<span lang="EN-US">This paper proposes a new model integrating a linear quadratic regulator (LQR) controller to mitigate frequency disturbances in the power system during cyber-attack, called as linear quadratic regulator to mitigate frequency disturbances (LQRMFD). As we know, most of the existing models have a common problem with achieving significant performances in mitigating dynamic response parameters, such as frequency deviation and settling time. However, the key aspect of LQRMFD is to mitigate the above issues with remarkable performance improvements. An uncommon and stable power system model has been considered in LQRMFD first to reach such a goal. A numerical problem has been solved to derive a certain characteristic equation, where the Routh-Hurwitz array criterion is applied for determining the stability of such a power system. After that, a state-space equation is developed from the power system to activate the LQR controller. Thus, achieving diversity and eliminating the redundancy of the power system considered can be obtained in LQRMFD. To evaluate the performance of LQRMFD, a series of experiments was conducted using the MATLAB-Simulink tool. Rigorous comparisons were also made among the results of LQRMFD, self-implemented and existing models. Furthermore, a detailed analysis was reported among those models to find the performance improvement of LQRMFD in percentage.</span>

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<span lang="EN-US">This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system.</span>

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