Online Monitoring For Uneven Length Batch Processes Using Function Space Principal Component Analysis

\ud \ud Online batch process monitoring has been a challenging task, as batch processes do not operate around a nominal steady state operating point. Various monitoring approaches where future batch trajectory is filled with average (nominal) batch trajectory have been proposed. Predicting future trajectory for a batch process is a difficult task. Recently a multiway principal component analysis (MPCA) based approach that does not involve future trajectory prediction was proposed. In this paper a new technique based on function space principal component analysis (FSPCA) is proposed for online batch process monitoring. The main advantage of the proposed FSPCA based methodology is its ability to detect incipient and small to medium magnitude faults and its relevance for uneven length batch processes. Efficiency and effectiveness of the proposed algorithm is demonstrated via a fed-batch penicillin cultivation process simulation. The diagnostic performance of the proposed approach is significantly better compared to MPCA based approache

Inverse Free Kalman Filter Using Approximate Inverse of Diagonally Dominant Matrices

Conventional Kalman filter (KF) requires matrix inversion. But the pervasive applications of KF cannot at times afford inversion. Especially, embedded implementations do not have the capabilities to compute inverse using methods such as Cholesky decomposition. For large matrices, inversion could be computationally prohibitive even for non-embedded implementations. To address this problem, an inverse free Kalman filter (IFKF) is proposed in this letter. The inverse of innovation covariance matrix required in the update step of the KF is approximated using Taylor series expansion. The approximate inverse has a closed form expression in the elements of the original matrix. Bounds on the error covariance of proposed IFKF are also established. The proposed IFKF does not require any iterations to converge

Low complexity block distributed Kalman filtering for interacting systems

State estimation of a distributed system is typically done by processing the measurements of all the subsystems at a central estimator. This is the case because of the availability of several optimal centralized estimation methods. Alternatively, one has to compromise on the accuracy of estimates and run distributed estimators neglecting interactions. A low complexity block distributed Kalman filtering technique is proposed in this manuscript under the name of Approximate Distributed Kalman Filter (ADKF). ADKF distributes estimation while considering interactions, such that the accuracy of estimates is similar to the central estimator yet the computational complexity at each subsystem is significantly lower. The proposed ADKF algorithm is implemented on a continuous stirred tank reactor system, having multiple reactors in series. The simulation results show that the accuracy of estimates obtained from the proposed ADKF algorithm is comparable to the central estimator

Compressed Sensing for Images with Chirps and Reed-Muller Codes

Compressed sensing is nothing but a signal processing technique which compresses the signal and reconstructs the signal. It is a new technique to receive signals that are sparse with a less number of measurements. This report describes the usage of deterministic sensing matrices which are obtained from chirps and Reed-Muller codes in compressed sensing. With the help of these chirps and ReedMuller codes, the signal is reconstructed whose sparsity is less than the original work. In the field of imaging, this approach is applicable. The algorithm used for reconstructing the images will involve new elements to improve the difficulty in calculation and the accuracy with which signal is reconstructed

Controlled Power Point Tracking for Power Balancing in PMSG based Wind Energy Conversion System

With the increasing penetration of wind energy into power system, wind energy conversion systems (WECSs) should be able to control the power flow for limited as well as maximum power point tracking. In contrast to the traditional pitch angle control, this paper focusses on field oriented speed control of permanent magnet synchronous generator (PMSG) for controlling the active power flow based on the wind turbine characteristics. In this paper a back to back AC/DC/AC topology is implemented for interfacing the WECS to the distribution network with various power electronic interfaces providing the necessary control over the power flow. The proposed control strategy can provide power balancing and reserve capacity without use of expensive energy storage devices like batteries. Simulations are carried out under varying load demand as well as changing weather conditions to demonstrate the applicability and effectiveness of the proposed control strategy

Data reduction and fault diagnosis using principle of distributional equivalence

Historical data based fault diagnosis methods exploit two key strengths of the multivariate statistical tool being used: i) data compression ability, and ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts[1], and hence is more suited for the task of fault detection and isolation(FDI). In this paper, we propose a methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to CA and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The above aspect is also validated for large scale system using benchmark Tennessee Eastman process simulation case study