RBFNN based GSA for optimizing TCSC parameters and location- A secured optimal power flow approach

摘要

Your abstract goes here. This paper proposes a hybrid technique for securing optimal power flow (OPF) with installation of Thyristor controlled series compensator (TCSC). The hybrid technique is the combination of Radial Basic Function Neural Network (RBFNN) and Gravitational Search algorithm (GSA). Here, RBFNN-GSA provides the new velocity and the position of the agent resulting in superior results for optimized parameters and location of TCSC as compared to traditional GSA and Fuzzy based GSA algorithm. The location of TCSC depends on the loading factor and apparent power flow index and the secured parameters are optimized by RBFNN-GSA. The proposed method is implemented in MATLAB working platform and the power flow security is evaluated with IEEE 6 bus and IEEE 14 bus transmission systems. The performance of the proposed method is evaluated and compared with the other existing methods. Then, the total generated power, power losses, and cost of the generation are examined by changing the system load. In addition, the contingency of the system is analyzed by reducing the line flow limits and the effectiveness of proposed method is confirmed by the results obtained.