A new method for power system contingency ranking using combination of neural network and data envelopment analysis

In this paper, an integrated algorithm has been proposed for ranking contingencies in the deregulated network. The network security and economical indices should be considered when dealing with market environment. Locational marginal price and congestion cost indices are the best signals to completely illustrate the market operation. In this paper, voltage violation, line flow violation, locational marginal price and congestion cost indices have been simultaneously considered to rank the contingencies. This algorithm uses neural networks method to estimate the power system parameters (locational marginal price, bus voltage magnitudes and angles). The efficiency of each of contingencies was calculated using data envelopment analysis and this index was employed for ranking. The efficiency of each contingency shows its severity and indicates that it affects network security and economic indices concurrently. Considering the proposed formulation for data envelopment analysis, the efficiency of a contingency will be higher if the calculated indices for that contingency are higher. More efficiency leads to increased severity of the contingency and shows that the contingency has concurrently more affected network security and economic indices. The proposed algorithm has been tested on IEEE 30-bus test power system. Simulation results show the high efficiency of the algorithm.