Power System Loading Modeling Based On Gene Expression Programming

Power system consists of three major components: generators, electricitynetwork and power load. Nowadays, research scholars in this academia area facedwith a lot of tough questions. In the past few decades, the power system transmissionnetwork modeling techniques and modeling generator and control technology havemade significant development. In modern computing power system analysis, powergenerators and transmission network model have been more accurate, while the loadmodel is still relatively rough, all is well known, in the power system controloperation process, the role is critical load, which makes the whole system seems notvery match.As the integrated load has its own characteristics-non-continuous, random,dispersion and diversity, which lead to we will face many difficulties in loadmodeling area. Therefore, in the current research, recognizing the power system loadmodeling is the key to power development is particularly important. With the growingsize of the state grid system, the grid structure is more complex, resulting dynamicstability and voltage stability problems of the grid have become increasinglyprominent, the field of power system load modeling is also recognized as one of aworld power system problems.The impact on the system load model calculation results is not to beunderestimated. In many engineering commissioning projects, such as in theNortheast-North interconnection commissioning research projects, we found that theimpact load model parameters on the stability of the system and the results are verysignificant, it can not only affect the reliability of the results, but also can lead toselect the program has become a particularly difficult decision, so researchers musthave been pay more attention to it. To solve this problem, it is urgent to establish aninterconnected system to adapt to the characteristics of China’s large stage loadmodels and modeling methods. Biological evolution is the evolution so that we havemany evolutionary computing ideas. According to the long-term practical and theoretical experience, most peopleagree that the current Darwinian theory of evolution, the core of Darwinian evolutiontheory is "natural selection, survival of the fittest." According to this theory, there arethree main forms in the development and evolution of biological: genetic variationand selection.From the perspective of numerical analysis, evolutionary algorithms can beregarded as a kind of stochastic optimization algorithms intelligent search; from theperspective of natural evolution, in the dynamic environment, evolutionaryalgorithms represents an adaptation process, rather than as a static environments inthe optimization process; from a physical standpoint, the complex is generatedphenomena between chaos and order in nonlinear dynamics. for the furtherdevelopment of the theory of evolution, these different angle views have laid a goodfoundation.The main object of this thesis is a new intelligent optimization search algorithm-Gene Expression Programming, abbreviated GEP. The GEP algorithm is introducedto the power system load modeling area, which is less common to apply. This paper’sambition is the use of GEP algorithm analysis in static load modeling. Geneexpression programming develop on the basis of the traditional genetic algorithmsand genetic programming, it belongs to a new kind of evolutionary algorithm withgenotype space and phenotype space at the same time, the use of the advantages ofthis algorithm is that it does not depend on there are too many experienced staff,programmers do not need to pre-ordained structure of the objective function, and thedata does not require staff to do some in-depth understanding in advance. Use GEPalgorithm programmed after setting parameters and run the program, the program canautomatically help us identify hidden relationships between data internally, enablingto get automatic modeling function. Of course, for some of the complex relationshipsfunction, we need a more mature program as a tool. Currently, in terms ofprogramming, you can use Matlab language or C++language. This paper choosesthe C++programming language.Examples used in this paper is a static induction motor voltage characteristics ofthe test data, a mathematical model by gene expression programming algorithm, compared with the conventional method of least squares, and several othermathematical models of evolutionary algorithms to build and evaluate, then compareits strengths and weaknesses, indicating the feasibility of gene expressionprogramming algorithm, at the same time, it can provides new methods and ideas forthe areas of power system load modeling in the future.