Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System

Chankaya, Mukul; Hussain, Ikhlaq; Ahmad, Aijaz; Khan, Irfan; Muyeen, S. M.

doi:10.3390/en14051365

Cited by 10 publications

(6 citation statements)

References 47 publications

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“…Much artificial intelligence (AI), i.e., fuzzy logic and neural network-based controls, have also been utilized for proportional-integral (PI) controller gain tuning, which increases the system's complexity [18,19]. Many metaheuristic optimization techniques (MOTs) have been implemented in an offline mode to deliver optimal gains of the PI controller [14,[20][21][22][23]. The fractional-order PI controller offers enhanced performance than the PI controller due to its non-integer integral gain.…”

Section: Introductionmentioning

confidence: 99%

“…The fractional-order PI controller offers enhanced performance than the PI controller due to its non-integer integral gain. The gains of fractional-order PI (FOPI) can be obtained by using MOTs, i.e., salp swarm optimization (SSO), chaotic grey wolf optimization (CGWO), and many more [20,23]. The increased tuning parameter as non-integer integral gain increases the system's robustness to deliver optimal control, better system response, and power quality [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…The HESS consists of static storage devices (i.e., battery, ultracapacitor (UC), fuel cells), which have a shorter response time to any system dynamics than the compressed air storage system (flywheel storage, etc.) [14,20]. Energy storage systems with faster response times are suitable for handling sudden, sustained, and prolonged disturbances, i.e., fault, load variations, and voltage sag/swell.…”

Section: Introductionmentioning

confidence: 99%

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Stability Analysis of Chaotic Grey-Wolf Optimized Grid-Tied PV-Hybrid Storage System during Dynamic Conditions

et al. 2022

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This paper presents the stability improvement of the three-phase four-wire (3P-4W) grid-tied PV-hybrid energy storage system (HESS) using chaotic grew wolf optimization (CGWO) for DC bus voltage and AC bus voltage control. The CGWO tuned fractional order proportional–integral (FOPI) controllers reduce the and variations during diverse, dynamic conditions, i.e., sudden irradiation variations, deep voltage sag/swell, etc. The DC bus is responsible for the current injection/extraction control, maximum PV power extraction, bi-directional power flow, dc second-harmonics component elimination, and active power balance. At the point of common coupling (PCC), the AC bus is accountable for bi-directional power flow and active and reactive power control. The two-level voltage source converter (VSC) is controlled by a novel variable step-size incremental least mean square (VSS-ILMS) in zero voltage regulation (ZVR) mode. Due to its varying step size, VSC control is less prone to noise signals offers better stability, improved convergence rate, dc offset rejection, and tracking speed during dynamics, i.e., large oscillations. A battery and ultracapacitor are coupled to the DC link by buck-boost converters in the HESS. To regulate power transit between the DC bus and the grid, the HESS current control technique is designed to shift frequently from charging to discharging stage and vice versa. The novelty of the PV-HESS system lies in CGWO tuned VSS-ILMS control of VSC, which effectively and efficiently filter out the active fundamental constituents of load current and eliminate dc offset from VSC output. The HESS control maintains the DC bus voltage profile by absorbing and delivering energy (during dynamic conditions) rather than curtailing it. The presented system is simulated in a MATLAB/SIMULINK environment. The simulation results in graphical and numerical forms verify the stable and satisfactory operation of the proposed system as per IEEE519 standard.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stability Analysis of Chaotic Grey-Wolf Optimized Grid-Tied PV-Hybrid Storage System during Dynamic Conditions

et al. 2022

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“…The optimization techniques of conventional and meta-heuristic (swarm and computational intelligence) techniques have been around for a while and are continuously evolving [27]. Nature-inspired optimization techniques, e.g., genetic algorithms (GA), particle swarm optimization (PSO) [28], salp swarm optimization (SSO) [29], mantra ray foraging optimization (MRFO) [30], Jaya algorithm [31], generalized normal distributed optimization (GNDO) [32], etc., have found applications in DC link control, DC-DC converter control and optimal parameter estimation of the VSC control.…”

Section: Introductionmentioning

confidence: 99%

Generalized Normal Distribution Algorithm-Based Control of 3-Phase 4-Wire Grid-Tied PV-Hybrid Energy Storage System

et al. 2021

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The presented work employs the multiple random feature kernel mean p-power algorithm (MRFKMP) for the voltage source converter (VSC) control of a three-phase four-wire grid-tied dual-stage photovoltaic-hybrid energy storage system (HESS) to achieve multiple objectives during various induced dynamic conditions. The proposed control enables the VSC to accomplish manifold goals, i.e., reactive power compensation, power quality enhancement, load, power balancing at common coupling point and grid voltage balancing during unity power factor mode of operation. The proposed system is scrutinized under steady-state and numerous dynamic states such as irradiation variation, specified power mode, abnormal grid voltage, load, and grid voltage unbalancing. The seamless control facilitates the swift resynchronization of the grid as well as maintaining stability during islanding and re-synchronization operations while satisfying the necessary load requirements. The associated HESS consisting of battery and ultra-capacitor is competent enough in managing the interruptions occurring on the grid, load and photovoltaic side. The DC bus voltage is controlled by the PI controller, which is tuned by the generalized normal distribution algorithm and kept at the desired level during diverse operating conditions. The optimized DC bus generates an accurate loss component of current and further enhances the VSC performance. The proposed system is investigated by simulation and found acceptable as per IEEE 519 standards.

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“…The Jaya algorithm is a specific parameter less technique that is robust but less efficient. In [20,21], the DC link optimization with the salp swarm optimization (SSO) technique and generalized normal distributed algorithm (GNDO) are presented. The SSO technique delivers a poor convergence rate on higher-dimensional problems and GNDO is inspired by the Gaussian distribution and require no special controlling parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

et al. 2021

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This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.

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Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System

Cited by 10 publications

References 47 publications

Stability Analysis of Chaotic Grey-Wolf Optimized Grid-Tied PV-Hybrid Storage System during Dynamic Conditions

Stability Analysis of Chaotic Grey-Wolf Optimized Grid-Tied PV-Hybrid Storage System during Dynamic Conditions

Generalized Normal Distribution Algorithm-Based Control of 3-Phase 4-Wire Grid-Tied PV-Hybrid Energy Storage System

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

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