Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Neda, Omar Muhammed

doi:10.11591/ijece.v10i6.pp6111-6121

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…An individual represents the candidate solution, and every group of individuals represents a swarm. Kennedy and Eberhart were first enhanced and developed this algorithm in 1995 [23]. Each individual in the PSO algorithm has the best position discovered by the experience of the individual itself, and it is stored in a memory called the local best position (𝑃 𝑏𝑒𝑠𝑡 ).…”

Section: Optimizationmentioning

confidence: 99%

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

Neda¹,

Ma’arif²

2022

IJRCS

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The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.

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Section: Optimizationmentioning

confidence: 99%

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

Neda¹,

Ma’arif²

2022

IJRCS

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“…Each individual in the PSO algorithm has local best position (𝑃 𝑏𝑒𝑠𝑡 ), and global best position (𝐺 𝑏𝑒𝑠𝑡 ) [23]. The velocity (𝑉 𝑖 ) and position (𝑋 𝑖 ) from 𝑃 𝑏𝑒𝑠𝑡 to 𝐺 𝑏𝑒𝑠𝑡 of the agents will be changed by utilizing in ( 7) and ( 8) [24], [25]. The simple flowchart of the PSO process is exposed in Figure 1.…”

Section: Pso Algorithmmentioning

confidence: 99%

Optimal distributed generation placement using artificial intelligence for improving active radial distribution system

Jumaa

Neda²,

Mhawesh

2021

Bulletin EEI

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There are several profits of distributed generator (DG) units which are believed for improving the safety of the distribution power grids. However, these profits can be maximized by ensuring optimum sizing and positioning of DG units because an arbitrary location of DG units may adversely affect and jeopardize power grids which could contribute to maximising of power loss and degradation of the voltage profile. Therefore, several approaches were suggested to ensure optimum position and size of DGs. The primary aim of this article is for establishing technique for optimum scheduling and operating of DG to lessen power loss, revamp voltage profile and overall network reliability. Artificial intelligence method called particle swarm optimization (PSO) is utilized for finding the best site and size of DG to lessen power loss and boost the voltage profile. In this paper, IEEE 33 distribution system is utilized to display applicability of PSO. The results of the PSO are compared with the results gotten by other methods in the literature. Finally, the results show that the PSO is superior than the other methods.

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“…Oscillations in the power system are caused by variations in operating points due to changes in load and short circuits [9], [10]. The frequency oscillation must be regulated and controlled according to the required limits [11].…”

Section: Introductionmentioning

confidence: 99%

Dingo optimization algorithm for designing power system stabilizer

Aribowo

Suprianto

Kartini

et al. 2022

IJEECS

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The dingo optimization algorithm (DOA) adopts the social life of dingo dogs. The dingo is a breed of ancient dog originating from Australia. Dingo hunting strategies such as assault with persecution, flocking, and scavenging behavior became the inspiration for DOA. In this paper, DOA is applied to a power system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a single-machine infinite bus (SMIB). DOA is used to obtain optimal parameters for PSS. The damping controller is designed for optimal lead-lag control. To obtain the performance of the DOA method, the results were compared with the uncontrolled method, conventional PSS, Whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Simulation using MATLAB with three different operating conditions, namely light load (20%), medium load (50%) and high load (100%). From the simulation using MATLAB with SMIB modeling, it was found that the application of the DOA method on PSS has the ability to reduce the average undershoot value by 28.16% and reduce the average undershoot value to 65.57% compared to the conventional PSS method.

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Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Cited by 11 publications

References 11 publications

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

Optimal distributed generation placement using artificial intelligence for improving active radial distribution system

Dingo optimization algorithm for designing power system stabilizer

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