Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

Fibre segment interferometry using code-division multiplexed optical signal processing for strain sensing applications

A novel optical signal processing scheme for multiplexing fibre segment interferometers is proposed. The continuous-wave, homodyne technique combines code-division multiplexing with single-sideband modulation. It uses only one electro-optic phase modulator to achieve both range separation and quadrature interferometric phase measurement. This scheme is applied to fibre segment interferometry, where a number of long-gauge length interferometric fibre sensors are formed by subtracting pairs of signals from equidistantly placed, weak back reflectors. In this work we give a detailed account of the signal processing involved and, in particular, explore aspects such as electronic bandwidth requirements, noise, crosstalk and linearity, which are important design considerations. A signal bandwidth of ±20 kHz permits the resolution of phase change rates of 2.5 × 104 rad s-1 for each of the four 16.5 m long segments in our setup. We show that dynamic strain resolutions below 0.2 nanostrain Hz-0.5 at 2 m sensor gauge length are achievable, even with an inexpensive diode laser. When used in applications that require only relative strain change measurements, this scheme compares well to more established techniques and can provide high-fidelity yet cost-effective measurements

Development of a dc-ac power conditioner for wind generator by using neural network

This project present of development single phase DC-AC converter for wind generator application. The mathematical model of the wind generator and Artificial Neural Network control for DC-AC converter is derived. The controller is designed to stabilize the output voltage of DC-AC converter. To verify the effectiveness of the proposal controller, both simulation and experimental are developed. The simulation and experimental result show that the amplitude of output voltage of the DC-AC converter can be controlled