Diagnosis of Inter-Turn Short Circuit of Synchronous Generator Rotor Winding Based on Volterra Kernel Identification

The inter-turn short circuit is a common fault in the synchronous generator. This fault is not easily detected at early stage. However, with the development of the fault, it will pose a threat to the safe operation of the generator. To detect the inter-turn short circuit of rotor winding, the feasibility of identifying the stator branch characteristics of synchronous generator during inter-turn short circuit was analyzed. In this paper, an on-line fault identification method based on Volterra kernel identification is presented. This method uses the stator branch voltage and stator unbalance branch current collected from the generator as input and output signals of the series model. Recursive batch least squares method is applied to calculate the three kernels of Volterra series. When the generator is in normal state or fault state, the Volterra kernel will change accordingly. Through the identification of the time-domain kernel of the nonlinear transfer model, the inter-turn short circuit fault of the synchronous generator is diagnosed. The correctness and effectiveness of this method is verified by using the data of fault experimental synchronous generator.

Third-Order Volterra Kernel Identification Technique in Aerodynamics

In this paper, we will extend a Volterra identification technique of nonlinear systems. In reality there exists a large class of weakly Nonlinear System which can be well defined by the first few kernels of the Volterra series. In general, Engineers believe that identifying high-order Volterra kernels is a big problem and hope for the advent of better identification techniques. However, with the extensive development of the Volterra kernels’ identification technique, the situation may improve. The formulas used to calculate kernels up to the third-order are given.

Volterra Kernel Identification Using Triangular Wavelets

The Nblterra series provides a convenient framework for the representation of nonlinear dynamical systems. One of the main drawbacks of this approach, however, is the large number of terns that are often needed to represent Wblterra kernels. In this paper we present an approach whereby wavelets are used to obtain low-order estimates of first-order and second-order blterra kernels. Several constructions of tensorproduct wavelets have been employed for some%blterra kernel approximations. In this paper, a triangular wavelet basis is constructed for the representation of the triangular fonn of the second-order kernel. These wavelets are piecewise-constant, orthonormal, and are supported over the triangular domain over which the second-order kernel is defined. The well-known Haar wavelet is used concurrently for the identification of the first-order kernel. This kernel identification algorithm is demonstrated on a prototypical nonlinear oscillator. It is shown that accurate kemel estimates can be obtained in terms of a relatively small number of wavelet coefficients. It is also demonstrated that, for this particular system, the derived Volterra model is valid for input amplitudes below a specified bound. When the input amplitude exceeds this threshold, higher-order kernels are needed to adequately describe the system dynamics. Thus, the approach taken in this paper is applicable to a large class of nonlinear systems provided that the input excitation is sufficiently bounded.