Solution of optimal reactive power dispatch problem using pathfinder algorithm

Yapıcı, Hamza

doi:10.1080/0305215x.2020.1839443

Cited by 38 publications

(17 citation statements)

References 44 publications

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“…It also contains adaptable parameters that dynamically regulate exploration operations. The following equations can be used to update the members and pathfinder 46 :

(}, \begin{array}{l} x_{i}^{t + 1} goodbreak= x_{i}^{t} goodbreak+ R_{1} goodbreak\times (x_{j}^{t} goodbreak- x_{i}^{t}) goodbreak+ R_{2} goodbreak\times (x_{p}^{t} goodbreak- x_{i}^{t}) goodbreak+ ε, i \geq 2 \\ x_{p}^{t + 1} goodbreak= x_{p}^{t} goodbreak+ 2 r_{3} (x_{p}^{t} goodbreak- x_{p}^{t - 1}) goodbreak+ A \\ R_{1} goodbreak= α r_{1}, R_{2} goodbreak= β r_{2}, α, β \in normalrand ([12]), r_{1}, r_{2}, \\ r_{3} \in normalrand ([01]), \\ A goodbreak= u_{2} \exp (goodbreak- 2 t / T), u_{2} goodbreak= normalrand ([- 1, 1]) \end{array})

where

x_{i}

and

x_{j}

are the position vectors of members i and j , respectively;

x_{p}

and

ε

are the pathfinder position vector and vibration coefficient, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Ali,

Zafar,

Tanveer

et al. 2023

Int J Imaging Syst Tech

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This study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

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“…It also contains adaptable parameters that dynamically regulate exploration operations. The following equations can be used to update the members and pathfinder 46 :

(}, \begin{array}{l} x_{i}^{t + 1} goodbreak= x_{i}^{t} goodbreak+ R_{1} goodbreak\times (x_{j}^{t} goodbreak- x_{i}^{t}) goodbreak+ R_{2} goodbreak\times (x_{p}^{t} goodbreak- x_{i}^{t}) goodbreak+ ε, i \geq 2 \\ x_{p}^{t + 1} goodbreak= x_{p}^{t} goodbreak+ 2 r_{3} (x_{p}^{t} goodbreak- x_{p}^{t - 1}) goodbreak+ A \\ R_{1} goodbreak= α r_{1}, R_{2} goodbreak= β r_{2}, α, β \in normalrand ([12]), r_{1}, r_{2}, \\ r_{3} \in normalrand ([01]), \\ A goodbreak= u_{2} \exp (goodbreak- 2 t / T), u_{2} goodbreak= normalrand ([- 1, 1]) \end{array})

where

x_{i}

and

x_{j}

are the position vectors of members i and j , respectively;

x_{p}

and

ε

are the pathfinder position vector and vibration coefficient, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…It also contains adaptable parameters that dynamically regulate exploration operations. The following equations can be used to update the members and pathfinder 46 :…”

Section: Path Finder Algorithmmentioning

confidence: 99%

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Ali,

Zafar,

Tanveer

et al. 2023

Int J Imaging Syst Tech

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show abstract

“…The leader whale guides the group's movements, ultimately converging towards the prey by encircling it to find the optimal solution in the search space. This process can be expressed mathematically in (10) and (11).…”

Section: Searching and Encircling Targetmentioning

confidence: 99%

Series and shunt FACTS controllers based optimal reactive power dispatch

Kar,

Parida,

Dash

2024

IJAPE

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Optimal reactive power dispatch involves the determination and management of reactive power resources in a power system to maintain voltage stability, improve power transfer capability, and minimize system losses. Reactive power is essential for maintaining voltage levels within acceptable limits and ensuring the reliable operation of electrical networks. The whale optimization algorithm (WOA) has been proposed to obtain the optimal location of flexible alternating current transmission system (FACTS) components. The efficacy of WOA is tested using conventional IEEE 14 and 30 bus test systems. Static var compensator (SVC) is used as shunt and the thyristor-controlled series capacitor (TCSC) as a series FACTS controller. The analysis is carried out for both the systems with and without FACTS controllers. Optimization techniques are applied to select the optimal control parameters. The suggested strategy is compared to other contemporary techniques such as particle swarm optimization (PSO) and grey wolf optimization (GWO). At various loading situations, the WOA-based technique outperforms other two techniques.

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“…The Modified PFA was proposed [40]. A and 𝜀 are the fluctuation and the vibration coefficients, which are modified for a better search, since both have the capability for random movements and transition between diversification and intensification.…”

Section: E Modified Pfa (Mpfa)mentioning

confidence: 99%

Optimum Reactive Power Dispatch Solution using Hybrid Particle Swarm Optimization and Pathfinder Algorithm

Adegoke¹,

Sun²

2022

IJC

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Optimum reactive power dispatch (ORPD) significantly impacts the operation and control of electrical power systems (EPS) due to its undeniable benefit in the economic operation and reliability of the systems. ORPD is a sub-problem of optimal power flow (OPF). The main aim is to reduce/minimize the real power loss. Among the swarm intelligence (SI) metaheuristic algorithms is particle swarm optimization (PSO), which has fast convergence speed and gives the optimum solution to a particular problem by moving the swarm in the intensification (exploitation) search space. Also, the pathfinder algorithm (PFA) mimics the collective movement of the swarms with a leading member. Therefore, combining the fast convergence of PSO with PFA to form a hybrid technique is considered a viable approach in this study to avoid decreasing PFA searchability when the dimension of the problem increases. In this article, a hybrid algorithm based on a particle swarm optimization and pathfinder algorithm (HPSO-PFA) is proposed for the first time to study the combination of the control variables (generator voltage, transformer tap, and sizing of reactive compensation to obtain the objective function (total real power loss). The proposed method is tested on the IEEE 30 and 118 bus systems. The losses were reduced to 16.14262 MW and 107.2913 MW for the IEEE 30 and 118 test systems. Furthermore, the percentage (%) reduction for the IEEE 30 and 118 test systems are 9.8% and 19.25%, respectively. The result demonstrates the performance of HPSO-PFA gives a better solution than the other algorithms.

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Solution of optimal reactive power dispatch problem using pathfinder algorithm

Cited by 38 publications

References 44 publications

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Series and shunt FACTS controllers based optimal reactive power dispatch

Optimum Reactive Power Dispatch Solution using Hybrid Particle Swarm Optimization and Pathfinder Algorithm

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