A Hybrid Algorithm for Recognition of Power Quality Disturbances

Kaushik, Rajkumar; Mahela, Om Prakash; Bhatt, Pramod Kumar; Khan, B. Zorina; Padmanaban, Sanjeevikumar; Blaabjerg, Frede

doi:10.1109/access.2020.3046425

Cited by 26 publications

(11 citation statements)

References 22 publications

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“…A decision tree-based algorithm is used to classify PQDs [17]. In many reviews, the types and classification methods of PQDs have been explained in detail [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

Topaloğlu

2022

J. Electr. Eng. Technol.

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Power quality is one of the most important research eras for the energy sector. Suddenly dropped voltages or suddenly rising voltages and harmonics in energy should be identified. All of these distortions are called power quality disturbances (PQDs). Deep learning based convolutional artificial neural networks with an attention model approach has been carried out. The main idea is to develop a new approach to convolutional neural network (CNN) based which classifies a particular power signal into its respective power quality condition. The attention model approach is based on the idea that the best solution will be taken from the newly produced data pool obtained by rescaling the available data according to the total number of pixels before the average data pool is created and then deep CNN processes will continue. In the attention model approach, all data is multiplied by the number of elements by the number of epoch time sixty-six tensors. The dataset used here contains signals which belong to one of the 9 classes. This means that each signal is characterized by 622 data points and 5600 data parameters. All signals provided are in time domain. Power quality (PQ) is directly depending on power disturbances’ absence or scarcity. The accuracy and error values of the developed model were obtained according to both the number of epochs and the number of iterations.

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“…A decision tree-based algorithm is used to classify PQDs [17]. In many reviews, the types and classification methods of PQDs have been explained in detail [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

Topaloğlu

2022

J. Electr. Eng. Technol.

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“…To illustrate the application of the proposed approach to detect and classify PQ problems, 16 different PQ events are generated using the equations depicted in Table 1 [20]. These events include the standard issues; sag, swell, interruption, flicker, transients, harmonics distortion, notch and spike, as well as combination of these issues MATLAB platform is used to generate the waveforms including the PQ issues.…”

Section: A Generation Of Waveforms With Pq Issuesmentioning

confidence: 99%

“…WT performance depends on the selection of the mother wavelet and the number of decomposition layers [15]. The S-transform (ST) is also used to identify PQ events and it is an extension to wavelet transform [16][17][18][19][20][21]. ST decomposes the waveform in both time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Approach for Power Quality Disturbances Recognition Based on Kalman Filter

2021

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This paper presents a new automatic detection and classification approach of power quality (PQ) problems using Kalman filter. Kalman filter is used as an estimator to calculate the fundamental frequency and harmonic components amplitudes of the voltage or current signals. Then the instantaneous total harmonic distortion (iTHD) and the energy are calculated. For each half cycle of the processed signal, five decision quantities are calculated based on iTHD and energy and these quantities are the three consecutive maximum values of iTHD, standard deviation and energy difference between distorted signal and its fundamental frequency component . Decision rules based on these decision quantities are applied to identify and classify the PQ events in this captured signal. The proposed approach is tested on single and combined PQ events that are generated using the MATLAB with the help of mathematical models that are conformity with standard IEEE-1159. The performance is assessed using more than 100 dataset of every PQ event and the results show that the accuracy is 100 and 98.8 for noiseless and high-level of noise, respectively. In addition, the proposed approach performance is validated through comparisons with other classification. Several practical PQ events are generated by lab experiments to validate the proposed approach. The simulation and experimental results show that the proposed approach is efficient and robust and can be implemented to design PQ monitoring device.

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“…To reduce this impact, filtering and compensation tools are being developed. Violations of the electrical power quality are not introduced into the metering system due to the lack of a valid assessment tool (Kaushik et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Electrical power components decomposition of periodic polyharmonic current

Bialobrzheskyi

Rodkin

Gladyr

2022

COMPEL

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Purpose At the current stage of electrical technology development, it is relevant to take into account the quality of electrical energy. It can be implemented if an assessed energy quality indicator is available. The amount of electrical energy is determined by active power, which is transmitted over a certain time period. In some cases, reactive power is included in the metering system. The distortion power is justifiably criticized and is not taken into account. The purpose of this paper consists in the substantiation of the indicator of the distortion of the periodic polyharmonic current electrical energy power, by separating from the instantaneous power such harmonics, which formed by same frequencies current and voltage harmonics. Design/methodology/approach Using the method of calculating linear polyharmonic current circuits, the following quantities are identified in instantaneous power: active, reactive and apparent powers of each harmonic. These components are known from references as canonical. Findings By the method of instantaneous power harmonic analysis, the components formed by current and voltage harmonics of the same frequency and different frequencies are distinguished. Research limitations/implications The RMS value of the instantaneous power due to current and voltage harmonics of different frequencies is justified in the work. This quantity allows you to distinguish the instantaneous power distortion level in comparison with the existing quantity. Practical implications The results can be used to assess the level of instantaneous power distortion level in commercial and technical metering systems. Originality/value The definition of instantaneous power distortion by extracting the canonical components from it and determining the root mean square value of the remainder is proposed.

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A Hybrid Algorithm for Recognition of Power Quality Disturbances

Cited by 26 publications

References 22 publications

Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

Deep Learning Based a New Approach for Power Quality Disturbances Classification in Power Transmission System

A Generalized Approach for Power Quality Disturbances Recognition Based on Kalman Filter

Electrical power components decomposition of periodic polyharmonic current

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