Real and Reactive Powers Decomposition Optimal Power Flow Using Particle Swarm Optimization

Rojanaworahiran, Kanatip; Chayakulkheeree, Keerati

doi:10.1109/icpei47862.2019.8945022

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…In part of the study, PSO [10] and SOA [33] are employed to resolve the OPF problem in the IEEE 30-bus configuration network. The network has 6 thermal units connected with nodes 1, 2, 5, 8, 11, and 13, 41 branches, four transformers, 24 loads, and nine capacitors.…”

Section: Resultsmentioning

confidence: 99%

“…Luckily, meta-heuristic algorithms have recently demonstrated themselves to be highly affordable and the most trusted computing method to deal with optimization problems, including OPF problems. For instant, different meta-heuristic algorithms have been applied to solve the OPF problems such as backtracking search algorithm (BSA) [1], genetic algorithm (GA) [2], Harris Hawks optimization (HHO) [3], [4], grey wolf optimizer algorithm (GWOA) [5], AMTPG-Jaya algorithm [6], differential evolution (DE) [7], [8], effective Cuckoo search algorithm (ECSA) [9], particle swarm optimization (PSO) [10], Gorilla troops algorithm (GTA) [11], modified coyote optimization algorithm (MCSA) [12], black hole optimization (BHO) [13], the hybrid method of Cuckoo search algorithm (MCOA) and sunflower optimization (SFO) [14], improved multi-objective multi-verse algorithm (IMOMVA) [15], firefly krill herd algorithm (FHHA) [16], improved moth-flame optimization (IMFO) [17], antlion optimization (ALO) and its improved version [18], [19], marine predator algorithm (MPA) [20], social spider algorithm (SSA) [21], slime mould algorithm (SMA) [22], whale optimization algorithm (WOA) [23], and golden ratio optimization (GRO) [24]. The effectiveness of meta-heuristic algorithms has shown a huge leap forward when compared with conventional computing methods, such as the Newton-Raphson technique [25], in terms of time response, robustness, and precision degree of the final results.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

“…New solutions of PSO are updated as (10) and (11): In (10) and (11), 𝑉𝑒 𝑖 𝑛𝑒𝑤 and 𝑋 𝑖 𝑛𝑒𝑤 are new velocities and positions belonging to particle i. 𝑉𝑒 𝑖 and 𝑋 𝑖 are the present velocities and positions belonging to particle i. E1 and E2 are accelerating ratios. 𝛾 1 and 𝛾 2 are the random value between 0 and 1.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Skill optimization algorithm for solving optimal power flow problem

Hien,

Duong,

Pham

2024

Bulletin EEI

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This research presents the implementation of a modern meta-heuristic algorithm called the skill optimization algorithm (SOA) to solve the optimal power flow problem (OPF). An IEEE 30-bus transmission system is selected to test the real performance of SOA. The main objective function of the study is to minimize the total fuel cost (TFC) of all thermal units. To clarify the high performance of SOA, a classical meta-heuristic named particle swarm optimization (PSO) is also applied for comparison. All results reached by SOA are compared with those of PSO on different criteria. Particularly, SOA has reached smaller cost than PSO by $1.04, equivalent to 0.13% of PSO’s TFC. Furthermore, SOA has reached a more stable performance by finding better average and maximum TFC over fifty runs. The evaluation of these criteria indicates that SOA completely outperforms PSO. Besides, the optimal solution reached by SOA satisfies all considered constraints with zero violation of the dependent variables. Therefore, SOA is highly suggested to handle the OPF problem.

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Section: Resultsmentioning

confidence: 99%

Section: Issn: 2302-9285 mentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Skill optimization algorithm for solving optimal power flow problem

Hien,

Duong,

Pham

2024

Bulletin EEI

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“…The equations used for designing the current loop control on the d-axis can also be used for designing the current loop control on the q-axis. The equations for calculating K pq and K iq are presented in (9).…”

Section: ) Current Loop Controlmentioning

confidence: 99%

Design and Optimization of Control System for More Electric Aircraft Power Systems Using Adaptive Tabu Search Algorithm Based on State-Variables-Averaging Model

