Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system

Nishant Kumar, Bhim Singh, Bijaya Ketan Panigrahi, L. Xu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard.

LanguageEnglish
Article number8605505
Pages9003-9012
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number11
Early online date8 Jan 2019
DOIs
Publication statusPublished - 1 Nov 2019

Fingerprint

Incident solar radiation
Harmonic distortion
Electric potential
Power quality
Solar energy
Resource allocation
Dynamic response
Capacitors
Topology
Irradiation
Maximum power point trackers
Power management

Keywords

  • solar PV array
  • power quality
  • grid integrated system
  • InC
  • MPPT

Cite this

@article{4c5d72c0a0e14cbd8ac5f54ea47267fb,
title = "Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system",
abstract = "This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard.",
keywords = "solar PV array, power quality, grid integrated system, InC, MPPT",
author = "Nishant Kumar and Bhim Singh and Panigrahi, {Bijaya Ketan} and L. Xu",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2019",
month = "11",
day = "1",
doi = "10.1109/TIE.2018.2890497",
language = "English",
volume = "66",
pages = "9003--9012",
journal = "IEEE Transactions on Industrial Electronics",
issn = "0278-0046",
number = "11",

}

Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system. / Kumar, Nishant ; Singh, Bhim ; Panigrahi, Bijaya Ketan ; Xu, L.

In: IEEE Transactions on Industrial Electronics, Vol. 66, No. 11, 8605505, 01.11.2019, p. 9003-9012.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system

AU - Kumar, Nishant

AU - Singh, Bhim

AU - Panigrahi, Bijaya Ketan

AU - Xu, L.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard.

AB - This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard.

KW - solar PV array

KW - power quality

KW - grid integrated system

KW - InC

KW - MPPT

UR - https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=41

U2 - 10.1109/TIE.2018.2890497

DO - 10.1109/TIE.2018.2890497

M3 - Article

VL - 66

SP - 9003

EP - 9012

JO - IEEE Transactions on Industrial Electronics

T2 - IEEE Transactions on Industrial Electronics

JF - IEEE Transactions on Industrial Electronics

SN - 0278-0046

IS - 11

M1 - 8605505

ER -