An optimal placement model of UPFC using Hybrid Taylor series-based spider monkey-teaching learning

Due to the inconsistent nature of the system, the power system uncertainties put forward numerous operational challenges that lead to enhanced risk. Also, minimizing the predictability of the power system makes the operational decisions becomes more challenging and undesirable. Therefore, optimal location, with the real and apparent power flow of a Unified Power Flow Controller (UPFC), is accomplished for enhancing the rate of predictability and minimizing the loss. In this paper, a novel Taylor Spider Monkey-Teaching Learning (TSMO-TL) algorithm is proposed which is the integration of two optimization algorithms namely the Taylor spider monkey as well as the teaching-learning algorithm. Here, the novel TSMO-TL algorithm is employed to optimize the location for situating the UPFC by the objective function (i.e. active power loss and active power flow predictability index). In addition to this, the TSMO-TL approach attains the optimal UPFC placement by considering the input bus voltage, line loss as well as angle. Finally, the proposed TSMO-TL algorithm is utilized in the IEEE-57 bus system to determine its effectiveness. Then the novel TSMO-TL method proved the effectiveness of the system with respect to active power loss, power flow predictability index as well as computational time.