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In order to be able to more perfectly and roundly deal with incomplete and indeterminate as well as ill fault symptom information in process of transformer fault diagnosis, set pair analysis (SPA) is applied to design emulation model and implement fault diagnosis in this paper. In this method, the consistency and discrepancy and conflict of fuzzy fault symptom information to same fault sources are...
Short-term load forecasting is important for the economic and secure operation of power system. Taking the random disturbances, especially the meteorological factors into account are the key to improve forecasting precision. This paper presents ant colony clustering model to process the historical load days. Ants pick or drop samples decided by the similarity of it to its surroundings. After iterative...
For the problems of dust discharging concentration and power consumption in power plants, a new optimizing control method on the whole was presented to improve the performance of the electrostatic precipitator. The corona power model was identified by least-squares method and dust concentration of the outlet was represented by the neural network model. Based on this, the operation voltage optimization...
Support vector machines (SVMs) have been proposed as a novel technique and applied to regression recently. In this paper, SVMS are used for load forecasting. The training sample sets are chosen and preprocessed before every forecasting. Then the interference of the non-correlative and bad samples for the forecasting can be avoided. The effectiveness and the feasibility of forecasting of the employed...
Multisensor information fusion seeks to combine data from multiple sensors to measure the variables that may not be possible from a single sensor alone, reducing signals uncertainty and improving the accuracy performance of the measuring. The two main parts in multisensor information fusion system are the fusion model and fusion algorithm. In this paper, a radial basis function (RBF) neural network...
A new algorithm for load forecasting - the neural network model based on particle swarm optimization (PSO-NN) for short-term load forecasting is proposed in this paper. The method is simple, easy to realize and its convergence rate is quick. The overall optimal solution of the problem can be found in great probability, and the intrinsic defects of artificial neural network, such as slow training speed...
Experimental platform is used to simulate typical faults of turbine. Based on the frequency domain feature, energy eigenvector of frequency domain is presented in the wavelet packet analysis method, and the way of best tree is used to choose symptom. Finally, the fault states are recognized using neural network, and the simulations show that it makes a good performance with the method
The fault diagnosis of power transformer is important for safety of the device and reliability of the power system. This paper proposes the large margin learning classifier, which is well designed for multi-class problem based on the large margin learning of SVM hyper-planes theory. Each time it attempts to find the separating hyper-plane with maximum margin to split the clusters. As a novel tool,...
This paper is based on the fault diagnosis analysis about electrical product of Bayesian network. We represent fault diagnosis as decision problem under ambiguity and immaturity of information. Bayesian network classifiers (BNC) is thus established which is based on the fault diagnosis of, and representing the ambiguity of information as probability description. Pre-process analysis is made to fault...
This paper describes a three-phase full control rectification circuit controller which is widely used in the system of speed regulation of DC motor. Conventional PID controller with double close-loops has been used in speed control of separately excited DC motor at present. But under conditions of actual operation we find that it isn't suitable for the high performance cases, because of the low robustness...
The contamination condition of insulators is usually estimated by detecting the root mean square (r.m.s) of surface leakage current via online monitoring system, ignoring the influence of environment factors, such as temperature, humidity, etc. For the detection factors have fuzzy characters, a new method based on fuzzy neural network is proposed in order to overcome the disadvantages of traditional...
A radial basis function (RBF) neural network used in fault diagnosis system is developed for power transformer fault analysis. The Gas extracted from transformer oil is the input of RBF-type neural network architecture. Our proposed cell-splitting grid algorithm determines the optimal network architecture of the RBF network automatically. This facilitates the conventional laborious trail-and-error...
With the development of power markets, electricity price especially the market clearing price (MCP) forecasting is becoming more and more important in such new competitive markets since the MCP forecasting is the basis of decision making for participants in electricity market. In this paper the problem of modeling market clearing price forecasting in deregulated markets is studied. And electricity...
Transformer substation automatic system is more and more developed towards the opening distributed and layered direction. And in the substation automatic system, communication is an important step. Despite the growing number of multi-agent software system and agent adapts well to dynamic physical environment, however, few power systems have adopted the technique of multi-agent. This paper adopts the...
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