Multiobjective Dynamic Voltage Restorer With Modified EPLL Control and Optimized PI-Controller Gains

Naidu, Talada Appala; Arya, Sabha Raj; Maurya, Rakesh

doi:10.1109/tpel.2018.2837009

Cited by 57 publications

(22 citation statements)

References 28 publications

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“…PSO was used in [8] to control the operation of switched capacitors for voltage balancing. Furthermore, PSO was used to tune proportional–integral (PI) controllers in different power applications such as dynamic voltage restorer control [9] and PWM controller for inverters to improve the voltage unbalance factor (VUF) [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Alkayyali

Ghaeb

2020

IET Generation, Transmission & Distribution

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Alkayyali

Ghaeb

2020

IET Generation, Transmission & Distribution

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“…Pathak et al [16] developed a renewable wind-hydro system to demonstrate a least-mean-square with reweighted control approach for solutions in power quality. Naidu et al [17][18] discussed a modified enhanced phase-locked loop and sliding mode observer control algorithm for a dynamic voltage restorer with optimization of proportionalintegral (PI) gains using particle swarm optimization (PSO) and Harris Hawks optimization algorithms for problems in power quality. The ant colony algorithm was explored for studying ant behavior for optimizing and solving the traveling salesman problem in Ref.…”

Section: India Ranks Fourth On the Renewable Energymentioning

confidence: 99%

Ant lion algorithm for optimized controller gains for power quality enrichment of off-grid wind power harnessing units

Kodakkal¹,

Rajagopal

Raju

et al. 2020

Chin. J. Electr. Eng.

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The proposed system uses an algorithm that works on the admittance of the system, for estimating the reference values of generated currents for an off-grid wind power harnessing unit (WPHU). The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds. The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU. The wind power harnessing unit comprising of a squirrel cage induction generator, a star-delta transformer, a battery storage system and the control unit are modeled using Matlab/Simulink R2019. An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line. The response of the system during the dynamic loading depends on the best possible compensator proportional-integral (PI) gains. The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence, high efficiency and fast calculating speed. It is therefore used to extract the optimal values of frequency and voltage PI gains. The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up. This algorithm is proven to have a quick response, maintain the required frequency, suppress the current harmonics, regulate voltage, help in balancing the load and compensating for the neutral current.

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“…As already explained in (11)- (18), fundamental reactive load current weight can also be computed in the same manner. The reactive component w qa , weight of FCI for 'a' phase is expressed as…”

Section: Fundamental Reactive Load Current Weightmentioning

confidence: 99%

“…There are numerous control techniques described in text like instantaneous reactive power theory (IRPT) [12], least mean square (LMS) [13], synchronous reference frame (SRF) [14], least mean fourth (LMF) [15], fixed forward prediction (FFP) [16], hyperbolic tangent function (HTF) [17], and enhanced phase-locked loop (EPLL) [18]. SRF has complicated computations with poor dynamic response.…”

Section: Introductionmentioning

confidence: 99%

Convex combination of least mean square for grid‐tied PV system

Jain

Hussain

Kandpal

et al. 2019

IET Renewable Power Generation

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Here, an implementation of three-phase grid-tied single-stage solar photovoltaic (PV) system is done at adverse grid situations of voltage unbalances. The proposed convex combination of least mean square (CCLMS) control approach is adaptive in nature, which extracts the fundamental component of load current (FCI) with fast convergence and provides good performance than conventional controls. The CCLMS algorithm overcomes the compromise of LMS filters between precision and tracking capability due to fixed stepping by providing a combination of two filters for precision and fast tracking. The perturb and observe (P&O)-based maximum power point tracking (MPPT) is employed for peak power extraction from a PV array. The system is tested in the laboratory on a developed prototype at several adverse conditions like voltages unbalance, load currents unbalances, and irradiation changes. The system performance is acceptable and harmonics distortion is in the limits according to the IEEE-519 standard while supplying energy to the utility and to the loads. Nomenclature i sa , i sb , i sc grid currents v sab , v sbc PCI line voltages i La , i Lb , i Lc three phase load currents i vsc PV-VSC currents i sa *, i sb *, i sc * grid reference currents V dc * , V dc reference and sensed DC link voltages P pv , V pv , I pv PV array-power, voltage and current u pa , u pb , u pc unit templates (in-phase) w pa , w pb , w pc fundamental active load current weight component w qa , w qb , w qc fundamental reactive load current weight component w sp net active fundamental component weight w sq net reactive fundamental component weight w lpa average weight of fundamental active load components R f , C f ripple filter components (resistance and capacitance) u qa , u qb , u qc unit templates (quadrature) CCLMS convex combination of least mean square

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Multiobjective Dynamic Voltage Restorer With Modified EPLL Control and Optimized PI-Controller Gains

Cited by 57 publications

References 28 publications

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Hybrid PSO–ANN algorithm to control TCR for voltage balancing

Ant lion algorithm for optimized controller gains for power quality enrichment of off-grid wind power harnessing units

Convex combination of least mean square for grid‐tied PV system

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