Back-Propagation Control Algorithm for Power Quality Improvement Using DSTATCOM

Singh, Bhim; Arya, Sabha Raj

doi:10.1109/tie.2013.2258303

Cited by 138 publications

(61 citation statements)

References 26 publications

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“…In fundamental frequency switching scheme the devices are turned on and off once in a cycle, thereby producing less switching losses. Generally, the switching angles at fundamental frequency are computed by solving a set of nonlinear equations known as Selective Harmonic Elimination (SHE) equations such that certain order harmonic components (generally lower order) are eliminated (Yao and Xiao, 2013;Singh and Arga, 2014). Alternatively, the switching angles can be calculated by using some optimization based technique so that Total Harmonic Distortion (THD) up to certain order (generally up to 49th order) is minimized instead of eliminating some individual harmonic components (Ramana et al, 2012).…”

Section: Selection Of Switching Anglementioning

confidence: 99%

Embedded Controlled Multi Level Voltage Source Inverter Based Distribution Statics Synchronous Compensator

Kishore¹

2014

American Journal of Applied Sciences

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Section: Selection Of Switching Anglementioning

confidence: 99%

Embedded Controlled Multi Level Voltage Source Inverter Based Distribution Statics Synchronous Compensator

Kishore¹

2014

American Journal of Applied Sciences

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“…The apparent advantages such as fast dynamic response, better steady state and transient stability, robustness, improved tracking and adaptive ability, accuracy and precision under parameter variation make neural network controller superior than other controllers [19]. In the control of DSTATCOM, different ANN structures have been implemented for the extraction of fundamental or harmonic load current such as adaptive neuro-fuzzy interference system (ANFIS) [20], the radial-basis-function neural network (RBFNN) [21], Adaptive Linear Neuron (ADALINE) [22], Hopfield-type [23], Anti-Hebbian [24], and Back propagation (BP) [25]. The BP and ADALINE neural network control strategies are preferably used for the control of DSTATCOM.…”

Section: Introductionmentioning

confidence: 99%

ANN based controller for three phase four leg shunt active filter for power quality improvement

Jayachandran

Sachithanandam

2016

Ain Shams Engineering Journal

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In this paper, an artificial neural network (ANN) based one cycle control (OCC) strategy is proposed for the DSTATCOM shunted across the load in three phase four wire distribution system. The proposed control strategy mitigates harmonic/reactive currents, ensures balanced and sinusoidal source current from the supply mains that are nearly in phase with the supply voltage and compensates neutral current under varying source and load conditions. The proposed control strategy is superior over conventional methods as it eliminates, the sensors needed for sensing load current and coupling inductor current, in addition to the multipliers and the calculation of reference currents. ANN controllers are implemented to maintain voltage across the capacitor and as a compensator to compensate neutral current. The DSTATCOM performance is validated for all possible conditions of source and load by simulation using MATLAB software and simulation results prove the efficacy of the proposed control over conventional control strategy. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…Literatürde, son yıllarda D-STATKOM ile ilgili çalışmaların sayısı artmış olup, kontrolcüsü zaman veya frekans düzleminde referans sinyalleri üretilerek kompanzasyonu gerçekleştirmektedir. Örnek olarak ani gerilim ve/veya akım vektörlerinin zamana bağlı sinyalleri algılanır ve bu sinyaller DQ Dönüşümü [1][2][3][4], PQ Dönüşümü [5][6][7], geri yayılım [8], karma gözlemci tabanlı [9], vektör kuantalamalı öğrenme [10], uyarlamalı eş-zamanlı referans çıkarma [11][12], yapay sinir ağları tabanlı Anti-Hebbian algoritması [13], üçgen fonksiyon karakteri [14], ayarlanabilir adımlı en küçük kareler [15], Adaline yapay sinir ağları [16] …”

Section: Gi̇ri̇ş (Introduction)unclassified