Case study - Jordan

The paper presents the next generation of power energy systems using solar- and wind-energy systems for the country of Jordan. Presently with the oil prices are on the rise, the cost of electrical power production is very high. The opportunity of a large wind and solar hybrid power production is being explored. Sights are chosen to produce electricity using the wind in the Mountains in Northern Jordan and the sun in the Eastern Desert. It is found that the cost of windmill farm to produce 100 – 150 MW is US

Radial Basis Function Neural Networks (RBFNN) and p-q Power Theory Based Harmonic Identification in Converter Waveforms

290 million while solar power station to produce 100 MW costs US

Synthesis of nonlinear control of switching topologies of buck-boost converter using fuzzy logic

560 million. The electrical power costs US

A multivariable fuzzy logic controller (MFLC) for a buck DC-DC converter

0.02/kWh for the wind power and US

On-line harmonic estimation in power system based on sequential training radial basis function neural network

0.077 for the solar power. The feasibility for using wind and solar energies is now when the price oil reaches US

PID control for improving P&O-MPPT performance of a grid-connected solar PV system with Ziegler-Nichols tuning method

100.00 per barrel. The paper also discusses different power electronics circuits and control methods to link the renewable energy to the national grid. This paper also looks at some of the modern power electronics converters and electrical generators which have improved significantly solar and wind energy technologies.

Magnetic Nanoparticle-Based Gyroscopic Sensor: A Review

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

Fault detection and classification for compensating network using combination relay and ANN

An intelligent fuzzy logic inference pipeline for the control of a DC-DC buck-boost converter was designed and built using a semi-custom VLSI chip. The fuzzy linguistics describing the switching topologies of the converter was mapped into a look-up table that was synthesized into a set of Boolean equations. A VLSI chip-a field programmable gate array (FPGA)-was used to implement the Boolean equations. Features include the size of RAM chip independent of number of rules in the knowledge base, on-chip fuzzification and defuzzification, faster response with speeds over giga fuzzy logic inferences per sec (FLIPS), and an inexpensive VLSI chip.

Generator out-of-step prediction using wavelet analysis

This paper describes the design and construction of a multivariable fuzzy controller (MFLC) for the control of a DC-DC buck converter. An off-the-shelf hardware-based MFLC system has been developed that is used to model the variable switching structure of the buck converter. The MFLC design criterion is based on the error in output voltage and the change of error in the output voltage as inputs of the controller, and the changes in the duty cycle ratio as output of the controller. The error and change of error are two membership functions bounded in the interval [0, 1]. The two inputs have two fuzzy subsets, negative fuzzy subset N and positive fuzzy subset P. The output is made up of fuzzy subsets of singletons. It will be shown that when the error and change of error have the same signs, the output moves away from a set point and therefore the same reasoning or rules can be used to change the output. The results will show that the MFLC technique provides robust control for nonlinear power electronics variable switching structure like the DC-DC buck converter.