Neural Network Based Indexing and Recognition of Power Quality Disturbances

Abstract

AbstrakAnalisis kualitas daya atau power quality (PQ) Kata Kunci: jaringan syaraf tiruan umpan maju, kualitas daya, pengenalan, transformasi wavelet kontinyu Abstract Power quality (PQ) analysis has become imperative for utilities as well as for consumers due to huge cost burden of poor power quality. Accurate recognition of PQ disturbances is still a challenging task, whereas methods for its indexing are not much investigated yet. This paper expounds a system, which includes generation of unique patterns called signatures of various PQ disturbances using continuous wavelet transform (CWT) and recognition of these

An Accurate Classification Method of Harmonic Signals in Power Distribution System by Utilising S-Transform

“…Presently, the enthusiasm on studies of PQ issue, especially on harmonic disturbances exponentially increased because of all power equipment have turned out to be less tolerant to terrible power quality. The observing of harmonic disturbances becomes crucial with a specific end goal to study the characteristic of the power system, to distinguish, to measure and mitigate the problems [2,3]. Thorough research is required for creating exact strategies for the harmonic signal detection and classification [4].…”

Power Quality Warning of High-Speed Rail Based on Multi-Features Similarity

“…Furthermore, most PQ researches also focused on the detection and identification of PQ disturbances and PQ evaluation [10][11][12][13]. Gupta et al [10] proposed a recognition method for PQ disturbances that is based on continuous wavelet transform and feedforward neural network. He et al [11] proposed a realtime power quality disturbances classification by using S-transform and dynamics.…”

Early Model of Traffic Sign Reminder Based on Neural Network

“…Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for driver assistance systems. One of techniques that can be used to recognize traffic sign patterns is Neural Network [5][6][7][8]. An implementation of TSR detection is to produce sign, to stop sign, and red-bordered circular sign can be done using these steps.…”

“…By neural network toolbox, the best network from certain epoch will be saved, thus it will be used to test the image data. Testing the system using webcam and saved network will produce the recognition image, and user will get information about the meaning of the traffic sign that is recognized [5], [8]. The activity diagram of the process is shown in Figure 3.…”