Abstract
On-line Transient Stability Assessment (TSA) is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN). The real and reactive power loads are taken as input features for training of the neural network. Principal Component Analysis (PCA) is used for dimensionality reduction of the input data set to select informative features. The proposed method is tested on IEEE-39 bus test system and the results obtained for transient stability assessment through predicted rotor angles are promising.
Highlights
Modern power systems operating close to their stability limits due to economic and operational constraints are more vulnerable to transient instability under severe credible contingency
The proposed Radial Basis Function Neural Network (RBFNN) based Transient Stability Assessment (TSA) approach is tested on IEEE-39 bus New England test system (Pai, 1989), the system consists of 10 generators, 12 transformers and 46 transmission lines
Training and testing data generation: The real and reactive loads are varied from 95 to 105% of the base case in steps of 1% and for each load topology 100 patterns are generated by randomly varying all loads, which covers a wide range of scenarios, the two contingency are simulated for each load pattern as mentioned in Table 1: three phase fault at bus-28 cleared by opening the line 28-26 and three phase fault at the midpoint of line 21-22 cleared by opening the line
Summary
Modern power systems operating close to their stability limits due to economic and operational constraints are more vulnerable to transient instability under severe credible contingency. On-line Transient Stability Assessment (TSA) involves generation of off-line data for varying operating conditions under probable contingencies and predicting the future state on-line for given operating scenarios under predefined set of contingencies so that preventive actions can be taken to enhance the transient stability (Morison, 2006). Preventive actions are derived based on the predicted state of the system for particular operating scenarios. It becomes imperative to find the correct post-fault scenarios so that suitable preventive actions can be taken (Vega and Pavella, 2003). The challenging task in implementation of energy based methods for on-line TSA is to find the function that defines the transient energy of the large and complex power system and simultaneously finding the critical transient energy of the system under given disturbance
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