Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

Zainuri, Muhammad Ammirrul Atiqi Mohd; Radzi, Mohd Amran Mohd; Soh, Azura Che; Mariun, Norman; Rahim, Nasrudin Abd; Hajighorbani, Shahrooz

doi:10.3390/en9060397

Cited by 19 publications

(22 citation statements)

References 51 publications

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“…From the modified ANN as shown in Figure 5, further changes have been implemented and even the control structure has been simplified. The algorithm is named as fundamental active current (FAC) ANN [22]. As compared to the modified ANN algorithm, the FAC ANN algorithm is reported to provide better performance in both steady-state and dynamic environments.…”

Section: Other Algorithmsmentioning

confidence: 99%

“…The extraction or detection of harmonic currents from the harmonic-polluted power line to generate the reference current signal by using a harmonic extraction algorithm is deemed the first and the most important operation stage of a SAPF's control system [21,22]. Being the very first algorithm to operate in the controller, accurate and fast harmonic currents extraction result in proper and fast reference current generation which further controls the SAPF to efficiently reproduce the reference current signal as the desired injection current for harmonics mitigation.…”

Section: Harmonic Extraction Algorithmmentioning

confidence: 99%

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Control Algorithms of Shunt Active Power Filter for Harmonics Mitigation: A Review

et al. 2017

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Current harmonics is one of the most significant power quality issues which has attracted tremendous research interest. Shunt active power filter (SAPF) is the best solution to minimize harmonic contamination, but its effectiveness is strictly dependent on how quickly and accurately its control algorithms can perform. This manuscript reviews various types of existing control algorithms which have been employed for controlling operation of SAPF. Harmonic extraction, DC-link capacitor voltage regulation, current control and synchronizer algorithms are examined and discussed. The most relevant techniques which have been applied for each control algorithm are described and contrasted in an organized manner to identify their respective strengths and weaknesses. It is found that the applied control algorithms differ in two conditions: (1) the condition where harmonic current distortion is treated by the SAPF in the presence of non-ideal source voltage; and (2) the condition where multilevel inverter is employed as the circuit topology of SAPF.

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Section: Other Algorithmsmentioning

confidence: 99%

Section: Harmonic Extraction Algorithmmentioning

confidence: 99%

Control Algorithms of Shunt Active Power Filter for Harmonics Mitigation: A Review

et al. 2017

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“…The proposed STF-based ADALINE algorithm is developed by referring to the recent unified ADALINE algorithm, as successfully implemented in [30,31]. Hence, for effective presentation on the working principle of the proposed STF-based ADALINE algorithm, and also for showing proper comparison, significant features of the unified ADALINE algorithm are first described.…”

Section: Self-tuning Filter (Stf)-based Adaptive Linear Neuron (Adalimentioning

confidence: 99%

“…The design concept and effectiveness of the proposed STF-based ADALINE algorithm are verified using MATLAB-Simulink (R2012a, MathWorks, Natick, MA, USA). A comparative study with the recent ADALINE approach (unified ADALINE algorithm [30,31]) is also performed to investigate performance improvement achieved by SAPF while using the proposed algorithm. Various source voltage conditions (balanced, unbalanced and distorted) are created to test performance of each algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Further improvement has been performed on this particular algorithm to reduce its complexity: by replacing the conventional synchronizer algorithms (PLL and ZCD) with an ADALINE-based fundamental voltage extraction algorithm. The proposed algorithm is known as unified ADALINE-based fundamental component extraction algorithm [30,31]. The algorithm is designed for dual functionality.…”

Section: Introductionmentioning

confidence: 99%

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A Self-Tuning Filter-Based Adaptive Linear Neuron Approach for Operation of Three-Level Inverter-Based Shunt Active Power Filters under Non-Ideal Source Voltage Conditions