Phosung,

Areerak,

Areerak

2024

IEEE Access

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An important reflection of the overall efficiency, reliability, and passenger safety of a more electric aircraft (MEA) is the output performance of its electrical power system (EPS) controller. This output performance encompasses the rise time, setting time, and percent undershoot of the voltage across the capacitor bank. Therefore, this article presents an optimal controller design using an artificial intelligence method called the adaptive tabu search (ATS) algorithm. The state-variables-averaging model is applied with the ATS algorithm to reduce computational time. Moreover, stability analysis based on the eigenvalue theorem is used as the penalty condition during the searching process to avoid unstable operation. The output performance of the proposed controller design is superior to that of the conventional controller design. All design results are verified by good agreement with MATLAB and hardware-in-theloop (HIL) simulations.INDEX TERMS More electric aircraft, vector control, state-variables-averaging model, adaptive tabu search algorithm. I. INTRODUCTIONAccording to published concepts for more electric aircraft [6]. aircraft performance optimization, flight reliability improvement, and passenger safety improvement are essential tasks. Altering the MEA controller design is an approach with strong potential to accomplish these tasks. Artificial intelligence (AI) techniques are required to achieve the optimal output performance of the MEA's electrical power system (EPS) controller in terms of the rise time, setting time, and percent undershoot of the voltage across the capacitor bank. AI can be used for controller design and many other functions. For example, AI has been applied to the design of active power filters for the adaptive tabu search (ATS) algorithm [7] and genetic algorithm [8], power flow optimization based on particle swarm optimization [9], ant colony search algorithm [10], antenna array design using artificial bee colony algorithm [11], and the application of the ATS algorithm to instability mitigation [12]. However, one of the crucial problems that arises when applying AI techniques to the EPS of MEA is the simulation time. This is because the simulation of EPS using software packages such as PSIM and MATLAB causes huge computational time due to switching behavior. Thus, a time-invariant model is necessary and sufficient for the controller design. There are several reasonable methods for deriving the timeinvariant model of EPS. The generalized state-space averaging modeling technique [13], [14] has been universally used for single-phase rectifiers of AC distribution systems and the power converters of DC distribution systems. The direct quadrature (DQ) approach [1], [2], [6], [12], [15], [16] is suitable for power converters in three-phase AC distribution systems. The nonlinear average-value method modeling technique [17] has been applied to analyze 6-and 12-pluse diode rectifiers.Based on a literature review, the AI technique called the ATS algorithm is selected for use in the present stud...

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“…Previous studies have successfully applied meta-heuristic algorithms for reaching optimal solution of OPF problems with different objective functions. These algorithms are comprised of conventional and modified versions such as Particle swarm optimization (PSO) and its improved version (Rojanaworahiran et al, 2021, Tran et al, 2016, Harris hawk optimization (HHO) (Birogul 2016), Grey wolf optimization (Ladumor et al, 2017), Self-learning Cuckoo search algorithm (SLCSA) (Nguyen et al,…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

Le²,

Huynh

et al. 2023

Int. J. Renew. Energy Dev.

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This paper implements two novel meta-heuristic algorithms, including the Coati optimization algorithm (COA) and War strategy optimization (WSO) for determining the optimal solutions to the optimal power flow problem incorporating the use of wind turbines. Two objective functions are considered in this study, including minimizing the entire electricity generation cost (EEGC) with the value point effect and minimizing the voltage fluctuation index (VFI). IEEE 30-bus system is chosen to conduct the whole study and validate the efficiency of the two applied methods. Furthermore, DFIG wind turbines are used in grids with varying power output and power factor ranges. The comparison of the results obtained from the two methods in all case studies reveals that WSO is vastly superior to COA in almost all aspects. In addition, the positive contributions of wind turbines to the EEGE and VFI while they are properly placed in the grid are also clarified by using WSO. As a result, WSO is acknowledged as a highly effective search method, and we strongly recommend using WSO for dealing with such OPF problems considering the presence of renewable energy sources.

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Real and Reactive Powers Decomposition Optimal Power Flow Using Particle Swarm Optimization

Cited by 3 publications

References 5 publications

Skill optimization algorithm for solving optimal power flow problem

Skill optimization algorithm for solving optimal power flow problem

Design and Optimization of Control System for More Electric Aircraft Power Systems Using Adaptive Tabu Search Algorithm Based on State-Variables-Averaging Model

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

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