An analysis of costs related to the loss of power quality

Pereira, Fernanda Cristina Oliveira; Souto, O. C. N.; Oliveira, José Carlos de; Vilaca, A.L.A.; Ribeiro, Paulo F.

doi:10.1109/ichqp.1998.760141

Cited by 20 publications

(5 citation statements)

References 4 publications

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“…In addition, industries like information technology and modern manufacture industries with sensitive loads are placed with high demand for power quality [3]. Power quality is becoming as a serious concern in the electric power grid and also higher impact on industries [4]. The concept of deregulation in the power sector industries and manufacture of semiconductor-based power equipment under various ranges is driven by the demand for higher power quality and the need for advanced mitigation techniques for PQ disturbances to ensure reliable and stable operation of various loads.…”

Section: Introductionmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

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The accurate classification of power quality disturbances (PQDs) is crucial for advancing real‐time monitoring and classification systems within the modern power grid. The proposed system must ensure dependable, safeguarded, and stable operating conditions amidst diverse power quality issues. This paper presents an approach to classifying power quality disturbances using a deep learning model that synergizes deep convolutional neural networks (DCNN) and Bidirectional Long Short‐Term Memory (BiLSTM). This amalgamation effectively extracts and classifies disturbance signals in real time, grounded on noise levels. The initial feature extraction from the signal is accomplished through a time‐frequency matrix. Subsequently, secondary extraction employs the BiLSTM layer to intricately and significantly classify disturbances in the power signal. This aids in transforming high‐dimensional matrices into a reduced set for enhanced performance. The detailed classification is facilitated by the softmax layer. The simulation results support the power quality evaluations under varied constraints and underscore the substantial classification of power quality disturbances through the DCNN‐BiLSTM algorithm, in comparison to alternative classification algorithms in terms of computational speed and accuracy.

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Section: Introductionmentioning

confidence: 99%

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Kandasamy,

Kumar,

Lakshmanan

et al. 2023

IET Generation Trans & Dist

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“…Furthermore, power electronic devices are typically employed to enable loads to operate with high capacity, high performance and low cost [2]. Nevertheless, they present a drawback as these devices are commonly represented by non-linear loads, which affect the grid quality and increment the electrical losses [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal planning and operation of static VAR compensators in a distribution system with non‐linear loads

Alvarez-Alvarado

Rodríguez-Gallegos

Jayaweera

2018

IET Generation, Transmission & Distribution

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…One of the major reasons of voltage unbalance is an uneven distribution of single phase loads that draw unbalanced currents from the system [10,11]. These unbalance currents will create unequal heating in each of the phases which creates unbalance heating in cables and other parts of the network, which might reduce the life time of the cables and other components [12,13]. Another reason for voltage unbalance is due to unbalanced faults.…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Unbalanced Voltage Sags and Voltage Unbalance in CIGRE Low Voltage Distribution Network

Bhutto¹,

Bak‐Jensen²,

Mahat³

et al. 2013

EPE

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Any problem with voltage in a power network is undesirable as it aggravates the quality of the power. Power electronic devices such as Voltage Source Converter (VSC) based Static Synchronous Compensator (STATCOM) etc. can be used to mitigate the voltage problems in the distribution system. The voltage problems dealt with in this paper are to show how to mitigate unbalanced voltage sags and voltage unbalance in the CIGRE Low Voltage (LV) test network and networks like this. The voltage unbalances, for the tested cases in the CIGRE LV test network are mainly due to single phase loads and due to unbalanced faults. The compensation of unbalanced voltage sags and voltage unbalance in the CIGRE distribution network is done by using the four STATCOM compensators already existing in the test grid. The simulations are carried out in DIgSILENT power factory software version 15.0.

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An analysis of costs related to the loss of power quality

Cited by 20 publications

References 4 publications

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Initial condition based real time classification of power quality disturbance using deep convolution neural network with bidirectional long short‐term memory

Optimal planning and operation of static VAR compensators in a distribution system with non‐linear loads

Mitigation of Unbalanced Voltage Sags and Voltage Unbalance in CIGRE Low Voltage Distribution Network

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