A new embedded power quality event classification system based on the wavelet transform

Summary Recently, both utilities and consumers have paid great attention to voltage fluctuation (flicker) problem, which is defined as low‐frequency fluctuation on the base signal of voltage. For mitigating the flicker, identification of its source is the first step. In this paper, a new method is proposed for identifying the location of flicker sources in a multi‐flicker sources power network in a more reliable and precise manner by a minimum number of monitoring points. This method is based on the wavelet transform (WT), the approximate model of the line and flicker power theory. In addition, in this method, a new factor is proposed for determining the contributions of individual consumers and utility providers in the global flicker level of a common coupling bus‐bar. For validating the performance of the proposed method, comprehensive simulations have been performed in MATLAB environment on a single‐ and multi‐flicker sources system based on IEEE 34 node test feeder. As proved by the results, the proposed method is able to identify the contributions and locations of flicker sources with higher reliability and accuracy compared with other methods.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method with minimum number of monitoring points for flicker source tracing by wavelet transform

Banaem

Abbasi

Tousi

2019

“…The features are generated from the selected band limited IMF estimated from adaptive VMD and XVMD for different PQ disturbances along with faults and islanding problems . The XVMD based techniques needs simple features to classify the disturbances in less time as compared to the VMD based methods.…”

Section: Feature Extraction From Optimal Blimfsmentioning

confidence: 99%

Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

Dash

Satapathy

Nayak

2020

Summary This article presents the detection and classification of islanding and power quality (PQ) disturbances for a multiple distributed generation based using an adaptive cross variational mode decomposition (XVMD) with reduced kernel ridge regression (RKRR). This article considers photovoltaic as the primary DG and studied the effect of the solar irradiation variation. Further the considered microgrid is subjected to different operating conditions pertaining to both islanding and non‐islanding disturbances. The proposed XVMD is an extension of VMD and with RKRR, it is a new contribution to the earlier studies to deal dynamic detection threshold requirement. The XVMD based RKRR needs simple features to classify the disturbances in less time as compared to the VMD based methods. The features to classify the events are calculated from the optimal mode selected by the hybrid firefly algorithm. For the performance analysis, voltage signals across the PV based DGs are obtained by initiating various power qualities and islanding disturbances. A validation case study through TMS 320C6713 Starter Kit (DSK) is presented for capacitor switching. The robustness of the proposed XVMD‐RKRR is observed from a large number of numerical experimentation by comparing it with other well‐known approaches.

“…Generally, in literature after extraction of PQD signal, the mean root‐mean‐square (rms) values of current and voltage, the coefficient of skewness, standard deviation, entropy, energy, etc, are extracted, and used as a feature vector and input to a classifier. Nevertheless, these techniques are not good enough for multiple disturbances …”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Self Cite

Summary 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.