Different optimization techniques are used for the training and fine-tuning of feed forward neural networks, for the estimation of STATCOM voltages and reactive powers. In the first part, the paper presents the voltage regulation in IEEE buses using the Static Compensator (STATIC) and discusses efficient ways to solve the power systems featuring STATCOM by load flow equations. The load flow equations are solved using iterative algorithms such as Newton-Raphson method. In the second part, the paper focuses on the use of estimation techniques based on Artificial Neural Networks as an alternative to the iterative methods. Different training algorithms have been used for training the weights of Artificial Neural Networks; these methods include Back-Propagation, Particle Swarm Optimization, Shuffled Frog Leap Algorithm, and Genetic Algorithm. A performance analysis of each of these methods is done on the IEEE bus data to examine the efficiency of each algorithm. The results show that SFLA outperforms other techniques in training of ANN, seconded by PSO.
Summary
In this paper, a 13‐level cascaded H‐bridge medium voltage static synchronous compensator (STATCOM) has been designed for dynamic reactive power compensation. The dynamic performance of STATCOM has been enhanced by finding the optimal PI controller parameters using particle swarm and gravity search algorithms. In order to avoid capacitor voltage drifting, individual voltage balance control loop is introduced. The results of optimization methods on finding optimal PI controller parameters of STATCOM are verified and compared by simulation studies. The results show that each optimization technique renders effective tracking of both, DC‐link voltage reference values, direct current, reactive current, and reactive power reference values during the closed‐loop operation of STATCOM.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.