A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

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.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

TIME-RESOLVED OPTICAL-EMISSION AND ELECTRON-ENERGY DISTRIBUTION FUNCTION MEASUREMENTS IN RF PLASMAS

Comparisons between experimentally measured time-dependent electron energy distribution functions and optical emission intensities are reported for low-frequency (100 and 400 kHz) radio-frequency driven discharges in argon. The electron energy distribution functions were measured with a time-resolved Langmuir probe system. Time-resolved optical emissions of argon resonance lines at 687.1 and 750.4 nm were determined by photon-counting methods. Known ground-state and metastable-state excitation cross sections were used along with the measured electron energy distribution functions to calculate the time dependence of the optical emission intensity. It was found that a calculation using only the ground-state cross sections gave the best agreement with the time dependence of the measured optical emission. Time-dependent electron density, electron temperature, and plasma potential measurements are also reported.

TIME-RESOLVED ELECTRON-ENERGY DISTRIBUTION FUNCTION MEASUREMENTS IN A PULSED MAGNETIC MULTIPOLE HYDROGEN DISCHARGE

The time evolution of measured plasma parameters, including the electron energy distribution function (EEDF), in the discharge and post-discharge regime of a pulsed hydrogen magnetic multipole plasma is presented. The time necessary for the plasma to reach equilibrium has been established as 160-mu-s. The present results clarify the mechanisms which initiate the discharge. The decay rates of the charged-particle density and energy in the post-discharge have been measured. These measurements indicate that particle transport to the wall is the dominant loss mechanism for both charged-particle density and energy. The time-resolved EEDF is found to be non-Maxwellian in the discharge and Maxwellian in the late post-discharge.

Molecular dynamics simulation model for the quantitative assessment of tool wear during single point diamond turning of cubic silicon carbide

Silicon carbide (SiC) is a material of great technological interest for engineering applications concerning hostile environments where silicon-based components cannot work (beyond 623 K). Single point diamond turning (SPDT) has remained a superior and viable method to harness process efficiency and freeform shapes on this harder material. However, it is extremely difficult to machine this ceramic consistently in the ductile regime due to sudden and rapid tool wear. It thus becomes non trivial to develop an accurate understanding of tool wear mechanism during SPDT of SiC in order to identify measures to suppress wear to minimize operational cost.

In this paper, molecular dynamics (MD) simulation has been deployed with a realistic analytical bond order potential (ABOP) formalism based potential energy function to understand tool wear mechanism during single point diamond turning of SiC. The most significant result was obtained using the radial distribution function which suggests graphitization of diamond tool during the machining process. This phenomenon occurs due to the abrasive processes between these two ultra hard materials. The abrasive action results in locally high temperature which compounds with the massive cutting forces leading to sp3–sp2 order–disorder transition of diamond tool. This represents the root cause of tool wear during SPDT operation of cubic SiC. Further testing led to the development of a novel method for quantitative assessment of the progression of diamond tool wear from MD simulations.

Molecular dynamics simulation of nanoindentation of Fe3C and tetrahedral Fe4C

Study of nanomechanical response of iron carbides is important because presence of iron carbides greatly influences the performance and longevity of steel components. This work contributes to the literature by exploring nanoindentation of nanocrystalline Fe3C and tetrahedral-Fe4C using molecular dynamics simulation. The chemical interactions of iron and carbon were described through an analytical bond order inter-atomic potential (ABOP) energy function. The indentations were performed at an indentation speed of 50 m/sec and a repeat trial was performed at 5 m/sec. Load-displacement (P-h) curve for both these carbides showed residual indentation depth and maximum indentation depth (hf/hmax) ratio to be higher than 0.7 i.e. a circumstance where Oliver and Pharr method was not appropriate to be applied to evaluate the material properties. Alternate evaluation revealed Fe3C to be much harder than Fe4C. Gibbs free energy of formation and radial distribution function, coupled with state of the average local temperature and von Mises stresses indicate the formation of a new phase of iron-carbide. Formation of this newer phase was found to be due to deviatoric strain rather than the high temperature induced in the substrate during nanoindentation

Can a carbon nano-coating resist metallic phase transformation in silicon substrate during nanoimpact?

Nanomechanical response of a silicon specimen coated with a sp3 crystalline carbon coating (1.8 nm thickness) was investigated using MD simulation. A sharp conical rigid tip was impacted at the speed of 50 m/sec up to a depth of ~80% of the coating thickness. Unlike pure silicon specimen, no metallic phase transformation was observed i.e. a thin coating was able to resist Si-I to Si-II metallic phase transformation signifying that the coating could alter the stress distribution and thereby the contact tribology of the substrate. The stress state of the system, radial distribution function and the load-displacement curve were all aligned with above observations

The diamond pyramid structure in electroless copper deposit, its atomic model and molecular dynamics simulation

In this seminar, I will talk about the discovery of the diamond pyramid structures in the electroless copper deposits on both epoxy and stainless steel substrates. The surface morphology of the structure was characterized with scanning electron microscopy (SEM). According to the morphological feature of the structure, an atom model was brought forward in order to describe the possible mechanism of forming such structure. Molecular dynamics simulations were then carried out to investigate the growing process of the diamond pyramid structure. The final structures of the simulation were compared with the SEM images and the atomic model. The radial distribution function of the final structures of the simulation was compared with that calculated from the X-ray diffraction pattern of the electroless copper deposit sample.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.