This paper concerns non-linear STATE ESTIMATION in a batch polymerization reactor where suspension polymerization of methyl methacrylate takes place. A kinetic model proposed in bie literature is selected and its validity has been verified through an experimental set-up. Based on this model monomer conversion and average molecular weights of the polymer are estimated using only one output measurement (reactor temperature). The performance of the estimator, which has the structure of an extended Kalman filter, is examined through simulation and experimental studies in the presence of different levels of parameter uncertainties. The effects of adding "fictitious noise" and parameter STATE" to the ESTIMATION algorithm are also investigated. To find the best fictitious STATE, the main parameters of polymerization model are divided into three groups. The parametric study of the MMA polymerization model indicates that, when the source of parameter uncertainty is unknown, the best selection of parameter STATE is the initial mass of monomer.

In this paper, a novel filter is provided that estimates the STATEs of any nonlinear system, both in the presence and absence of uncertainty with high accuracy. It is well understood that a robust filter design is a compromise between the robustness and the ESTIMATION accuracy. In fact, a robust filter is designed to obtain an accurate and suitable performance in presence of modelling errors. So in the absence of any unknown or time-varying uncertainties, the robust filter does not provide the desired performance. The new method provided in this paper, which is named hybrid robust cubature Kalman filter (CKF), is constructed by combining a traditional CKF and a novel robust CKF. The novel robust CKF is designed by merging a traditional CKF with an uncertainty estimator so that it can provide the desired performance in the presence of uncertainty. Since the presence of uncertainty results in a large innovation value, the hybrid robust CKF adapts itself according to the value of the normalized innovation. The CKF and robust CKF filters are run in parallel and at any time, a suitable decision is taken to choose the estimated STATE of either the CKF or the robust CKF as the final STATE ESTIMATION. To validate the performance of the proposed filters, two examples are given that demonstrate their promising performance.

Correct information about a power system’s dynamic variables is important and necessary for protection and control issues. Today's power systems, which differ from past systems, face new challenges due to converter-based resources. A solution to these challenges is dynamic STATE ESTIMATION in short time intervals, such as the time domain. This paper simulates a standard 68-bus system in the presence of converter-based resources with a high penetration percentage in DIgSILENT software and compares the performance of four Bayesian filters in estimating the dynamic variables of the synchronous generators of the system using values in the time domain with each other. The four types of filters used include extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter.The MATLAB software suite was used for the comparison of the performance of the four filter types in different scenarios, including the presence of measurement and processing noise, extreme noise, network fault, data missing, STATE ESTIMATION time by each filter, and the comparison of time domain method with other methods such as phasor domain. Finally, the advantages and disadvantages of each were identified.