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Additional resources for High-Power Density Steam Turbine Design Evolution
Example text
Applications of this predictive model based approach for different control objectives are summarized in [32]. Keeping aside the obvious concern regarding the accuracy of the predictive models, on-line computation of Q G for all valid switching states would increase the computational burden to a great extent. Superiority of this technique over the performance obtained with SVM approaches has not been established yet [32]. 5 Dynamic Model for Controller Design Tight coupling between the input and output side of MC complicates controller design, particularly for applications requiring high bandwidth.
54), the individual elements of the corresponding transfer matrix D(s) in dq domain are 1 2 j D12 (s) = − 2 D11 (s) = D Z (s − jωT ) D Z (s + jωT ) + , D P (s + jωT ) D P (s − jωT ) D Z (s + jωT ) D Z (s − jωT ) − . 84) Therefore the common denominator polynomial is Cd (D) = D P (s + jωT ) D P (s − jωT ). 85) The roots of common denominator Cd (D) of the transfer matrices are obtained by shifting only the imaginary part of the roots of D P (s). Therefore the real part of the roots remain unaffected as shown in Fig.
Analysis of poles and zeros for this specific sub class of common denominator transfer function matrices (CdTM) are described in the following section. 1 Poles and Zeros of CdTM A 2 × 2 transfer matrix G(s) with normal rank 2 is considered where G(s) = 1 G 11 (s) G 12 (s) = G 21 (s) G 22 (s) d(s) N11 (s) N12 (s) . 74) d(s) is the common denominator polynomial of each elements and N11 (s), N12 (s), N21 (s) and N22 (s) are the numerator polynomials. 75) Identifying the poles and zeros of the 2 × 2 matrix G(s), requires deriving a diagonal matrix L(s) from G(s) as [42, 43], L(s) = U1(s)G(s)U2(s).
High-Power Density Steam Turbine Design Evolution
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