et al. 2017

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This paper presents a self-tuning filter (STF)-based adaptive linear neuron (ADALINE) reference current generation algorithm to enhance the operation of a three-phase three-level neutral-point diode clamped (NPC) inverter-based shunt active power filter (SAPF) under non-ideal (unbalanced and/or distorted) source voltage conditions. SAPF is an effective and versatile mitigation tool for current harmonics. As for its controller, ADALINE-based reference current generation algorithmd have widely been applied and proven to work effectively under balanced and purely sinusoidal source voltage conditions. However, no work has been conducted to study its performance under non-ideal source voltage conditions. In this work, a STF-based fundamental voltage extraction algorithm is integrated with an ADALINE algorithm, serving as synchronizer algorithm to ensure in-phase operation of the generated reference current with the non-ideal source voltage. Hence, it completely eliminates any dependency on conventional synchronizer algorithms such as phase-locked loop (PLL) and zero-crossing detector (ZCD). Additionally, the proposed STF-based ADALINE algorithm implements the modified Widrow-Hoff (W-H) weight updating algorithm for fast generation of reference current. Both simulation and experimental works are performed to verify design concept and effectiveness of the proposed algorithm. Comparative study with another recently reported algorithm is performed to investigate the performance improvement achieved by SAPF while using the proposed algorithm.

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Advanced nonlinear controller of single‐phase shunt active power filter interfacing solar photovoltaic source and electrical power grid

Hekss

Abouloifa

Lachkar

et al. 2021

Int Trans Electr Energ Syst

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This paper addresses the nonlinear controller design and the stability analysis of a shunt active power filter (SAPF) interfacing a solar photovoltaic (PV) system that consists of a couple of PV panels, a single-phase two-stage IGBT half-bridge SAPF and two identical DC link capacitors. We seek for the considered system the fulfillment of three control objectives: (a) extracting the maximum PV power using an appropriate maximum power point tracking (MPPT) algorithm; (b) regulation of the DC link capacitor voltages to the reference provided by the MPPT controller hence ensuring the exchange of power between the PV source and the AC power grid; (c) compensation for reactive power and undesired harmonics caused by the nonlinearity of electronic power loads, that is, performing power factor correction (PFC). The control objectives are achieved using a new two-loop cascaded controller. The Backstepping approach is applied in the inner loop to perform PFC. A filtered proportional-integral (PI) controller is applied in the outer loop to ensure tight regulation of the PV voltage. The MPPT control is implemented using a perturb-and-observe (PO) algorithm. The performance of the closed-loop system under the two-loop control strategy is formally analyzed through the Lyapunov approach applied to the averaged model. Numerical simulations are performed in MATLAB/SimPowerSystems environment to validate the design methodology and to confirm the theoretically predicted performances of the system under the proposed nonlinear controller.average analysis, Lyapunov stability analysis, nonlinear control, power factor correction, shunt active power filter, solar photovoltaic system List of Symbols and Abbreviations: L g , grid inductor; r g , grid resistor; R, load resistor; L, load inductor; C pv , filter capacitor; r f , filter resistor; L f , filter inductor; N s ,N p , number of connected modules in series and in parallel respectively; G, irradiance; T, temperature; μ, binary switching signal; i g , grid current; v go , grid voltage; i f , filtering current; i L , load current; i pv1 , i pv2 , PV output currents; v pv1 , v pv2 , PV output voltages; u, continuous control input; β, external control signal; P pv , PV panels power; v f , inverter output voltage; v g , voltage at point of common coupling; I f , peak value of output filter current; V f , peak value of inverter output voltage; θ 1 , phase of the voltage at point of common coupling; θ 2 , phase of the output filter current; χ, square of the PV voltage; u Ã , Steady state value of u; V , Lyapunov function candidate; f PWM , switching frequency; Q g , reactive power; T g , grid period; P g , active power; c 1 ,c 2 , c 3 , c 4 , controller parameters; ω g , angular frequency; IC, incremental conductance; IGBT, insulated-gate bipolar transistor; MPP, maximum power point; MPPT, maximum power point tracking; PCC, point of common coupling; PFC, power factor correction; PI, proportional-integral; PO, perturb and observe; PV, photovoltaic; PWM, pulse width modulation principle; SAP...

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Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

Cited by 19 publications

References 51 publications

Control Algorithms of Shunt Active Power Filter for Harmonics Mitigation: A Review

Control Algorithms of Shunt Active Power Filter for Harmonics Mitigation: A Review

A Self-Tuning Filter-Based Adaptive Linear Neuron Approach for Operation of Three-Level Inverter-Based Shunt Active Power Filters under Non-Ideal Source Voltage Conditions

Advanced nonlinear controller of single‐phase shunt active power filter interfacing solar photovoltaic source and electrical power grid

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