Optimal Location of SVC using Particle Swarm Optimization and Voltage Stability Indexes

Dazahra, Mohamed Nouh; Elmariami, Faissal; Belfqih, Abdelaziz; Boukherouaa, Jamal

doi:10.11591/ijece.v6i6.11921

Cited by 15 publications

(8 citation statements)

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“…So as to transform the exploration of PSO in a real space dimensions to binary space dimensions, sigmoid transformation is applied to the velocity element to force the velocities within [0, 1], and force the component values of the positions of agents to be (0 s or 1 s). So, the equations of BPSO are: is the inertia weight and it is reduce linearly from (0.9 to 0.4) at each iteration, and it can be express as follows [26][27]:…”

Section: Constrainsmentioning

confidence: 99%

A new hybrid algorithm for solving distribution network reconfiguration under different load conditions

Neda

2020

IJEECS

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Distribution Network Reconfiguration (DNR) is a significant problem for keeping the network running under normal conditions. In this study, for preventing the premature convergence issue, also to improving the searching ability of the Binary Particle Swarm Optimization (BPSO) algorithm, chaotic strategy is incorporating with BPSO algorithm to create a new hybrid algorithm called Chaotic BPSO (CBPSO). Undeniably, the chaotic strategy enables the hybrid CBPSO algorithm to slip from the local optima and also to reach optimal solution in fewer number of iterations compare to BPSO due to the remarkable behavior and ergodic of the chaos strategy than random search in BPSO algorithm. The CBPSO algorithm is presented as a advantageous optimization tool for solving DNR. In this problem, decreasing of real power loss () is an objective function while node voltage, system radially and line current have been utilized as a constrains of the system. The search space in this problem for the presented technique is a group of lines (switches) that are normally opened or closed. Two types of loads are presented: the constant and variable loads for testing the efficacy of the CBPSO method for tackling DNR problem when the load is changes. The proposed technique is implemented on IEEE Node system by utilizing R2013b software for verifying the efficacy of CBPSO technique. The simulation results confirm that technique has high ability in reducing and raising the voltage profile of the grid compared to and other procedures in the literature.

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Section: Constrainsmentioning

confidence: 99%

A new hybrid algorithm for solving distribution network reconfiguration under different load conditions

Neda

2020

IJEECS

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“…Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm that has been applied widely in many optimization areas [29][30][31]. PSO is inspired by the social behaviors of animals like fish schooling and bird flocking [29].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

Anam¹,

Al-Jumaily²

2018

IJECE

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Myoelectric pattern recognition (MPR) is used to detect user's intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and knearest neighbor (kNN).

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“…Now the Genetic algorithm is the most widely used optimization method in power supply system optimization [5][6][7][8]. The other population-based Swarm Intelligence algorithms are used successfully for the optimization of reactive power control, reactive power dispatch, and other power system optimization problems: Particle Swarm optimization [9][10][11], Fire Fly optimization [12]; Crow search [13], Cuckoo search [14], Bee algorithm [15].…”

Section: Introductionmentioning

confidence: 99%

Swarm algorithms in dynamic optimization problem of reactive power compensation units control

Manusov

Matrenin

Khasanzoda

2019

IJECE

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Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.

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Optimal Location of SVC using Particle Swarm Optimization and Voltage Stability Indexes

Cited by 15 publications

References 0 publications

A new hybrid algorithm for solving distribution network reconfiguration under different load conditions

A new hybrid algorithm for solving distribution network reconfiguration under different load conditions

Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

Swarm algorithms in dynamic optimization problem of reactive power compensation units control

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