Neural Network-Based Faulty Line Identification in Power Distribution Systems

Artificial neural networks (ANNs) have large input-error tolerance ranges and can be used as classifiers. Utilizing this property, a neural network-based detector, which identifies the faulty line directly by taking current and voltage patterns as feature vectors, has been designed. The quality of classification is not dependent on the transmission model, but rather on the net topology, training set, and the choice of learning law. A feed-forward multilayer perceptron, using the Back-Propagation Learning Algorithm, has been used to realize an optimal classifier. The classification quality, by simulating certain faults on the lines, has demonstrated the capability of the proposed approach for distribution power system protection.     

Board-level built-in self-repair method of RAM

This paper describes the method of built-in self-repairing of RAM on board, designs hardware circuit, and logic for the RAM’s faults self-repairing system based on FPGA. The key technology is that it utilizes FPGA to test RAM according to some algorithm to find out failure memory units and replace the faulty units with FPGA. Then it can build a memory that has no fault concern to external controller, and realizes the logic binding between external controller and RAM. Micro Controller Unit (MCU) can operate external RAM correctly even if RAM has some fault address units. Conventional MCS-51 is used to simulate the operation of MCU operating external memory. Simulation shows FPGA can complete the faulty address units’ mapping and MCU can normally read and write external RAM. This design realizes the RAM’s built-in self-repairing on board.