ANN based control scheme for a three-level converter based unified power quality conditioner

Vinnakoti, Sudheer; Kota, Venkata Reddy

doi:10.1016/j.jesit.2017.11.001

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…Each layer is made up of a variable number of neurons with the neurons in the last layer present the outputs of the system. [15][16][17][18][19][20][21][22][23] In this study, we used an MLP network with (1:5:2) structure as shown in Figure 4. (1:5:2) structure comprises an input layer that receives data (modulation index M), a hidden layer composed of five neurons and an output layer formed of two neurons (switching angles θ 1 and θ 2 ).…”

Section: The Ann-based She Techniquementioning

confidence: 99%

“…The multilayer perceptron ( MLP ) network type is organized into several layers in which information circulates starting the input to the output layers; it is a feed‐forward NN . Each layer is made up of a variable number of neurons with the neurons in the last layer present the outputs of the system 15–23 . In this study, we used an MLP network with (1:5:2) structure as shown in Figure 4.…”

Section: The Ann‐based She Techniquementioning

confidence: 99%

“…[15][16][17] Kermadi et al 18 proposed the study of AI-based maximum power point tracking controllers (MPPTs) for photovoltaic (PV) systems. Vinnakoti et al 19 have presented a three-level converter-based unified power quality conditioner (UPQC) using ANN-based control scheme. Mohamed et al 20 have proposed a neural network (NN) for model predictive control (MPC) of a three-phase inverter.…”

Section: Introductionmentioning

confidence: 99%

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A neural network‐based selective harmonic elimination scheme for five‐level inverter

Maamar

Helaimi

Taleb

et al. 2021

Circuit Theory & Apps

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This paper presents the implementation of selective harmonic elimination (SHE) in a five-level inverter structure using artificial neural networks (ANNs). SHE is an effective low-frequency modulation technique to eliminate selected harmonics and control multilevel converters. The use of ANN-SHE requires the calculation of the optimum values of switching angles via the solving system of nonlinear equations for the total harmonic distortion (THD) reduction, where the nonlinear equations are founded by the complex Fourier series analysis of the inverter output voltage. The procured switching angle values are directly implemented by a multilayer perceptron (MLP) algorithm without a lookup table. The ANN model is obtained by training the neural network (NN), taking the modulation index (M) as an input and approximating switching angles as an output. A thorough analysis was carried out to show the programming steps of the proposed ANN-based SHE using Matlab/ Simulink environment. A realized inverter prototype steered by the proposed ANN-based SHE was tested with various modulation indexes on a real-time mode using a digital signal processor (DSP) C2000 Delfino-TMS320F28379Dembedded board. A comparison between the simulation results and the experimental data is presented. The obtained results illustrate that the experimental results match the simulation closely, and the ANN model provides a fast and precise estimate of the switching angles for each value of the modulation index.

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Section: The Ann-based She Techniquementioning

confidence: 99%

Section: The Ann‐based She Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neural network‐based selective harmonic elimination scheme for five‐level inverter

Maamar

Helaimi

Taleb

et al. 2021

Circuit Theory & Apps

View full text Add to dashboard Cite

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“…Nevertheless, the strategy is applicable where load periodicity is not constant. A Multi-level diode clamped converter (DCC) ANN-based UPQC has been evaluated with distorted supply voltage and unbalanced non-linear loads in [17,18] to eliminate the mathematical model and complex controllers. An ANN-based controller for current control of shunt APF has been presented in [3] to replace the PI controller over the ANN controller to encounter voltage related issues.…”

Section: Introductionmentioning

confidence: 99%

Implementation of ANN Controller Based UPQC Integrated with Microgrid

et al. 2022

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This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown.

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“…Nonetheless, they require a lot of time and effort to be implemented in order to avoid the overfitting of the data while maintaining a good accuracy. [41][42][43][44] For the case in which EV chargers are used as APFs, [45][46][47][48] they success in reducing harmonic content but they also involve a high development cost, even more if they are designed for both reducing harmonics and giving grid support. 49,50 Many control strategies focus on selective harmonic elimination (SHE) algorithms, [51][52][53][54] but most are based on multilevel converters, which despite their better harmonic response are more expensive in cost, and require more volume and weight as they consist of more elements.…”

Section: Introductionmentioning

confidence: 99%

Vehicle‐to‐grid charging control strategy aimed at minimizing harmonic disturbances

et al. 2021

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This paper presents the design and implementation of a control for an electric vehicle charger to provide vehicle to grid (V2G) services while reducing the current total harmonic distortion (THD). The control is composed of two current loops. The main one controls the active and reactive powers in a decoupled way, and the second one, focuses on the reduction of the fifth order harmonic component. After explaining the mathematical modelling, the meth-

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ANN based control scheme for a three-level converter based unified power quality conditioner

Cited by 17 publications

References 14 publications

A neural network‐based selective harmonic elimination scheme for five‐level inverter

A neural network‐based selective harmonic elimination scheme for five‐level inverter

Implementation of ANN Controller Based UPQC Integrated with Microgrid

Vehicle‐to‐grid charging control strategy aimed at minimizing harmonic disturbances

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