Showing 3 results for Asghari
P. Asgharian, R. Noroozian,
Volume 15, Issue 1 (March 2019)
Abstract
Microturbine generation system is one of the most promising and a fast growing distributed generation sources. It is used in various applications thanks to high efficiency, quick start and high reliability. Combination of the microturbine and storage system (e.g. battery bank) is desirable selection to satisfy the load requirements under all conditions and hence the battery bank can play an important role in restoring balance between source and demand. In this paper, modeling of the microturbine with battery energy storage system is presented to supply sensitive loads. Appropriate power exchange between battery and the microturbine is an essential issue so, a new control method is proposed for battery energy storage based on instantaneous value of DC-link voltage. In this new strategy, DC-link voltage as well as battery parameters (current and voltage) are used in order to produce desirable DC-DC switching. A control scheme based on voltage, current and frequency measurement is presented for the corresponding inverter. Simulations are carried out in MATLAB/Simulink software and the results show that storage along with proper control improves system reliability to supply sensitive load. The proposed configuration can be used as a remote power, emergency power and also in micro-grid.
F. Asghariyehlou, J. Javidan,
Volume 18, Issue 2 (June 2022)
Abstract
This paper deals with the optimization of the CORDIC-based modified Gram-Schmidt (MGS) algorithm for QR decomposition (QRD) and presents a scalable algorithm with maximum throughput, the least possible latency, and hardware resources. The optimized algorithm is implemented on Xilinx Virtex 6 FPGA using ISE software as a fixed point with selected accuracy based on the results of MATLAB simulation. Using the loop unrolling technique with different coefficients, an attempt is made to reduce the latency and increase the throughput. In contrast, increasing the unrolling factor leads to a decrease in the frequency of the CORDIC unit as well as a decrease in the number of resources. As a result, there is a trade-off between the unrolling factor and the frequency of the CORDIC unit. By investigating the different unrolling factors, it is shown that the loop unrolling technique with a factor of 4 has the highest throughput with the value of 5.777 MQRD/s and the lowest latency with the value of 173 ns. Moreover, it is shown that throughput and latency are improved by 42.52% and 73.74% respectively compared to the not optimized case. The proposed method is also scalable for different sizes of m×m complex channel matrices, where log2 m ∈ N.
Pardis Asghari, Alireza Zakariazadeh,
Volume 19, Issue 4 (December 2023)
Abstract
This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.
