Voltage Variation Signals Source Identification And Diagnosis Method

Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays.It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source identification and diagnosis method by determining the average time frequency representation (TFR) phase power of the impedance. The signals focused in this study are the voltage variation signals, which include voltage sag, swell and interruption. The voltage variation signals from different source location (upstream, downstream as well as up and downstream) according to the IEEE Standard 1159 by using the mathematical models. The signals are first analyzed by using the Spectrograms which act as the feature producing tool. Then, the average power TFR of phase domain of each signal is calculated and tabulated. Finally, the performance of the method is identified by using support vector machine (SVM) and k-nearest neighbor (kNN).The results show that this method is an effective and suitable technique for identifying the source of voltage variatio

Support vector regression and rule based classifier comparison for power quality diagnosis

This paper presents a comparative study for performing automated power quality diagnosis using rule base classifier (RBC) and support vector regression (SVR) to identify the causes of short duration voltage disturbances such as voltage sag and swell. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform was used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The RBC and SVR which are intelligent techniques were then used to identify whether the voltage disturbances were caused by permanent, non-permanent transient or incipient faults. Test results proved that the RBC performed better than the SVR in diagnosing the causes of short duration voltage disturbances