Power quality disturbances classification based on curvelet transform

Shen, Yue; Hussain, Fida; Liu, Hui; Addis, Destaw

doi:10.1080/1206212x.2017.1398213

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The sudden change in frequency, magnitude, current and phase angle can cause PQ disturbances. Automatic classification and detection of PQ disturbances with appropriate methods have solved this issue [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

et al. 2019

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The excessive use of power semiconductor devices in a grid utility increases the malfunction of the control system, produces power quality disturbances (PQDs) and reduces the electrical component life. The present work proposes a novel algorithm based on Improved Principal Component Analysis (IPCA) and 1-Dimensional Convolution Neural Network (1-D-CNN) for detection and classification of PQDs. Firstly, IPCA is used to extract the statistical features of PQDs such as Root Mean Square, Skewness, Range, Kurtosis, Crest Factor, Form Factor. IPCA is decomposed into four levels. The principal component (PC) is obtained by IPCA, and it contains a maximum amount of original data as compare to PCA. 1-D-CNN is also used to extract features such as mean, energy, standard deviation, Shannon entropy, and log-energy entropy. The statistical analysis is employed for optimal feature selection. Secondly, these improved features of the PQDs are fed to the 1-D-CNN-based classifier to gain maximum classification accuracy. The proposed IPCA-1-D-CNN is utilized for classification of 12 types of synthetic and simulated single and multiple PQDs. The simulated PQDs are generated from a modified IEEE bus system with wind energy penetration in the balanced distribution system. Finally, the proposed IPCA-1-D-CNN algorithm has been tested with noise (50 dB to 20 dB) and noiseless environment. The obtained results are compared with SVM and other existing techniques. The comparative results show that the proposed method gives significantly higher classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

et al. 2019

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“…Most PQ problems result from grid-connected distributed generations. In addition, nonlinear electronic loads introduce PQ disturbances into the grid because electric car chargers, rectifiers, lighting, switch-mode power supplies, and other power electronic devices are commonly used [20].…”

Section: Introductionmentioning

confidence: 99%

An Open Hardware Design for Internet of Things Power Quality and Energy Saving Solutions

Viciana

Alcayde

Montoya

et al. 2019

Sensors

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An important challenge for our society is the transformation of traditional power systems to a decentralized model based on renewable energy sources. In this new scenario, advanced devices are needed for real-time monitoring and control of the energy flow and power quality (PQ). Ideally, the data collected by Internet of Thing (IoT) sensors should be shared to central cloud systems for online and off-line analysis. In this paper openZmeter (oZm) is presented as an advanced low-cost and open-source hardware device for high-precision energy and power quality measurement in low-voltage power systems. An analog front end (AFE) stage is designed and developed for the acquisition, conditioning, and processing of power signals. This AFE can be stacked on available quadcore embedded ARM boards. The proposed hardware is capable of adapting voltage signals up to 800 V AC/DC and currents up to thousands of amperes using different probes. The oZm device is described as a fully autonomous open-source system for the computation and visualization of PQ events and consumed/generated energy, along with full details of its hardware implementation. It also has the ability to send data to central cloud management systems. Given the small size of the hardware design and considering that it allows measurements under a wide range of operating conditions, oZm can be used both as bulk metering or as metering/submetering device for individual appliances. The design is released as open hardware and therefore is presented to the community as a powerful tool for general usage.

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Signal Processing and Deep Learning Techniques for Power Quality Events Monitoring and Classification

Liu

Hussain

Shen

et al. 2019

Electric Power Components and Systems

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Power quality disturbances classification based on curvelet transform

Cited by 6 publications

References 34 publications

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

An Open Hardware Design for Internet of Things Power Quality and Energy Saving Solutions

Signal Processing and Deep Learning Techniques for Power Quality Events Monitoring and Classification

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