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A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

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Purpose Poor water-solubility of BCS class II drugs can limit their commercialization because of reduced oral bioavailability. It has been reported that loading of drug by adsorption onto porous silica would enhance drug solubility due to the increased surface area available for solvent diffusion. In this work, solid dispersions are formed using supercritical carbon dioxide (scCO2). The aim of this research was to characterise the solid-state properties of scCO2 dispersion and to investigate the impact of altering scCO2 processing conditions on final amorphous product performance that could lead to enhancement of drug dissolution rate for BCS class II drugs. Methods Indomethacin (IND) was purchased from Sigma-Aldrich (Dorset, UK) and was used as a model drug with two grades of high surface area silica (average particle sizes 3&[micro] and 7&[micro]), which were obtained directly from Grace-Davison (Germany). Material crystallinity was evaluated using powder X-ray diffraction (PXRD, Rigaku™, miniflex II, Japan) and high-speed differential scanning calorimetry (Hyper-DSC 8000, Perkin Elmer, USA). Materials were placed in a high-pressure vessel consisting of a CO2 cylinder, a Thar™ Technologies P50 high-pressure pump and a 750 ml high-pressure vessel (Thar, USA). Physical mixtures were exposed to CO2 gas above its critical conditions. SEM imaging and elemental analysis were conducted using a Jeol 6500 FEGSEM (Advanced MicroBeam Inc., Austria). Drug release was examined using USP type II dissolution tester (Caleva™, UK). Results The two grades of silica were found to be amorphous using PXRD and Hyper-DSC. Using PXRD, it was shown that an increase in incubation time and pressure resulted in a decrease in the crystalline content. Drug release profiles from the two different silica formulations prepared under the same conditions are shown in Figure 1. It was found that there was a significant enhancement in drug release, which was influenced, by silica type and other experiment conditions such as temperature, pressure and exposure time. SEM imaging and elemental analysis showed drug deposited inside silica pores as well as on the outer surface. Conclusion This project has shown that silica carrier platforms may be used as an alternative approach to generating polymeric solid dispersions of amorphous drugs exhibiting enhanced solubility.