Development of intelligent systems for evaluating voltage profile and collapse under contingency operation

Abstract

Monitoring and control of modern power systems have become very complex tasks due to the interconnection of power grids. These large-scale power grids confront system operators with a huge set of system inputs and control parameters. This work develops and compares intelligent systems-based algorithms which may be considered by power system operators or planners to help manage, process, and evaluate large amounts of data due to varying conditions within the system. The methods can be used to provide assistance in making operational control and planning decisions for the system in a timely manner. The effectiveness of the proposed algorithms is tested and validated on four different power systems. First, Artificial Neural Network (ANN) models are developed and compared for two different voltage collapse indices and utilizing two different-sized sets of inputs. The ANNs monitor and evaluate the voltage profile of a system and generate intelligent conclusions regarding the status of the system from a voltage stability perspective. A feature reduction technique, based on the analysis of generated data, is used to decrease the number of inputs fed to the ANN, decreasing the number of physical quantities that need to be measured. The major contribution of this work is the development of four different algorithms to control the VAR resources in a system. Four different objectives were also considered in this part of the work, namely: minimization of the number of control changes needed, minimization of the system power losses, minimization of the system's voltage deviations, and consideration of the computational time required. Each of the algorithms is iterative in nature and is designed to take advantage of a method of decoupling the load flow Jacobian matrix to decrease the time needed per iteration. The methods use sensitivity information derived from the load flow Jacobian and augmented with equations relating the desired control and dependent variables. The heuristic-sensitivity based method is compared to two GA-based methods using two different objective functions. In addition, a FL algorithm is added to the heuristic-sensitivity algorithm and compared to a PS-based algorithm. The last part of this dissertation presents the use of one of the GA-based algorithms to identify the size of shunt capacitor necessary to enhance the voltage profile of a system. A method is presented for utilizing contingency cases with this algorithm to determine required capacitor size.