Cai Guowei

The transformer fault diagnosis based on Quantum Neural Network

It is very difficult to analyze the fault for dissolved gas analysis(DGA) because of the less and uncertain transformer gas information. In this paper, the Quantum Neural Network(QNN) was applied to diagnosis the transformer fault by employing the DGA data. The QNN can be used to reduce the uncertainty of pattern recognition by allocatting the uncertain data to the right fault pattern quickly and rationally as its model draws on the ideas of quantum phase computing and the complementary amendment relationship exists between the hidden layer output and phase shift parameters. The validity and feasibility of the proposed method were verified by testing the real DGA data. The results of the propoed model was compared with BP neural network in dealing with the actual DGA data also.

A new method based on fuzzy TOPSIS for transformer dissolved gas analysis

This paper presents a new classifier based on Fuzzy Technique of Order Preference by Sunilarity to Ideal Solution (TOPSIS) for power transformer fault diagnosis in dealing with the dissolved gas analysis(DGA) problem, which is treated as the collision between lowering complex process of the analysis and increasing quantity of monitoring informations. Two methods are employed to ameliorate the diagnosis condition. Firstly, the essential concept of Fuzzy Set using semantic variables is introduced. And this classification approach is then combined with the TOPSIS considering the efficiency in managing imperfect and incorrect datas, and the multiple attribute decision matrix is deduced by fuzzy mapping. Secondly, the vector interval is calculated, which fixes the relative similarity of the possible fault types on the evidence of various gas ratios. Finally, contrasted with IEC Three-ratio method, the new method has been proved rapid and exact in judging the operation states, and therefore the safety, stability and economy of transformer are improved.

A multi-objective optimization model of power generation with fuel and emission minimized

Real power generation dispatch and scheduling become a kind of multi-objective decision problems when fuel costs and emission are needed to be minimized simultaneously. These objectives are usually conflicting. Based on the concept of ideal point, generation dispatch model and generation scheduling model with fuel and emission minimized simultaneously are formulated. It is accomplished by constructing a hyperspace. In the hyperspace, the dimension number of which is that of the goals to be optimized, different coordinates of a space point are of such realistic meanings as costs or emission quantities. Not only the non-commensurable and conflicting problems of different goals could be dealt with, the conclusions also could give us useful insights into the multi-objective real power optimization problems by analyzing the necessary conditions of these models are discussed. These conditions are also helpful when solving these ideal point method models.

The study on the energy-conservation power generation dispatch

At the present time, the country has carried out the measure of energy-saving power scheduling. The main object of this measure is to increase the efficiency and to reduce pollution which is important to the power stations. This paper calculated three kinds of equivalent price coefficient of energy and environment in the case of the lowest bidding price or the least energy or the most minimum pollution to the environment. In this way, the multi-objective optimal problem with different physical units was reformulated into a new single-objective optimal problem. The numerical result shows that only considering economic indicators is unable to meet the development of the market. More considering on saving energy, lower energy consumption and reducing pollutants discharge must be given to put the power suppliers in a favorable position in the future market.