Study Of Forecasting In Power Systems With Regression And Classification Based On Sparsity Bayesian Learning

Forecasting technology is a complex cross-science, it has almost involved in every social fieled, in Electric power systems, and it also plays a very vital role. Not only does the power loaded need forecasting, but the power price, voltage adequacy, power-angle curve, harmonic wave analysis, stability assessment, fault classification, system reliability, working riskprofile, etc. need forecasting. In these forecasting, all belong to regression and classification. The problem and concerns is how to promote the forecasting accurancy and save the computing time. A good forecasting model is definitively for many questions in power systems, such as:system operation and control, system stability and protection, grid optimal reactive power attemper, system operation planning, generators'optimization, dealer pricing of power market etc.. Essentially, power system is a complicated large-scale dynamic nonlinear system. Consequentially, it has many problems about complex engineering calculation and nonlinear optimization; further more it has a sharp time-bound. Especially, along with the grid development and power market, engineers face the problems become complicated more and more, in the other side, customs need the grid safe and electric energy quality increasingly higher and higher. Although researchers of automation in electric power systems have sought many methods to resolve the problems for a long time, the problems can not be attained the faster and more accurate results.Comparing with the traditional calculating method, artificial intelligence (AI) programs have the great irreplaceable advantages in complex nonlinear systems. It makes up the shortcomings of the traditional methods which purely lie upon the exactly mathematical models, and resolve the problems which can not be settled or difficult to be settled by traditional methods. As it has the ability which can deal with the nonlinear problem, and admit the models are uncertainty and inexactness, in recent years, AI technology applications research have infiltrated through the electric power system and electric engineering, and some of these have in practical, so AI and traditional mathematical models must be existing side by side in a long period, and mutually form the practical control and optimization strategy in concerted.With the hardcore of AI:machine learning (ML) technology, applying pattern identification and regression analysis in power systems, is the dissertation's emphasis. Aim at the development of machine learning in power systems, the dissertation summarizes the machine learning technology applications for power systems in existence, and introduces a new machine learning technology which based on probability learning and sparse Bayesian theorem, and gives the modeling methods to power systems forecasting. For improving the model, data mining, kernel principal components analysis, kernel function construction, particle swarm optimization also are used, after experiment and emulation we get a satisfactory result both in regression and classification. The main contributions of the dissertation include the following.(1) Utilizing the bran-new thought of probabilistic learning practical model: 'relevance vector machine' (RVM), a general Bayesian framework for obtaining sparse solutions to classify or regress predicting, respectively construct the medium term load forecasting model and transient stability assessment(TSA) model, validating the result from two sides of regression and classification. In the same condition, comparing with the popular and state-of-the-art'support vector machine' (SVM) and Radial Basis Function Artificial Neural Nets(RBF-ANN), RVM model achievs better results both in two sides. For its arithmetic's structure is high sparsity and based on probabilistic learning, RVM not only achieves good forecasting accuracy but also cut down the computing time, and it can offer the probabilistic prediction and arbitrary using kernel functions. Predictably, RVM must has a extraordinary board prospect in electric power system. Especially, for its probabilistic prediction and super sparsity, it has a good practical value for online computation and forming the hierarchical control strategy in electric power systems forecasting.(2) Aim at the enormous database of day load time series, an electric load data clustering algorithm (CA) using multi-hierarchy analysis based on time series curve contour similarity is proposed, the performance is proved by theory also. The experiment result makes clear that the algorithm can exactly classify the historical load series accord with the practical regulation, and also can find the special movement in the samples. Application in a RVM short-term load forecasting model, decreasing the input vector dimension, at the same time we attain the high level of accuracy.(3) As there are a great deal of factors influent the transient stability, further more SCADA collect the operation data are magnanimity, if we analyze the data space directly, not only brought out'Curse of Dimensionality', but could not achieve a good result. The dissertation gives kernel principal component analysis (KPCA) for feature abstract in TSA model, the method eliminate the irrelevance and redundancy from original input eigenvalues, The emulation in TSA model, result shows the method gives good prediction accuracy and the same time reduces the input dimension greatly.(4) Utilizing the compound kernel function ideology, first-use for the RVM constructing kernel functions. Multi-linearity-compound kernels based on Gaussian kernel, polynomial kernel and tensor product spline kernel were built, and the compound kernel functions'parameters are automatic optimized by algorithm of particle swarm optimization (PSO), then attain the optimal kernel parameters to enhance the model's efficiency. Emulation of TSA models and short term load forecasting based on multi-kernel RVMs. The result shows the compound kernels RVM models give the better generalization capability than the single kernel ones. It indicates that using compound kernel construction method is a viable way to enhance RVM model's forecasting accuracy.