Dual heuristic dynamic programing control of grid-connected synchronverters

Saadatmand, Sepehr; Nia, Mohamad Saleh Sanjari; Shamsi, Pourya; Ferdowsi, Mehdi

doi:10.1109/naps46351.2019.9000382

Cited by 27 publications

(7 citation statements)

References 8 publications

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“…An NN-based adaptive critic design is utilized in a typical PI-based synchronverter controller to optimize its cost function. The characteristic of the NN improves the performance under uncertainties and unknown parameters such as varying irradiance and temperature [22].…”

Section: Reward (R)mentioning

confidence: 99%

Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter

2020

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In recent years, the domination of power electronics-interfaced renewable energy source (RES) such as solar photovoltaic (PV) system causes grid frequency instability issue. This paper proposes a new machine learning (ML)-based virtual inertia (VI) synthetization in synchronverter topology to integrate the solar PV system and the power grid with high-frequency stability. The proposed ML-based VI is synthetized by amalgamating the action and critic network to decouple active and reactive power control. Therefore, the proposed synchronverter exhibits decoupled control and flexible moment of inertia (J) changes that lead to high stability and fast transient response as compared to the conventional proportional-integral (PI) and fuzzy logic (FL)-based synchronverters. Various case studies in MATLAB/ Simulink simulation have been carried out, and the results proved the feasibility and effectiveness of the proposed ML-based synchronverter. Through the proposed control strategy, the maximum frequency deviation from the nominal value, settling time to reach quasi-steady-state frequency and steady-state error has been reduced by 0.1Hz, 35% and 27% respectively. INDEX TERMS Synchronverter, virtual inertia, power quality, frequency stability, grid-connected solar photovoltaic system.

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Section: Reward (R)mentioning

confidence: 99%

Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter

2020

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“…Based on this issue, the algorithm can explore all possible areas in the set. After these three steps, the new generations are generated, and the algorithm continues iteratively to find a feasible solution [41], [59]- [69].…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

Sirat

Mehdipourpicha

Zendehdel

et al. 2020

2020 IEEE Power and Energy Conference at Illinois (PECI)

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Loss minimization in distribution networks (DN) is of great significance since the trend to the distributed generation (DG) requires the most efficient operating scenario possible for economic viability variations. Moreover, voltage instability in DNs is a critical phenomenon and can lead to a major blackout in the system. The decreasing voltage stability level restricts the increase of load served by distribution companies. DG can be used to improve DN capabilities and brings new opportunities to traditional DNs. However, installation of DG in non-optimal places can result in an increase in system losses, voltage problems, etc. In this paper, genetic algorithm (GA), harmony search algorithm (HSA) and improved HSA have been applied to determine the optimal location of DGs. Simulation results for an IEEE 33 bus network are compared for different algorithms, and the best algorithm is stated for minimum losses.

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“…This emulated T is the synthetic inertia in the synchronverter. Research pertaining to synchronverter controllers has been performed in several areas, such as power frequency (p-f ) control, direct-current (DC) link voltage controllers [25] , self-synchronous controllers, damping correction loops [26] , neural networks [27] , and model predictive control [28] . However, research pertaining to digitalized fuzzy logic for synchronverter smart controllers is limited.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic controller-based synchronverter in grid-connected solar power system with adaptive damping factor

Yap

Beh

Sarimuthu

2021

Chin. J. Electr. Eng.

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In recent years, renewable energy sources, specifically solar power systems, have developed rapidly owing to their technological maturity and cost effectiveness. However, its grid integration deteriorates frequency stability because of insufficient rotating masses and inertial response. Hence, a synchronverter, which is an inverter that mimics the operation of a synchronous generator, is crucial to interface solar power in a power grid. It stabilizes the power grid by emulating a virtual inertia. However, a conventional proportional-integral (PI)-based synchronverter is not equipped with an adaptive damping factor (D p ) or a digitalized smart controller to manage fast-responding solar inputs. Hence, a novel fuzzy logic controller (FLC) framework is proposed such that the synchronverter can operate in a grid-connected solar power system. In this study, D p is controlled in real time using an FLC to achieve balance between speed and stability for frequency error correction based on frequency difference. Results of four case studies performed in Matlab/Simulink show that the proposed FLC-based synchronverter can stabilize the grid frequency by reducing the frequency deviation by at least 0.2 Hz (0.4%), as compared with the conventional PI-based synchronverter.

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Dual heuristic dynamic programing control of grid-connected synchronverters

Cited by 27 publications

References 8 publications

Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter

Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter

Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

Fuzzy logic controller-based synchronverter in grid-connected solar power system with adaptive damping factor

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