Islanding detection approach with negligible non-detection zone based on feature extraction discrete wavelet transform and artificial neural network

Summary This work introduces an optimized wavelet designed by fractionally delaying the coefficients of the unit delay filter. The wavelets produced by optimized fractionally delayed filter coefficients are named as fraclets. Fraclet has been utilized in the real‐time power quality events' detection and classification which has been hitherto addressed with the help of unit delay filters. Normal signal, voltage sag, swell, harmonics, sag with harmonics, swell with harmonics, sag and swell with harmonics, and sag and swell with interruption are considered in this work to validate the performance of proposed algorithm based on fraclet. Along with real‐time generation of various power quality events with TMS320C6748 DSP board, different events have also been simulated by using parametric equations. The upper hand of fraclets over wavelets has been highlighted by maximally flat frequency response of fraclets. Moreover, fraclets facilitate better multiresolution analysis by providing low energy compaction ratio (ECR). Further, 11 characteristic features have also been extracted from each decomposition level of PQ signal up to fifth level and fed to the modified probabilistic neural network (MPNN) for validating the proposed power quality event detection and classification algorithm. MPNN has outperformed the support vector machine (SVM), both with fraclet and wavelet. The proposed algorithm relying on fraclets has shown better results as compared with wavelet.

Section: Introductionmentioning

confidence: 99%

Optimum fractionally delayed wavelet design for PQ event detection and classification

Saini

Beniwal

2017

“…any disturbance is appreciable, but a proper threshold setting and absolute features selection are desperately needed to differentiate the islanded condition from nonislanded condition accurately. To overcome this limitation of proper threshold setting, the intelligent classifiers are used after the feature extraction stage to segregate islanding and nonislanding events . In this work, an ELM classifier is generally used to detect islanded signals by considering its comparative advantages of a high value of detection accuracy and execution speed than other existing classifier …”

Section: Discussionmentioning

confidence: 99%

“…However, an associated wide nondetection zone and the necessities of absolute threshold settings are the 2 major drawbacks of passive methods. With the application of signal processing (SP) and intelligent classifier‐based methods, the abovementioned limitations can overwhelm to a greater extent . Mostly the SP‐based IDMs are based on integral transform and frequency domain analysis because its application in some cases may lead to the huge computational burden.…”

Section: Introductionmentioning

confidence: 99%

Loss of main detection in distribution generation system based on hybrid signal processing and machine learning technique

Mishra

Rout

2018

Summary This manuscript presents a novel approach of islanding detection based on hybrid signal processing techniques comprised with mathematical morphology and wavelet transform. The combination of mathematical morphological filter and translational invariant wavelet is considered as a preprocessing filter unit to decrease the random noises in the original power signal. This technique uses the 2 basic mathematical morphology operator erosion dilation difference filter and opening‐closing difference operator to operate on negative sequence voltage and current signal received at target distribution generation bus for the extraction of different useful features. An extreme learning machine‐based machine learning technique is further used as a classifier to discriminate islanding events from nonislanding events. Apart from that, another islanding detection method based on the combination of proposed preprocessing filter unit and wavelet transform is presented. The comparative result demonstrates improved accuracy for the proposed approach of islanding detection and classification, even under certain extremely noisy conditions.

“…Over the past years, several identification methods have been introduced. These methods are mostly classified into remote and local techniques . Moreover, we can consider local detection method as a passive and active method .…”

Section: Introductionmentioning

confidence: 99%

A hybrid islanding detection technique for inverter‐based distributed generator units

Seyedi

Taher

Ganji

et al. 2019

Summary Due to high importance of islanding detection as an essential protection requirement, especially for the distributed generation (DG), a new approach to islanding detection is developed in the present paper. The method is based on a mixture of both active and passive algorithms, which uses voltage phase angle (VPA) and voltage unbalance (VU). The suggested method presents a combination of benefits of each individual passive/active method, and therefore it is useful to appropriately detect islanding situation in a wide range of scenarios. The use of the suggested method results in smaller nondetection zone (NDZ), faster response, and improved power quality. In order to illustrate the effectiveness of this approach, simulation results are provided under different conditions, showing that the suggested algorithm can reach the required islanding protection requirements. In addition, the performance of the proposed method and some other methods are compared by the presence of a variety of transient faults and insignificant power mismatch.