| Soft computing includes Artificial Neural Network, Fuzzy Logic, Evolutionary Algorithms, Rough Set (RS) Theory, etc. As a new soft computing, Rough Set can analyze and handle imprecise, inconsistent and incomplete data efficiently. In addition, connotative knowledge and latent rules will be discovered by using Rough Set Theory. Therefore, Rough Set is a powerful tool for analyzing data and is tolerant for faults. Hence, Rough Set can overcome shortages of other soft computing in some aspects. But the researching and application of RS in Power System are very rare. So, using the advantages of RS, the paper does some pilot study on Power System. The majority of work is reported as follows:1. Study on fault diagnosis for distribution network based on Rough Set TheoryThe quick and precise fault diagnosis is the key problem to enhance the reliability of power supply and is one of the most important problems in distribution automation. Nowadays, the real time information of distribution network can be afforded to operators through Supervisory Control and Data Acquisition (SCADA). But, the alarm signals from SCADA have disadvantages as follows: (1) The open-air Feeder Terminal Units (FTU) are interfered by strong electromagnetic and thunder. Besides, the failure of relays and the fault of FTU can also lead to that fault information is interfered and aberrant. (2) In the process of transmission and commutation, the error in the communication equipment may result in the signals err or lose. (3) The malfunction or failing operation of protective relays and circuit breakers may lead to imperfect alarm signals. Since these factors, the alarm signals are imperfect, and make fault diagnosis more difficult.Based on Rough Set Theory, the paper proposes a new distribution network fault diagnosis approach to deal with the imperfect alarm signals. Firstly, a decision table including all kinds of fault cases is established by considering the signals of protective relays and circuit breakers. Then, diagnostic rules are extracted by reducing the decision table. Using the reductions of decision table, diagnosis rules can be obtained directly from fault samples which have been established. The method can tell indispensable fault signals from dispensable ones and discover the inherent redundancy of alarm signal set. In a word, the method can realize effective fault diagnosis when the alarm signals are imperfect.2. Design the scheme of Expert System for transformer fault diagnosis based on DGA and Rough Set TheoryIEEE considers Dissolved Gas Analysis (DGA) is the most efficient method to examine the interior fault of transformer. Because the mechanism of transformer fault is yet not clear and the instruments have some limitation, it is difficult to establish the math model of fault phenomena and causes. Therefore, Expert System, which can simulate the decision-making of experts, has been abroad applied to diagnose the fault of transformers. However, obtaining complete knowledge, as a bottleneck problem, always restrict the development of Expert System.Based on DGA and Rough Set Theory, the paper designs a scheme of expert system for fault diagnosis of power transformer. In order to resolve the bottleneck problem of obtaining complete knowledge of Expert System, a Rough Set approach is mainly proposed to build and maintain knowledge base for transformer fault diagnosis. From the reductions of decision table defined by history fault data, a series of nodes network rule sets, with suitable belief degree under different reductive levels, is developed by calculating the rough subjection degree of every rule. With the increasing of fault samples, the error samples will be submerged by right samples through computing the rough subjection degree of every rule. In this way, the knowledge base is maintained. When the fault information of transformer is given, one can match the information to the rule sets of relative nodes. Even when the data of DGA are imperfect, the diagnosis results are correct.
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