The Study Of Multi-levels Power System Overvoltage Recognition Based On Manifold Methods

Overvoltge is one of the key factors for safety operations of power system. There are varieties of overvoltage types which are caused by different reasons happened in power system ever year. Since the overvoltage signal contains plenty of power system information. Utilizing the data from overvoltage on-line monitoring system to identify the types of the overvoltage automatically is quite beneficial for the self-diagnose and safety design or operation of power system.Based on the currently consensus about overvoltage types classification, a new overvoltage multi-level recognition structure is proposed by this paper by considering the multi-level subordination relationship of different overvoltage types. Unlike the traditional single level recognition structure, the multi-level overvoltage recognition algorithm uses different independent classifier to subdivision the overvoltage type gradually. Since each classifier is independent from each other, different mathematic methods can be used to abstract characteristics parameters and construct classifier algorithm. As a result, each classifier can classify overvoltage pertinently with high efficient and the whole multi-level recognition algorithm is easy to be modified.The happening reason, developing process and waveform characteristics of lightning overvoltage, power frequency overvoltage, operation overvoltage and resonance overvoltage are introduced in this paper. Wavelet theory is adopted to abstract the characteristics of overvoltage energy distribution in time-frequency space. Also, due to the unique advantage of S transform theory and singular value theory in reducing the signal random disturbance, the two theories above are combined together to abstract overvoltage characteristics in this paper. For lightning overvoltage, shield failure overvoltage and back flashover recognition, this paper suggests use front part of transmission line current wave as research object because the front part of current wave can avoid the disturbance of wave reflection. The time domain characteristic parameters of transmission current wave are raised to identify the three kind of lightning overvoltage above. By comprehensively considering the mathematic method above and the task of each classifier, this paper chooses different characteristic parameters abstraction mathematic methods for each classifier pertinently.At last, the basic principles of support vector machine and particle swarm optimism algorithm are introduced. Support vector machine, which is suitable for classify data in finite sample set condition, is adopted as classify algorithm cause there is no too much field overvoltage data. The effect of multi-level recognition structure to classify rate is discussed in the paper and it is found that in order to maintain high classify rate, suitable initial value for the parameters of support vector machine is necessary. The particle swarm optimism algorithm is introduced to improve the support vector machine. The particle swarm optimism takes the classify rate as fitness function and the improved support vector machine is used to classify the overvoltage type. The testing calculation demonstrates that since the particle swarm optimism algorithm is quite insensitive to itself initial parameters value and has good performance in convergence, it is quite suitable to improve the support vector machine. The compare of three kinds of classify algorithm shows that the particle swarm optimism algorithm optimized support vector machine can greatly remedy the disadvantage of multi-level structure and has better performance than common support vector machine and BP artificial networks under same condition.