Identification Of Induction-motor Stator Resistance Based On Neutral Network

Great developments have taken place since Direct Torque Control system (DTC) was brought forward. It has been a focus among domestic and foreign scholars because it has character with simple structure, novelty thought and wonderful property. DTC system is different from vector control in that it is located in stator coordinate. As a result, few parameters have effect on DTC systems and it has littler calculation. This paper mainly has obtained a great deal of data about the stator resistance which changes with different voltage frequencies, the stator currents, the stator end temperate through the experiment, and obtains stator resistance and voltage frequency, stator current as well as the stator end warm between change relations from the data, then uses the neural network algorithm to construct the model of stator resistance changing with variety voltage frequencies, stator currents and the temperature and has carried on the simulation using the MATLAB simulation tool.The stator resistance is affected by many factors, such as the driver's runtime, current magnitude, frequency, and temperature of the environment etc. It is extremely difficult to find the relationship through the tradition methods because of complicated variety of resistance. Therefore the main point of this paper is to apply generalized regression neural network to the detection of stator resistance; to combine many factors into three parameters: frequency, electric current and temperature. Using the generalized regression neural network, this paper structures the model in order to confirm stator resistance.In full speed area, this paper carries on the identification to the stator resistance. Because of in the low-frequency area, the stator resistance parameter change influences the flux linkage characteristic. The key point which improves low-frequency characteristic of the non-velocity generator direct torque control system is how to enhance the precision of the stator resistance observation. In order to enhance the neural network the training speed, and reduce the mutual influence of the network weight between middle & high- frequency and low- frequency sample in training process, this paper separately establishes respective neural network to middle & high- frequency and low- frequency stator resistance model.Comparing with the identification result of the stator resistance by the BP neural network, the generalized regression neural network which this paper designs has follow merits:1. The ability of approach is very strong. The concealment level node function (primary function) of the generalized regression neural network uses the Gauss function. As a non-negative nonlinear function, it takes one kind of partial distribution which weakens the center radial symmetry, the Gauss function will have the response to the input signal in the part, namely the concealed level node will have the big output, when input signal nears the central scope of the primary function, it can be seen from this that this kind of network has the strong ability in approaching part.2. The study speed is higher than BP network. The network finally converges at the optimization regression surface which gathers the most sample data, once the study samples are fixed, the e the corresponding network structure and the neuron connection weight are also fixed. In fact, the training process of the network is only to confirm the smooth parameter.3. The ability of extend is very strong. When the sample data are scarce, the effect is also satisfying. The network may process the unstable data, too.