Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2019Scopus SJRQ2/r/munzur/3419
  Classification of multiple power quality events via compressed deep learning

  Liu, Hui; Hussain, Fida; Yue, Shen; Yildirim, Ozal; Yawar, Sheikh Junaid

  This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine-tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS- and SAE-based DL algorithms can be efficiently used for single and multiple PQDs classifications.

  AtıfScopus28·OpenAlex28

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• MakaleAçık Erişim2018Scopus SJRQ2/r/munzur/3484
  A new embedded power quality event classification system based on the wavelet transform

  Eristi, Belkis; Yildirim, Ozal; Eristi, Huseyin; Demir, Yakup

  In this paper, a real time embedded intelligent recognition system is proposed for diagnosing the power quality problems. The intelligent recognition system has a structure capable of classifying and detecting the power quality problems in real time. Hardware applications of the wavelet transform and decision tree classifier are realized inside the recognition system using with field programmable gate array (FPGA) device. These methods operating as embedded in the FPGA environment provide real-time diagnosis of power quality problems. A new approach of this recognition system is its capability to simultaneously detect a power quality event in the power systems and power quality disturbances occurring on each phase following the event. In this paper, 2 different recognition systems that online and offline are presented. The online recognition system operates in the fields with signal processing and classification structures embedded. In the offline system, the distinctive features of the event signals obtained from the online system are used and these signals are classified in the computer environment by means of the least square support vector machines. A prototype model of the power system is created in the laboratory environment in order to test the FPGA-based online intelligent recognition system, determine accuracy rates and evaluate its performance. Power quality event types are created in wide parameter ranges on this model. Obtained results from both recognition systems indicated the hardware and software designs of our embedded systems are quite effective, fast, and have high success performance.

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  AtıfScopus48·WoS35·OpenAlex50

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