This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase system.The control scheme employs the fractional order,proportional-integral-derivative(FOPID)...This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase system.The control scheme employs the fractional order,proportional-integral-derivative(FOPID)controller to improve on the DVR performance in order to enhance the power quality in terms of the response time,steady-state error and total harmonic distortion(THD).The result obtained was compared with fractional order,proportionalintegral(FOPI),proportional-integral-derivative(PID)and proportional-integral(PI)controllers in order to show the effectiveness of the proposed DVR control scheme.A water cycle optimization algorithm(WCA)was utilized to find the optimal set for all the controller gains.They were used to solve four power quality issues;balanced voltage sag,balanced voltage swell,unbalanced voltage sag,and unbalanced voltage swell.It showed that one set of controller gain obtained from the WCA could solve all the power quality issues while the others in the literature needed an individual set of optimal gain for each power quality problem.To prove the concept,the proposed DVR algorithm was simulated in the MATLAB/Simulink software and the results revealed that the four optimal controllers can compensate for all the power quality problems.A comparative analysis of the results in various aspects of their dynamic response and%THD was discussed and analyzed.It was found that PID controller yields the most rapid performance in terms of average response time while FOPID controller yields the best performance in term of average%steady-state error.FOPI controller was found to provide the lowest THD percentage in the average%THD.FOPID did not differ much in average response from the PID and average%THD from FOPI;however,FOPID provided the most outstanding average steady-state error.According to the CBMA curve,the dynamic responses of all controllers fall in the acceptable power quality area.The total harmonic distortion(THD)of the compensated load voltage from all the controllers were within the 8%limit in accordance to the IEEE std.519-2014.展开更多
In this research article,we introduce a numerical investigation through artificial neural networks(ANN)integrated with evolutionary algorithm especially Archimedean optimization algorithm(AOA)hybrid with the water cyc...In this research article,we introduce a numerical investigation through artificial neural networks(ANN)integrated with evolutionary algorithm especially Archimedean optimization algorithm(AOA)hybrid with the water cycle algorithm(WCA)to address and enhance the analysis of the non-linear magneto-hydrodynamic(MHD)Jeffery-Hamel problem,especially stretching/shrinking in convergent and divergent channel.This combined technique is referred to as ANN-AOA-WCA.The complex nonlinear magneto-hydrodynamic Jeffery-Hamel problem based partial differential equations are transformed into non-linear system of ordinary differential equations for velocity and temperature.We formulate the ANN based fitness function to find the solution of non-linear differential.Subsequently,we employ a novel hybridization of AOA and WCA(AOA-WCA)to optimize the ANN based fitness function and identify the best optimal weights and biases for ANN.To demonstrate the effectiveness and versatility of our proposed hybrid method,we explore MHD models across a range of Reynolds numbers,channel angles and stretchable boundary value leading to the development of two distinct cases.ANN-AOA-WCA numerical results closely align with reference solutions(NDSOLVE)and the absolute error between NDSOLVE and ANN-AOA-WCA is up to 3.35´10^(-8),particularly critical to the understanding of stretchable convergent and divergent channel.Furthermore,to validate the ANN-AOA-WCA technique,we conducted a statistical analysis over 150 independence runs to find the fitness value.展开更多
Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in com...Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations research.Indeed,metaheuristic-based algorithms are a sub-field of AI.This study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution.Since the parameters are stochastic,the corresponding constraints are probabilistic.They are converted into deterministic constraints using the stochastic programming approach.In this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems.WCA is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous system.It is concluded that WCA presents better results when compared with the neural network algorithm.展开更多
基金This Research was Financially Supported by Faculty of Engineering,Mahasarakham University(Grant year 2021).
文摘This paper proposes a low complexity control scheme for voltage control of a dynamic voltage restorer(DVR)in a three-phase system.The control scheme employs the fractional order,proportional-integral-derivative(FOPID)controller to improve on the DVR performance in order to enhance the power quality in terms of the response time,steady-state error and total harmonic distortion(THD).The result obtained was compared with fractional order,proportionalintegral(FOPI),proportional-integral-derivative(PID)and proportional-integral(PI)controllers in order to show the effectiveness of the proposed DVR control scheme.A water cycle optimization algorithm(WCA)was utilized to find the optimal set for all the controller gains.They were used to solve four power quality issues;balanced voltage sag,balanced voltage swell,unbalanced voltage sag,and unbalanced voltage swell.It showed that one set of controller gain obtained from the WCA could solve all the power quality issues while the others in the literature needed an individual set of optimal gain for each power quality problem.To prove the concept,the proposed DVR algorithm was simulated in the MATLAB/Simulink software and the results revealed that the four optimal controllers can compensate for all the power quality problems.A comparative analysis of the results in various aspects of their dynamic response and%THD was discussed and analyzed.It was found that PID controller yields the most rapid performance in terms of average response time while FOPID controller yields the best performance in term of average%steady-state error.FOPI controller was found to provide the lowest THD percentage in the average%THD.FOPID did not differ much in average response from the PID and average%THD from FOPI;however,FOPID provided the most outstanding average steady-state error.According to the CBMA curve,the dynamic responses of all controllers fall in the acceptable power quality area.The total harmonic distortion(THD)of the compensated load voltage from all the controllers were within the 8%limit in accordance to the IEEE std.519-2014.
文摘In this research article,we introduce a numerical investigation through artificial neural networks(ANN)integrated with evolutionary algorithm especially Archimedean optimization algorithm(AOA)hybrid with the water cycle algorithm(WCA)to address and enhance the analysis of the non-linear magneto-hydrodynamic(MHD)Jeffery-Hamel problem,especially stretching/shrinking in convergent and divergent channel.This combined technique is referred to as ANN-AOA-WCA.The complex nonlinear magneto-hydrodynamic Jeffery-Hamel problem based partial differential equations are transformed into non-linear system of ordinary differential equations for velocity and temperature.We formulate the ANN based fitness function to find the solution of non-linear differential.Subsequently,we employ a novel hybridization of AOA and WCA(AOA-WCA)to optimize the ANN based fitness function and identify the best optimal weights and biases for ANN.To demonstrate the effectiveness and versatility of our proposed hybrid method,we explore MHD models across a range of Reynolds numbers,channel angles and stretchable boundary value leading to the development of two distinct cases.ANN-AOA-WCA numerical results closely align with reference solutions(NDSOLVE)and the absolute error between NDSOLVE and ANN-AOA-WCA is up to 3.35´10^(-8),particularly critical to the understanding of stretchable convergent and divergent channel.Furthermore,to validate the ANN-AOA-WCA technique,we conducted a statistical analysis over 150 independence runs to find the fitness value.
基金This work was funded by the Deanship of Scientific Research at King Saud University through research Group Number RG-1436-040.
文摘Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations research.Indeed,metaheuristic-based algorithms are a sub-field of AI.This study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution.Since the parameters are stochastic,the corresponding constraints are probabilistic.They are converted into deterministic constraints using the stochastic programming approach.In this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems.WCA is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous system.It is concluded that WCA presents better results when compared with the neural network algorithm.
