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The stressful condition of power system and high transmission losses leads to voltage security and reliability problems of system operation. If the voltage stability is not assessed and the problems occurred are not mitigated properly, cascading events may occur and this may lead to voltage collapse or blackout events. Therefore, there is a need to assess the voltage stability in order to detect the voltage collapse point and mitigate it when it occurs. This paper aims to compare four existing voltage stability index performance towards reactive and real power load changes. The performances are analyzed theoretically, and using simulation on IEEE 30-bus test system. Total power loss is also observed for each case. The comparison shows that all indices are prone to either reactive or real load power changes only. None of the indices are sensitive towards both power loads changes. Meanwhile, the total power loss is mostly contributed by reactive power losses. Index Terms-Line Stability Index, Voltage stability analysis, Voltage Collapse, Maximum permissible load

Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable.