Particle Swarm Optimization Based Optimal Reactive Power Dispatch for Power Distribution Network with Distributed Generation

Oo, Khine Zin

doi:10.11648/j.ijepe.20170604.12

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…In connection with the economical dispatch of power, optimal power flow (OPF) was defined in 1962 by Carpentier [7,8]. ORPD is a particular case of OPF, the goal of which is controlling continuous and discrete variables while adjusting generator voltage set points and transformer tap settings, and improving the voltage profile and reactive compensation, to reduce power loss [9,10]. One ORPD problem has a large and significant impact on the economical and secure operation of power systems.…”

Section: Introductionmentioning

confidence: 99%

“…The ORPD presents itself as a challenging nonlinear optimization problem, incorporating a combination of equality and inequality constraints. Traditional methods have shown inefficiency in solving nonlinear problems; therefore, metaheuristic algorithms such as the GA [9,10], PSO [11,12], OGSA [13], HBMO [14], HFA [15], and CFA [16] are preferred for solving the ORPD problem. These algorithms are known for their global search capability, robustness, and convergence to near-optimal solutions, making them well-suited for solving ORPD problems.…”

Section: Introductionmentioning

confidence: 99%

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A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Varan,

Erduman,

Menevşeoğlu

2023

Energies

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Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Varan,

Erduman,

Menevşeoğlu

2023

Energies

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Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

Lenin

2022

Technol Econ Smart Grids Sustain Energy

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This paper proposes Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm for genuine loss lessening. Important goals of the paper are Power fidelity extension, power eccentricity minimization and genuine loss lessening. The leading stimulus is in the sculpting of Repositioning -Peripatetic and argumentative actions of Ruddy turnstone. Ruddy turnstone will guzzle wiretaps, young insect and it subsists in bundling style. The constellation of Ruddy turnstone, which mobile from one place to alternate in the sequence of repositioning and the fresh investigation agent position is to evade the smash amongst their contiguous Ruddy turnstone. In the proposed Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Ruddy turnstone Optimization Algorithm approach enhances Extreme Learning Machine features to determine an optimal skeleton of Extreme Learning Machine for enhanced canons. In Chaotic based Ruddy turnstone optimization (CRTO) algorithm Exploration and Exploitation are augmented. In Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, features emulate the analogous performance with the certain stage as they route in a credible powdered of median. Opposition based Ruddy turnstone optimization (ORTO) Algorithm employs Laplace distribution to enhance the exploration skill. Then examining the prospect to widen the exploration, a new method endorses stimulating capricious statistics used in formation stage regulator factor in Ruddy turnstone Optimization Algorithm. In the projected chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm, the transaction of erratic figures is completed with the irrational digits enthused by Laplace distribution to amplify the support of the probability of formation level inside the exploration zone. Proposed Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm are corroborated in Garver's 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system -WDN 220 KV (Unified Egyptian Transmission Network (UETN)). Loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained.

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Comparative Analysis for Loss Control in Power Transmission System via Composite Compensation Scheme

Ismail,

Musirin,

Mansor

et al. 2024

2024 IEEE 4th International Conference in Power Engineering Applications (ICPEA)

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Particle Swarm Optimization Based Optimal Reactive Power Dispatch for Power Distribution Network with Distributed Generation

Cited by 6 publications

References 11 publications

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

Comparative Analysis for Loss Control in Power Transmission System via Composite Compensation Scheme

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