Study On The Novel Metaheuristic Intelligent Optimization Algorithm And Its Application In Power System

At present,the power system is becoming more and more complex and a large number of distributed renewable energy generations penetrate.Optimal installation of flexible AC transmission systems(FACTS)devices and parameter identification of solar photovoltaic models are two key technologies in the field of power system,which are of great significance for building a safe,efficient,green,low-carbon and sustainable modern energy system in China's energy transformation.Therefore,researching and exploring novel metaheuristic intelligent optimization algorithm and its application in power system has a very important theoretical research value and practical significance.In this dissertation,two key scientific and technological problems in the field of power system,namely the problem of optimal installation of FACTS devices and the problem of parameter identification of solar photovoltaic models are taken as the starting point,and two novel metaheuristic intelligent optimization algorithms,namely cuckoo search algorithm(CSA)and symbiotic organisms search algorithm(SOS)are emphatically studied and analyzed.From the point of view of performance improvement and engineering application of the two algorithms,this dissertation makes full use of cross-disciplinary research,focusing on the application of CSA and SOS and their improved algorithms in the two key scientific problems mentioned above in the field of power system.The major work and innovation are listed as follows:(1)For the problem to maximize power system static security in terms of branch loading and voltage level under normal operation and even the most critical single line contingency condition,a hybrid approach is proposed to find out the optimal locations and settings of two classical types of FACTS devices,namely thyristor-controlled series compensator(TCSC)and static var compensator(SVC)for solving this problem.Our proposed approach requires a two-step strategy.Firstly,In order to reduce the search space for solution to the problem,the min cut algorithm(MCA)and the tangent vector technique(TVT)are applied to determine the proper candidate locations of TCSC and SVC,respectively.The number of lines and buses which need to be investigated to determine the best locations of TCSC and SVC will be significantly decreased.Hence,the computing burden of CSA to solve the problem will be lessened in the following step,and then the CSA is employed for solving this problem via simultaneously optimizing the locations and settings for installations of TCSC and SVC.Finally,the results from the IEEE test systems show that the proposed hybrid approach is capable of finding out the best locations and settings of TCSC and SVC in such effective way for enhancing power system static security by removing or alleviating the overloads and voltage violations under normal operation and even the most critical single line contingency condition.(2)A novel CSA with quasi-oppositional population initialization strategy called QOPIS-CSA is proposed to solve the problem of identifying the parameter of solar cell model based on measured current versus voltage data of real solar cell.Firstly,to speed up the standard CSA convergence rate and improve its solution accuracy,the QOPIS-CSA is proposed by applying quasi-opposition based learning(QOBL)strategy in the population initialization stage of standard CSA.And then,the proposed QOPIS-CSA has been verified on the different solar cell models,i.e.,the single diode and double diode models.Experimental results and comparisons with other approaches applied in the related literature confirm the excellent capability of the proposed QOPIS-CSA.(3)An improved SOS algorithm called ImSOS is proposed for parameter identification of PV module models.Firstly,to enhance the performance of original SOS,a novel improved SOS algorithm,named as ImSOS is proposed.In ImSOS,a quasi-reflection-based learning(QRBL)scheme is employed in the population initialization step of original SOS.Moreover,the strategy of the modifications of benefit factors is used in the mutualism phase of SOS.A strategy of narrowing the search range of randomly generated coefficients is adopted in the commensalism phase of SOS.And then,the procedures and flowchart of employing the proposed ImSOS for solving the PV module models parameter identification problem based on experimental current versus voltage(I-V)data of a real PV module is detailed.Finally,the proposed ImSOS has been demonstrated on the parameter identification of different PV module models of the Sharp ND-R250A5 PV module.Experimental results and comparisons with original SOS and the other seven novel intelligent optimization algorithms imply the effectiveness and superiority of the proposed ImSOS.(4)To solve the disadvantages of the most PV models parameter identification algorithms at present,which have low accuracy and poor reliability,a novel improved version of CSA called ImCSA is proposed to solve the problem of estimating the parameters of PV models based on measured I-V data from the real PV cells/modules.For addressing the drawbacks of original CSA and improving its performance,the ImCSA is proposed by combining three strategies with original CSA.Firstly,a strategy called QOBL scheme is employed in the population initialization step of CSA.Secondly,a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA,which makes the step size with zero parameter initialization adaptively change according to the individual nest's fitness value over the course of the iteration and the current iteration number.Thirdly,a dynamic adjustment mechanism for the fraction probability or discovery rate(P_a)is proposed for providing better tradeoff between the exploration and exploitation to increase searching ability.Finally,the proposed ImCSA has been demonstrated on the various PV models,i.e.,single diode model(SDM),double diode model(DDM)and PV module model(PMM).The results show that the proposed ImCSA is able to seek out the best parameter values for PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules.Compared with original CSA and other different methods used in recent literature,the superior performance of the ImCSA is confirmed.