Effects of interruption in power supply of induction motors in isolated electrical grid

Mirošević, Marija; Maljković, Zlatko; Gašparac, Ivan

doi:10.1109/acemp.2011.6490691

Cited by 2 publications

(1 citation statement)

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“…The ENN proposed in this paper, replaces the Euclidean distance with the extension distance to calculate the geometric distance between the test samples and clusters for rapid and proper classification and identification. To prove the feasibility of the proposed method, the transient signals of five common power quality disturbances were analyzed, including power interruptions [28,29], voltage surges [30], voltage sag [31], voltage flicker [32], and power harmonics [33]. First, the power transient signals were processed by DWT [34,35] and Parseval's theorem [36,37] to extract the meaningful features and reduce the large data.…”

Section: Introductionmentioning

confidence: 99%

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

Sian

Wang

et al. 2019

Applied Sciences

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The development of renewable energy and the increase of intermittent fluctuating loads have affected the power quality of power systems, and in the long run, damage the power equipment. In order to effectively analyze the quality of power signals, this paper proposes a method of signal feature capture and fault identification, as based on the extension neural network (ENN) algorithm combined with discrete wavelet transform (DWT) and Parseval’s theorem. First, the original power quality disturbance (PQD) transient signal was subjected to DWT, and its spectrum energy was calculated for each order of wavelet coefficients through Parseval’s theorem, in order to effectively intercept the eigenvalues of the original signal. Based on the features, the extension neural algorithm was used to establish a matter-element model of power quality disturbance identification. In addition, the correlation degree between the identification data and disturbance types was calculated to accurately identify the types of power failure. To verify the accuracy of the proposed method, five common power quality disturbances were analyzed, including voltage sag, voltage swell, power interruption, voltage flicker, and power harmonics. The results were then compared with those obtained from the back-propagation network (BPN), probabilistic neural network (PNN), extension method and a learning vector quantization network (LVQ). The results showed that the proposed method has shorter computation time (0.06 s), as well as higher identification accuracy at 99.62%, which is higher than the accuracy rates of the other four types.

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Section: Introductionmentioning

confidence: 99%

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

Sian

Wang

et al. 2019

Applied Sciences

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Influence of Electricity Disconnections on Electric Drives of Industrial Machines and Mechanisms

Sinyavsky¹,

Savchenko²,

Kabanov³

2022

Energy and automation

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Disconnection of consumers from the power supply leads to losses from disruption of technological processes, long downtime and production of defective products, reducing the service life of electrical equipment and appliances, increasing costs and losses of electricity, emergencies. Currently in Ukraine, power outages reach up to 10 % of the total time of technological processes during the year and more. Power outages cause frequent starts and braking of the motor. At a significant frequency of the induction motor, losses in transients cause its intense heating. The aim of the study was to establish the effect of power outages on the temperature of the drive electric motor of industrial machines and mechanisms. A study of the impact of power outages on energy losses and thermal regime of the electric motor was conducted. The dependences of energy losses and allowable frequency of motor starts on the number of outages are determined. It is established that frequent power outages cause an increase in energy losses, which are directly proportional to the frequency of outages. At the same time the admissible frequency of switching on of the electric motor decreases in proportion to quantity of shutdowns.

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Effects of interruption in power supply of induction motors in isolated electrical grid

Cited by 2 publications

References 0 publications

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

Application of Extension Neural Network with Discrete Wavelet Transform and Parseval’s Theorem for Power Quality Analysis

Influence of Electricity Disconnections on Electric Drives of Industrial Machines and Mechanisms

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