Combined DWT Dynamic Data Reconciliation Research And Application

In this dissertation, methodologies are proposed to solve dynamic data reconciliation problems by using discrete wavelet transform. As dealing with the dynamic data reconciliation problems, two approaches are usually considered:By the first approach, system differential equations must be transformed to algebraic equations in advance and the problems are solved by the mathematical programming method in the following; by the second approach, Kalman filter method is used to deal with system dynamic equations along with the estimation simultaneously.In the research, discrete wavelet transform theories are applied to the above-mentioned two approaches to accomplish the reconciliation. Wavelets theories are aimed to analyze and filter the measurement signals, which can deal with some shortcomings and deficiencies, e.g. the polynomial approach has difficulty as encountering the tortuous signals, in the data reconciliation problems.There are three main parts in the research. In the first part, considering a linear dynamic system, system's differential algebraic equations are converted into algebraic equations using Simpson's integration rule, which can be solved easily by the steady-state data reconciliation theories for this dynamic data reconciliation problem. Discrete wavelets transform is used to filter the measurement signals to decrease the errors from the transformation.In the second part, on-line dynamic data reconciliation approach is proposed by using the Kalman filter estimation which has time-recursive formulations. Augmenting the states is always needed in this approach which may result significant errors for reconciliation. Thus, an on-line filtering using the discrete wavelets transform is applied to filter the measurements from a real time process for reconciliation. Some difficulties e.g. the boundary problems, encountered in the wavelets filtering are overcome by a proposed robust algorithm. In the third part, through using the scaling functions in the wavelets theories, the measurement signals and their derivatives are approximated and the dynamic data reconciliation problem is formulated and solved by optimization via mathematical programming method. As optimization, the searching space for is the domain of the scaling function coefficients, which can reduce the number of the searching variables. A single gross error detection and isolation method is also proposed which can isolate the fault successfully.Examples are performed to illustrate the proposed approaches and summaries are given for the comparisons of the proposed three methods.