Fault diagnosis of substation by the constructed decision tree based on principal component analysis(PCA) and rough set
Zhao Ying-kai · Power System Protection and Control · 2010
A method for substation fault diagnosis based on principal component analysis(PCA) and rough set theory,then to constructed decision tree,is proposed.By this method,PCA is used to decrease the dimension of all the condition attributes of the original fault decision table and get fault decision table that consists of principle component variables.Then,an equal-frequency devision method is used to discrete the value of the above decision table.The next is that a rough attribute reduction based on the significance of principle component variables is applied to obtain a minimum reduction of the discrete decision table.Finally,based on the core attributes,an improved decision tree algorithm is used to train the reducted data set and construct a decision tree for the diagnosis.Test proved that this method can simplify the fault diagnosis system,extract the diagnosis rules of better fault tolerance and increase the fault recognition rate.