In recent years, voltage stability issues have become a serious concern with regard to the safety of electrical systems, these issues are more evident and have wider consequences in vertical networks with an insufficient reactive power reserve. Pakistan is currently suffering from the worst energy crisis in its history. Owing to an increase in energy demand, the current transmission system is becoming increasingly inadequate. It has thus become necessary to reduce losses and enhance the system voltage profile for more efficient energy utilization. In this study, the main emphasis is on assessing the feasibility of using flexible AC transmission system devices and distributed generation to compensate power failures on the power lines of the Pakistani power transmission system. The load flow and contingency analyses are performed on a 132 kV transmission system that feeds power to the Quetta electric supply company. The region of Baluchistan is studied to evaluate the effectiveness of the proposed method. The system is simulated using NEPLAN, which accurately models the details of all system elements and the optimal power flow. The simulation results indicate that the proposed method helps reduce system losses, voltage deviation, and power flow congestion, with all system constraints within permissible limits.

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Transient stability assessment (TSA) plays a critical role in ensuring the reliable operation of power systems. However, existing approaches for TSA often encounter challenges such as data imbalances, limited sample sizes, and the need for adaptability in the face of system changes, necessitating the exploration of more advanced techniques. This paper proposes a novel deep transfer learning (DTL) framework to address these limitations that incorporates CNN-LSTM and stacked denoising auto-encoder (SDAE) techniques, aiming to significantly improve the speed and accuracy of power system TSA, especially in online applications and adaptability to system changes. First, the utilization of SDAE enables effective feature extraction, while the implementation of class weight balancing and cross-entropy loss function techniques effectively addresses data imbalances. Second, a CNN-LSTM classifier is constructed using transfer progressive learning. This approach allows for the effective analysis of spatial and temporal dynamic measurements by leveraging unsupervised pre-training (auto-encoder) and additional CNN-LSTM layers. Third, we propose the DTL, which leverages knowledge transfer from the CNN-LSTM model and incorporates fine-tuning techniques. This innovative approach ensures adaptability under in four scenarios, which is a prevalent challenge in power systems for continuous prediction. As compared with other techniques, the results demonstrate that our proposed approach achieves TSA accuracy of up to 99.68% on the IEEE 39-bus system and 99.80% on the South Carolina 500-bus system. Furthermore, to compare the performance of continuous prediction with other methods, our proposed method exhibits a significant improvement of 2% even with a limited sample size.