Optical Fiber Refractive Index Sensor for Chloride Ion Monitoring

The development of a reflective, gold-coated long-period grating-based sensor for the measurement of chloride ions in solution is discussed. The sensor scheme is based around a long-period fiber grating (LPG)-based Michelson interferometer where the sensor was calibrated and evaluated in the laboratory using sodium chloride solutions, over a wide range of concentrations, from 0.01 to 4.00 M. The grating response creates shifts in the spectral characteristic of the interferometer, formed using the LPG and a reflective surface on the distal end of the fiber, due to the change of refracting index of the solution surrounding it. It was found that the sensitivity of the device could be enhanced over that obtained from a bare fiber by coating the LPG-based interferometer with gold nanoparticles and the results of a cross-comparison of performance were obtained and details discussed. The approach will be explored as a basis to create a portable, low-power device, developed with the potential for installation in concrete structures to determine the ingress of chloride ions, operating through monitoring the refractive index change.

Measurement-based Method for Wind Farm Power System Oscillations Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This study presents a measurement-based method for the early detection of power system oscillations, with consideration of mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet-based support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in frequency bands, whereas the SVDD method is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude, or that are resonant, can be alarmed to the system operator, to reduce the risk of system instability. The proposed method is exemplified using measured data from a chosen wind farm site.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Effective monitoring for ractopamine residues in samples of animal origin by SPR biosensor and mass spectrometry

Ractopamine (RCT) is a member of the beta-2-agonist (beta-agonist) family. It is licensed for use as an animal growth promoter in more than 20 countries worldwide, including the United States and Canada, but is either not licensed or prohibited by over 150 others, including those within the European Union. The issue of the use of RCT in livestock bound for human consumption has risen to prominence recently following the decision by The People's Republic of China to ban the import of pork from a number of processing plants after finding traces of RCT in shipments from the U.S.A.