Islanding detection techniques for distribution system using minimum power system parameters / Syed Safdar Raza - Syed Safdar , Raza (2016)
Islanding condition is one of the most important protection issue in modern power system, which adversely affects the power quality and reliability. In order to prevent this issue, the current practice is to disconnect the DERs when islanding occurs. Passive techniques are widely used by utility companies, apart from other detection techniques, because of their low cost and minimum disturbance of power quality. However, it shows poor performance if the power mismatch is small. The inclusion of computational intelligent based techniques has foreshadowed a new era for passive techniques. These techniques employ many parameters as an input to intelligent classifier for discrimination between islanding and non-islanding events. Although it produces good results, the usage of many parameters makes it more complex. For real time execution, simple and economical techniques are preferable. This work proposes an intelligent islanding detection technique based on Artificial Neural Network (ANN) that employs minimal features from the power system. The selection of minimal features is made by analyzing the sensitivity of 16 power system parameters which can be used in passive techniques, to detect islanding and non-islanding events. By sensitivity based ranking analysis, it is observed that the rate of change of frequency over reactive power (df/dq) can effectively detect minute disturbances in power supply. It is also shown that active and reactive power mismatch has an opposing effect on the variation of frequency (df) in real time environment. As a result of this, a new passive technique based on df/dq is proposed. The simulation results indicate that the proposed technique is able to distinguish islanding from other non-islanding events. The proposed technique is also compared with conventional islanding detection technique in terms of their non-detection zone. The simulation results show that the proposed technique has absolute discrimination between islanding and other events in a closely mismatched conditions. In order to yield the optimal performance of ANN with minimum number of features, its indices such as learning rate, momentum and number of neurons in the hidden layers are optimized by using Evolutionary Programming (EP) and Particle Swarm Optimization (PSO). The performance comparison between stand-alone ANN, ANN-EP and ANN-PSO in the form of regression value is performed to obtain the best feature combination and optimal data formation for an efficient islanding detection. The proposed technique is tested on- and off-line for various islanding and non-islanding events. The simulation results indicate that the proposed technique can successfully distinguish islanding from other non-islanding events such as load variation, capacitor switching, faults, induction motor starting and DER tripping. Thus, this research proves that islanding detection is technically feasible for the reliability of the power system.