Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

A novel index for identification of weak nodes for reactive compensation to improve voltage stability

In this study, a new reactive power loss index (RPLI) is proposed for identification of weak buses in the system. This index is further used for determining the optimal locations for placement of reactive compensation devices in the power system for additional voltage support. The new index is computed from the reactive power support and loss allocation algorithm using Y-bus method for the system under intact condition and as well as critical/severe network contingencies cases. Fuzzy logic approach is used to select the important and critical/severe line contingencies from the contingency list. The inherent characteristics of the reactive power in system operation is properly addressed while determining the reactive power loss allocation to load buses. The proposed index is tested on sample 10-bus equivalent system and 72-bus practical equivalent system of Indian southern region power grid. The validation of the weak buses identification from the proposed index with that from other existing methods in the literature is carried out to demonstrate the effectiveness of the proposed index. Simulation results show that the identification of weak buses in the system from the new RPLI is completely non-iterative, thus requires minimal computational efforts as compared with other existing methods in the literature.