Titel: Huvudrubrik: Power System Security Assessment Underrubrik: Application of Learning Algorithms
The last years blackouts have indicated that the operation and control of power systems may need to be improved. Even if a lot of data was available, the operators at different control centers did not take the proper actions in time to prevent the blackouts. This depends partly on the reorganization of the control centers after the deregulation and partly on the lack of reliable decision support systems when the system is close to instability. Motivated by these facts, this thesis is focused on applying statistical learning algorithms for identifying critical states in power systems. Instead of using a model of the power system to estimate the state, measured variables are used as input data to the algorithm. The algorithm classifies secure from insecure states of the power system using the measured variables directly. The algorithm is trained beforehand with data from a model of the power system.
The thesis uses two techniques, principal component analysis (PCA) and support vector machines (SVM), in order to classify whether the power system can withstand an (n-1)-fault during a variety of operational conditions. The result of the classification with PCA is not satisfactory and the technique is not appropriate for the classification problem. The result with SVM is much more satisfying and the support vectors can be used on-line in order to determine if the system is moving into a dangerous state, thus the operators can be supported at an early stage and proper actions can be taken.
In this thesis it is shown that the scaling of the variables is important for successful results. The measured data includes angle difference, busbar voltage and line current. Due to different units, such as kV, kA and MW, the data must be preprocessed to obtain classification results that are satisfactory. A new technique for finding the most important variables to measure or supervise is also presented. Guided by the support vectors the variables which has large influence on the classification are indicated.