The Research On Method Of Stator Winding Inter-tum Fault Diagnosis In Three-Phase Induction Motors

Induction motors play an important role as prime movers in manufacturing,process industry, agricultural production and transportation. With the increase inproduction capabilities of modern manufacturing systems, capacity of a single motorkeeps increasing and the load also becomes more complicated. Unexpected downtimedue to machinery failures has become more costly than before. The faults of inductionmotors may not only cause the interruption of product operation but also increasemaintenance costs, decrease product quality and affect the safety of operators. Statorwindings short circuit is one of the most common faults in electric machines. Thistype of fault is caused by the combination of various stresses acting on the stator, suchas thermal, electrical, mechanical, and environmental stresses. Such fault produceshigh currents and winding overheating resulting in severe phase-to-phase, turn-to-turnor turn-to-ground faults. It may produce a catastrophic damage in the windings or inthe motor core. For this reason, rapid and accurate detection of incipient faultsbetween turns during motor operation is very important. Therefore, the objective ofthis thesis is to study the on-line turn fault detection methods. The main contents andachievements of the thesis are as follows:According to the electromagnetic theory and the healthy model, a transientmodel for an induction machine with stator winding turn faults is derived usingreference frame transformation theory. The dynamic equations is presented instate-space form, which is suitable for digital simulation. A model-based strategy forstator inter-turn short circuit detection on induction motors is proposed. The currentestimation error is used as a state observer for the incipient detection of the inter-turnfault. By measuring only stator voltages and currents,the current for the observer canbe calculated. The current estimation error between the measurement and calculationis decomposed into two parts: one part is used for the faulty severity, the other is usedin an adaptive scheme for speed estimation. The proposed technique is able to rapidlydetect incipient faults, independently of the phase in which the fault occurs. And theobserver includes an adaptive scheme for rotor-speed estimation, avoiding the use of aspeed sensor.Stator inter-turn short circuit fault in the induction motors is affected by theloads and unbalanced supply voltages. The relationship between them is usually uncertain. Fuzzy neural network has the ability to achieve nonlinear dynamicmappings with simple structure, rapidly convergence and easily implementation,therefore it can be used to detect stator winding turn fault in induction motors atvarious conditions. When inter-turn short circuit occurs in stator winding, the Parkmodule will change. In order to detect inter-turns accurately, a method based onextended-Park transform and fuzzy neural network is presented. By spectrum analysis,the ratio between the magnitude of2f1component and the Park module can serves asfaulty characteristic.Then a model which take load and phase voltage unbalance intoconsideration is constructed based on fuzzy-neural network for detection of statorinter-turn short circuit fault.The development of stator inter-turn fault is a dynamic and slow processfrom the incipience to severe. Recurrent wavelet neural network based on-line statorwinding turn fault detection approach for induction motors has been done. In theapproach, two recurrent wavelet neural networks are employed to detect inter-turnfault, one is used to estimate the fault severity, the other is used to determine the exactnumber of fault turns. In order to overcome the lacks of BP algorithm such as slowconvergence, non-stability of convergence and local minimum problem In the courseof training, Levenberg-Marquardt (LM) algorithm is introduced to make therecurrent wavelet neural network network converging more quickly.In order to detect and locate an inter-turn short circuit fault on the statorwindings of induction motors, an approach based on faulty model and BP neuralnetwork is presented. The diagnostic process is achieved through monitoringsimultaneously the values of the three-phase shifts between the line current and thephase voltage of the machine. Genetic algorithm is adopted to optimize the BPNNparameters for improving dynamical processing ability.All the proposed models and methods are verified through simulations andexperiments. The results demonstrate great engineering value.