A Novel Hybrid Fuzzy-JAYA Optimization Algorithm for Efficient ORPD Solution

Gamal, Mona; El‐Sehiemy, Ragab A.; Hasanien, Hany M.

doi:10.1109/access.2019.2955683

Cited by 18 publications

(7 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here

c

is described as the set of control variables that includes generator terminal voltages

{V}_G

, transformer tap position

t

, and shunt compensation

{Q}_C

1 …”

Section: Problem Descriptionmentioning

confidence: 99%

“…ORPD manages the reactive power by controlling the generator voltage, the position of the transformer tap, and the location of reactive sources to reduce the active power loss and voltage deviation. It should be controlled within a prescribed limit 1 . It is the most challenging, non‐linear, and non‐convex power system optimization issue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

View full text Add to dashboard Cite

In this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

show abstract

“…Here

c

is described as the set of control variables that includes generator terminal voltages

{V}_G

, transformer tap position

t

, and shunt compensation

{Q}_C

1 …”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…The maximum number of iterations is set to 350 and it is considered as the stopping criterion of the optimization process. JOA and PSO parameters are customized from [33]- [37].…”

Section: Parameter Estimation Of Single-phase and Three-phase Transformersmentioning

confidence: 99%

Parameter Estimation of Electric Power Transformers Using Coyote Optimization Algorithm With Experimental Verification

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms.INDEX TERMS Coyote, PSO, Jaya, single-phase transformer, transformer equivalent circuit.

show abstract

“…Metaheuristic techniques are more suitable than classic methods in solving non-linear optimization problems like ORPD. There are many metaheuristic optimization techniques were used to find the best solution for the ORPD problem such as; Whale Optimization Algorithm (WOA) [9],Particle Swarm Optimization (PSO) [10],Ant Lion Optimizer (ALO) [11], Improved Social Spider Optimization Algorithm (ISSO) [12], Improved Antlion Optimization Algorithm (IALO) [13],Genetic Algorithm (GA) [14], Ant Colony Optimizer (ACO) [15], Opposition-Based Gravitational Search Algorithm (OGSA) [16], Wind Driven Optimization Algorithm (WDO) [17], modified differential evolution algorithm (MDEA) [18], Specialized Genetic Algorithm (SGA) [19], evolutionary programming [20], comprehensive learning particle swarm optimization [21], fuzzy adaptive PSO (FAPSO) [22], seeker optimization algorithm (SOA) [23], cuckoo search algorithm (CA) [24] , Hybrid Evolutionary Programming (HEP) [25], harmony search algorithm [26], Teaching Learning-Based Optimization [27], biogeographybased optimization [28], modified sine cosine algorithm [29], water cycle algorithm [30], hybrid Fuzzy-Jaya optimizer [31].…”

Section: Iintroductionmentioning

confidence: 99%

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

et al. 2021

View full text Add to dashboard Cite

The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem. INDEX TERMS renewable energy; uncertainty; time-varying demand, optimal reactive power dispatch (ORPD); RAO algorithm, backward reduction algorithm

show abstract

A Novel Hybrid Fuzzy-JAYA Optimization Algorithm for Efficient ORPD Solution

Cited by 18 publications

References 56 publications

An enhanced arithmetic optimization algorithm for optimal control of reactive power

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Parameter Estimation of Electric Power Transformers Using Coyote Optimization Algorithm With Experimental Verification

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

Contact Info

Product

Resources

About