Study On The Sequential-Data-Sets-Based Nonlinear Mapping Models For Some Complex Problems In Power Systems

With the wide access of renewable energy,the massive application of power electronics,the continuous development of new loads,and the coupled integration of multi-energy systems,the network structure and operating characteristics of the modern power systems have been deeply changed.The changes not only trigger the urgent need for upgrading and updating the existed technology and methods,but also bring various new problems that are extremely complex and difficult to be solved in the power systems.Therefore,the power systems are facing the challenge of how to address the new complex problems today.At the same time,due to the rapid development and wide application of the advanced technologies,e.g.high-speed networks,cloud computing,artificial intelligence,robots,and drones,there are several new features,such as rich data resources,powerful computing capabilities,and convenient communication ways,in the modern power systems.These new features not only strongly support the upgrade and update of the existed technology and methods,but also bring new opportunities to study the novel methods to address the new complex problems in the power system.In the above background,for studying on the new complex problems in the modern power systems,this thesis regards the way using the opportunity to address problems as a guide,stands on the advances of complex problems of modern power systems,concentrates on the urgent needs from the operation and maintenance,makes full use of the data resources,selects the dimension feature of the sequential data sets in the spatio-temporal coordinate as the clue,and explores the novel nonlinear mapping modeling methods.The main contributions of this thesis are as follow:For the ill-posed problem in the structure construction and parameters estimation of the nonlinear mapping models,since the sequential data sets either contain the dynamic characteristics of the system or reflect a certain geometric characteristic,a regularization method based on the curve geometric characteristics of sequential data sets is proposed.The proposed method only involves the geometric characteristics of the sequential data set,and it could be adopted without knowing the characteristics of the physical system,therefore,it can be widely adopted,especially in data-driven modeling.On the topic of the nonlinear mapping modeling based on the sequential data sets from the snapshot,the study focuses on the reading recognition of analog instruments in substations,a novel nonlinear mapping model of the automatic reading recognition of an analog instrument is proposed.In the image pre-processing,the model has successfully enhanced the robustness of lightness and camera angle by synthetical using the multi-scale Retinex with color restoration and perspective transform.In the reading recognition procedure,according to the gradient approach and voting methods,a fast and reliable improved Hough circle detection method is presented,which effectively improved the automatic recognition speed of the nonlinear mapping model.The proposed model is with strong robustness,fast recognition speed,and high reliability,and can be easily adapted for automatic reading recognition of the analog instruments in the smart operation and maintenance.On the topic of the nonlinear mapping modeling based on the sequential data sets from the temporal dimension in the spatio-temporal coordinate,the study focuses on the dynamic equivalent model of the active distribution networks(ADNs),a dynamic equivalent nonlinear mapping model of ADNs is proposed based on the artificial neural networks(ANN)which have memory capability.For the construction of the model structure,based on the way of combining the mapping relationship of ANN with the physical characteristics of the circuit system,the design guidelines of the LSTM for the dynamic equivalent model of ADNs are given.For the estimation of model parameters,a regularized objective function based on the curvature of the sequential data sets,and the corresponding training algorithm of LSTM are established.The proposed modeling approach provides a novel idea and method for dynamic equivalent modeling of the complicated ADNs.On the topic of the nonlinear mapping modeling based on the sequential data sets from the temporal dimension and a spatial dimension in the spatio-temporal coordinate,the study focuses on the optimal operation of the integrated power and gas energy systems(IPGES),an optimization model for operating IPGES is proposed based on the nonlinear function-space mapping.In the modeling process,according to the function approximation theory,the operation matrices,which are related to the integration and differentiation in the algebraic space,are developed in the function space.Then,based on the optimal energy flow model of IPGES and function-space mapping,an optimization model for operating IPGES is constructed in the function space.The proposed modeling approach gives a novel idea for modeling the optimized operation of IPGES with considering the dynamic characteristics of natural gas systems.