Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Electronics and Electrical Engineering
D-index
38
Citations
8,968
211
World Ranking
1995
National Ranking
6

- Control theory
- Electrical engineering
- Mathematical optimization

M. A. Abido spends much of his time researching Electric power system, Control theory, Mathematical optimization, Robustness and Optimization problem. His Electric power system study integrates concerns from other disciplines, such as Time domain, Hybrid system, Control engineering and Range. His Control theory study incorporates themes from Particle swarm optimization and Power system simulation.

His work on Evolutionary algorithm and Multi-objective optimization as part of general Mathematical optimization research is frequently linked to Standard test, bridging the gap between disciplines. In his research on the topic of Robustness, Minification is strongly related with Control variable. The Optimization problem study combines topics in areas such as Genetic algorithm and Static VAR compensator.

- Optimal power flow using particle swarm optimization (833 citations)
- Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization (658 citations)
- Environmental/economic power dispatch using multiobjective evolutionary algorithms (636 citations)

His scientific interests lie mostly in Control theory, Electric power system, Mathematical optimization, Control theory and Robustness. He combines subjects such as Control engineering, Optimization problem, Particle swarm optimization and Voltage with his study of Control theory. His Electric power system study combines topics in areas such as Time domain, Genetic algorithm, Singular value decomposition and Controllability.

His study in Mathematical optimization is interdisciplinary in nature, drawing from both Fuzzy logic and Power flow. His Robustness research incorporates themes from Control variable, Algorithm, Power system simulation and System parameter. His Evolutionary algorithm research includes themes of Fuzzy set, Pareto principle and Differential evolution.

- Control theory (59.93%)
- Electric power system (39.38%)
- Mathematical optimization (26.37%)

- Control theory (59.93%)
- Control theory (20.21%)
- Fault (13.01%)

M. A. Abido focuses on Control theory, Control theory, Fault, Electric power system and Voltage. His work in the fields of Control theory, such as Torque, overlaps with other areas such as Model predictive control. His work deals with themes such as Genetic algorithm, Optimization problem and Compensation, which intersect with Electric power system.

His Optimization problem research is multidisciplinary, incorporating perspectives in Nonlinear programming, Particle swarm optimization, Swarm behaviour, Sensitivity and Robustness. The study incorporates disciplines such as Mathematical optimization, Integer programming and Spring in addition to Robustness. His studies deal with areas such as Phasor, Redundancy and Observability as well as Mathematical optimization.

- A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems (26 citations)
- Efficient Predictive Torque Control for Induction Motor Drive (17 citations)
- An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch (16 citations)

- Electrical engineering
- Control theory
- Artificial intelligence

M. A. Abido mainly investigates Control theory, Fault, Particle swarm optimization, Model predictive control and Induction motor. His research in Control theory intersects with topics in Voltage source and Transient. In his work, Electric motor and Fault detection and isolation is strongly intertwined with Stator, which is a subfield of Fault.

M. A. Abido interconnects Distributed computing and Robustness in the investigation of issues within Particle swarm optimization. The various areas that M. A. Abido examines in his Robustness study include Phasor, Electric power system, Nonlinear programming, Swarm behaviour and Sensitivity. M. A. Abido has researched Mathematical optimization in several fields, including Redundancy and Observability.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Optimal power flow using particle swarm optimization

M.A. Abido.

International Journal of Electrical Power & Energy Systems **(2002)**

1328 Citations

Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization

M. A. Abido.

IEEE Power & Energy Magazine **(2002)**

972 Citations

Environmental/economic power dispatch using multiobjective evolutionary algorithms

M.A. Abido.

IEEE Transactions on Power Systems **(2003)**

935 Citations

A novel multiobjective evolutionary algorithm for environmental/economic power dispatch

M.A. Abido.

Electric Power Systems Research **(2003)**

481 Citations

Optimal multiobjective design of robust power system stabilizers using genetic algorithms

Y.L. Abdel-Magid;M.A. Abido.

IEEE Transactions on Power Systems **(2003)**

467 Citations

Multiobjective particle swarm optimization for environmental/economic dispatch problem

M.A. Abido.

Electric Power Systems Research **(2009)**

431 Citations

Optimal power flow using differential evolution algorithm

A.A. Abou El Ela;M.A. Abido;S.R. Spea.

Electric Power Systems Research **(2010)**

429 Citations

A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch

M.A. Abido.

International Journal of Electrical Power & Energy Systems **(2003)**

423 Citations

Optimal Power Flow Using Tabu Search Algorithm

M. A. Abido.

Electric Power Components and Systems **(2002)**

409 Citations

Simultaneous stabilization of multimachine power systems via genetic algorithms

Y.L. Abdel-Magid;M.A. Abido;S. Al-Baiyat;A.H. Mantawy.

IEEE Transactions on Power Systems **(1999)**

398 Citations

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