Search

In order to solve the problems of subjectivity, limitation of decision method and only considering the defect of single fuzziness in transformer condition assessment, a condition assessment method of power transformer based on fuzzy nearness degree and improved cloud matter-element model is proposed. Considering the randomness and fuzziness of transformer at the condition level boundary, the cloud matterelement model is used to construct the basic framework of condition assessment. Then, in order to take into account the clarity and fuzziness of the "3En" rule and the "50% association degree" rule, and to avoid conflicts of condition conclusions, cloud matter-element model is improved by cloud entropy optimization algorithm to calculate the membership degree of corresponding level of evaluation index. In addition, based on the multi-objective classification algorithm of fuzzy nearness degree, the evaluation index and asymmetric nearness degree vector of each condition level are calculated, the positive and negative ideal levels are determined by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the condition of the transformer is determined according to each asymmetric nearness degree vector and symmetric nearness degree of the positive and negative ideal levels. Taking two 220kV oil-immersed power transformers as examples, the validity and accuracy of the method are proved.

In order to improve the accuracy and efficiency of photovoltaic system output prediction effectively, realize short-term photovoltaic (PV) output forecasting, and to provide auxiliary reference basis for grid dispatching and planning, a short-term PV output prediction method based on improved PSO-RVM algorithm is established. Firstly, the improved fuzzy clustering method is used to obtain the similar day, the wavelet analysis method is used to decompose the similar day PV output by high frequency and low frequency. Then, the improved PSO-RVM algorithm is used to predict the wavelet component of the prediction day respectively. Finally, the predicted results are added together to obtain the predicted values of PV output. The simulation results show that the proposed method can significantly improve the short-term PV output prediction accuracy.