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All Journal International Journal of Electrical and Computer Engineering
M. M. H. Elroby
Ain Shams University
Computer Science & IT Electrical & Electronics Engineering

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FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints M. M. H. Elroby; S. F. Mekhamer; H. E. A. Talaat; M. A. Moustafa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp945-955

Abstract

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system.
Generalized optimal placement of PMUs considering power system observability, communication infrastructure, and quality of service requirements M. M. H. Elroby; S. F. Mekhamer; H. E. A. Talaat; M. A. Moustafa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1428.071 KB) | DOI: 10.11591/ijece.v10i3.pp2824-2841

Abstract

This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems.
Population based optimization algorithms improvement using the predictive particles M. M. H. Elroby; S. F. Mekhamer; H. E. A. Talaat; M. A. Moustafa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1397.809 KB) | DOI: 10.11591/ijece.v10i3.pp3261-3274

Abstract

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.
Co-Authors H. E. A. Talaat M. A. Moustafa Hassan S. F. Mekhamer