On line fault detection for transmission line using power system stabilizer signals

Abstract

It is a well known fact that power systems security is required to smooth power operations and planning. This requires that power system operators at the control centre appropriately handle information on faults and detect these faults effectively. In this study, the “oscillation� for each of the four machines in “no fault condition�, “fault with PSS� and “without PSS “are recorded at various fault conditions for fault detection using a Multi Resolution Analysis (MRA) Wave Transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and condition of the fault. The same features are also fed to the Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) as a fault classifier and the results are compared for analyzing classification rate performance. Once the fault is classified using the above classifier, its location is sent to the lookup table using the online neuro- fuzzy control strategy the optimum value of the gain and time constant for the PSS (Power System Stabilizer) are selected and used to compensate the damping at various fault conditions. Then by using PST(Power System Toolbox) to build state variable models in small signal analysis, and for modeling of machines and control system for performing transient stability simulation of a power system, These dynamic models are coded as MATLAB functions. The expected results will show that the control action of PSS (Power System Stabilizer) using this method is more robust in damping the oscillation compared to the fixed conventional PSS. Hence, this study will show that not only the PSS able to compensate the damping due to the disturbance but also by using the developed algorithm it succeeds to detect and classify the fault conditions on the parallel transmission lines.