Power quality improvement in an AC network using artificial neural network and hysteresis band current  controller

Sulehri, Abdul Mutal

doi:10.15446/ing.investig.v38n3.67885

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…As illustrated in Figure 4, the PI controller is a feedback controller that regulates the overall error as well as the integral value. The compensating computer is controlled by a multi-layer back propagation in ANN [19]. The toolbox trains ANNs [20].…”

Section: A Mathematical Model For Voltage Injection By Dvrmentioning

confidence: 99%

Improvements in Voltage Profile of JDW Sugar Mills’ Jawar Distribution Feeder RYK Pakistan Using ANN Based Dynamic Voltage Restorer

Siddique,

Mujahid,

Aslam

et al. 2024

JESA

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Voltage-related power quality issues, including voltage sag, swell, and total harmonic distortion (THD), have become a significant concern in recent times. These issues, particularly harmonics, are known to degrade utility performance and lifespan, necessitating urgent rectification to ensure a high-quality power supply. This is crucial as our generation increasingly depends on electricity for enhanced living standards. Flexible AC transmission system (FACTS) devices are gaining considerable interest as effective solutions to these problems. Among these, the dynamic voltage restorer (DVR) is particularly noteworthy for its potential to reduce power quality disturbances in the distribution network. In this study, we developed a DVR based on an artificial neural network (ANN) controller. The activation function employed was Train LM for the input and hidden layers, and pure linear for the output layer, with the Levenberg Marquardt back propagation (LMBP) serving as the training algorithm. The designed model was then tested to tackle voltage-related power quality problems in the distribution network of Jamal Din Wala (JDW) sugar mills. The comprehensive model featured a three-phase voltage source inverter, a scheme utilizing rotating reference frame theory, and sine pulse width modulation (SPWM) for voltage sag and swell sensing along with insulated gate bipolar transistor (IGBT) switching. We analyzed three types of DVR output defects using MATLAB/Simulink and compared the results of the ANN controller with those of a conventional PI controller. The DVR output was modeled in MATLAB/Simulink for three types of defects and two degrees of voltage sag and swell. The results demonstrated that the DVR effectively mitigated voltage sags and swells in the JDW sugar mills distribution network. Furthermore, during the validation of the proposed ANN, a comparison of results with the conventional PI controller under balanced and unbalanced sags and swells showed a significant improvement. The ANN achieved a voltage restoration of up to 99.8% and a total harmonic distortion of 13.5%, a marked improvement over the PI controller, which achieved 97% voltage restoration and 19.5% total harmonic distortion, respectively.

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Section: A Mathematical Model For Voltage Injection By Dvrmentioning

confidence: 99%

Improvements in Voltage Profile of JDW Sugar Mills’ Jawar Distribution Feeder RYK Pakistan Using ANN Based Dynamic Voltage Restorer

Siddique,

Mujahid,

Aslam

et al. 2024

JESA

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“…Whale optimization algorithm is implemented to design PI controller parameters of photovoltaic power systems under different operating conditions (8) . Fuzzy and ANN based controllers are proposed in literature to improve the performance of grid connected system (9)(10)(11) . PI, fuzzy based FACTS devices for grid connected system is designed for control of real and reactive power flow in grid connected system (12) .…”

Section: Introductionmentioning

confidence: 99%

Enhancement of power quality in grid connected PV system

Rani¹

2020

IJST

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Background/Objectives: In grid connected photo voltaic systems inverter is the key element. The inverter is required to shape dc current into sinusoidal current and provide fast response under various disturbances. The quality of power injected into the grid depends on proper inverter control. The objective of this paper is reducing harmonics and to improve power factor in grid connected system with balanced and unbalanced loads. Methods/Statistical analysis: In this study, three control mechanisms, adaptive neuro fuzzy inference system (ANFIS), ANFIS with static synchronous compensator (STATCOM), ANFIS with dynamic voltage restorer (DVR) are employed to improve power quality. The performances of fuzzy and ANFIS controllers are compared in terms of total harmonic distortion and power factor. MATLAB/ Simulink is used to perform the simulation. Findings: ANFIS controller is more effective compared to fuzzy controller. ANFIS controller with DVR gives less THD and improved power factor as compared to fuzzy controller. Novelty/Applications: Fuzzy and neural network controllers do not need any mathematical modelling and give accurate control as compared to classical PI controllers. In this work, ANFIS controller alone and ANFIS controller along with custom power devices STATCOM and DVR are designed for reduction in total harmonic distortion and improved power factor. The results are tabulated, analyzed and compared with fuzzy controller. It has been shown that ANFIS controller gives better performance compared to fuzzy controller.

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Implementation Of ANFIS-Based Dynamic Voltage Restorer To Improve The Voltage Profile Of a PV-integrated Grid

Asad¹,

Abunima²,

Şeker³

2022

IJEPS

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Power quality improvement in an AC network using artificial neural network and hysteresis band current controller

Cited by 3 publications

References 20 publications

Improvements in Voltage Profile of JDW Sugar Mills’ Jawar Distribution Feeder RYK Pakistan Using ANN Based Dynamic Voltage Restorer

Improvements in Voltage Profile of JDW Sugar Mills’ Jawar Distribution Feeder RYK Pakistan Using ANN Based Dynamic Voltage Restorer

Enhancement of power quality in grid connected PV system

Implementation Of ANFIS-Based Dynamic Voltage Restorer To Improve The Voltage Profile Of a PV-integrated Grid

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