IV. Simulation Experiments and Results

Overview of the Algorithm

The classification experiments were done with power quality disturbance signals simulated by MatLab. The classes include harmonics, capacitor fast switching transients, capacitor slow switching transients, voltage sudden sag, voltage gradual decay sag, and voltage swell. Each example signal consists of five cycles of a voltage waveform sampled 256 times per cycle, with up to 0.5% added randomly generated noise. We use totally 6000 examples (1000 examples per class) to design the modified Fisher’s Discriminant Kernel and train the 3-layer feedforward neural network. Totally 1800 examples (300 examples per class) are used to test the classification method. All the example events are generated in a random way. The starting time, duration, and distortion magnitude of each training and testing event are all random. This makes the testing results more reliable, because none of these are fixed for real power system disturbance events. Substantial computer simulations have been conducted to optimize the feature extraction algorithm and neural network structure. The input layer of ANN has 13 neurons, hidden layer 8 neurons, and output layer 6 neurons. The 13 inputs to the ANN include 12 feature points extracted from the ambiguity plane and one bias. The 6-point output vector determines which category the disturbance event belongs to. The results for a 6-class classification are competitive and shown in table I.