Abstract: The contamination level estimation method for high voltage insulators collects samples of naturally contaminated insulators and builds an image data set for the collected insulators. Flashover voltages of several insulators samples are measured. ESDD levels of the collected insulators are estimated. Images are input to image processing algorithms to extract representative features. The images are segmented. Transforming the image from RGB color space into grayscale model excludes the background from the image. Subsequently, the segmented images are transferred back to RGB color space model using matrix manipulation. Since contaminants on the insulator surface affect the color of the insulator, the segmented image is transformed from RGB to HSV color space which is used for extracting statistical and linear algebraic features from the hue image. A trained artificial neural network correlates the extracted features to the contamination levels enabling testing of other contaminated insulators.

Abstract: A controller includes circuitry configured to detect an occurrence of a transient instability event at a multi-machine power system (MMPS) based on one or more sensed operational parameters at one or more energy generation devices. Excitation voltage input values to the one or more energy generation devices are determined over a predetermined prediction horizon based on minimizing a predetermined cost function bound by one or more constraints. Control signals are output to one or more actuators associated with the energy generation devices based on the excitation voltage input values to reduce a length of time of the transient instability event.

Abstract: The contamination level estimation method for high voltage insulators collects samples of naturally contaminated insulators and builds an image data set for the collected insulators. Flashover voltages of several insulators samples are measured. ESDD levels of the collected insulators are estimated. Images are input to image processing algorithms to extract representative features. The images are segmented. Transforming the image from RGB color space into grayscale model excludes the background from the image. Subsequently, the segmented images are transferred back to RGB color space model using matrix manipulation. Since contaminants on the insulator surface affect the color of the insulator, the segmented image is transformed from RGB to HSV color space which is used for extracting statistical and linear algebraic features from the hue image. A trained artificial neural network correlates the extracted features to the contamination levels enabling testing of other contaminated insulators.

Abstract: The method of modeling fly ash collection efficiency in wire-duct electrostatic precipitators provides for the optimization of fly ash collection through the generation of numerical solutions to the electrostatic and electrodynamic equations associated with the particular geometry of the wire-duct electrostatic precipitator. Particularly, the solutions are developed through use of the finite element method and a modified method of characteristics.

Abstract: The method of modeling fly ash collection efficiency in wire-duct electrostatic precipitators provides for the optimization of fly ash collection through the generation of numerical solutions to the electrostatic and electrodynamic equations associated with the particular geometry of the wire-duct electrostatic precipitator. Particularly, the solutions are developed through use of the finite element method and a modified method of characteristics.