Belkacem Mahdad

Bio: Belkacem Mahdad is an academic researcher. The author has contributed to research in topics: AC power & Electric power system. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

New strategy based modified Salp swarm algorithm for optimal reactive power planning: a case study of the Algerian electrical system (114 bus)

Abstract: Optimal reactive power planning is an important task for experts and industrials to ensure the reliability of modern power systems. Actually, the structure of practical power systems becomes dynamic and characterised by uncertainty in load and non-linear characteristic of various elements of power systems such as constraints associated to thermal units, constraints associated with FACTS devices and renewable sources. This study introduces an intelligent strategy based new metaheuristic named Salp swarm algorithm (SSA) to improve the solution of reactive power dispatch by optimising the total power loss, the total voltage deviation individually and simultaneously considering static VAR compensators (SVCs). To improve the efficiency of the original algorithm in solving large test systems, a sub SSA is formed to optimise the various objective functions based on a grouped control variable. In this study, four grouped swarms named SSA_PG for active power, SSA_VG for voltages, SSA_Ti for Tap transformers, and SSA_SVC for SVCs are formed to operate in a flexible structure to minimise a specified objective function. The proposed intelligent planning strategy validated on the IEEE 30 bus and to the large electrical test system 114 Bus of the Algerian network at normal condition and considering critical situations such as margin loading stability and contingency. Results found using the proposed strategy compared to those cited recently in the literature proves its particularity in terms of solution quality and convergence characteristics.

Voltage Constrained Reactive Power Planning Problem for Reactive Loading Variation Using Hybrid Harris Hawk Particle Swarm Optimizer

Abstract: This study proposes the application of an efficient and hybrid meta-heuristic algorithm of Harris Hawk-Particle Swarm Optimizer (HHOPSO) for solving voltage constrained reactive power planning (VCR...

An improved salp optimization algorithm inspired by quantum computing

Abstract: Salp Swarm Algorithm (SSA) is a novel optimization algorithm which is widely used in engineering problems An improved SSA inspired by quantum computing is proposed in this paper The principles of quantum computing, such as qubits and quantum states, are introduced into the original SSA in order to overcome the defect of trapping into local optimum easily Instead of updating the salp position directly, the quantum angle related to the quantum state is updated to increase the diversity of states Two multidimensional benchmark functions are used to verify the proposed improved SSA, the result shows that the introduction of quantum computing can successfully prevent the SSA from falling into the local optimum and increase the accuracy