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Enhancement of power quality in three-phase GC solar photovoltaics

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Abstract

The proliferation of grid-connected photovoltaic (PV) systems has generated considerable apprehension among power system operators due to worries about electricity quality, leading to the implementation of increasingly strict standards and regulations. Inter-harmonics and DC offset have emerged as prominent power quality issues in grid-connected PV systems, constituting significant obstacles. This article provides a thorough examination of the methods used to improve the performance of a three-phase grid-connected PV system, with a specific focus on mitigating inter-harmonics and DC offset. The presence of inter-harmonics and DC offset may have a substantial negative impact on the overall performance of a system, resulting in compromised power quality and diminished energy extraction capabilities. In order to address these challenges, a method known as ensembled deep reinforcement learning (EDRL)-based maximum power point tracking (MPPT) is used to optimize the extraction of electricity from the PV array. Furthermore, the integration of a coati optimization algorithm (COA) with a fuzzified phase-locked loop (PLL) synchronization mechanism is used to ensure precise synchronization with the grid. The EDRL-MPPT approach demonstrates a proficient ability to accurately monitor and follow the maximum power point of the PV array. This is achieved by using a reward system that is based on the lowest overall harmonic distortion in the grid current. The COA (coati optimization algorithm) is used to effectively tune the hyperparameters of the fuzzy system. The primary objective of this optimization process is to reduce the DC offset, hence ensuring a steady and precise synchronization between the fuzzy system and the grid. The efficacy of the proposed system is assessed by means of comprehensive simulations and experimental validation. The findings of this study provide evidence supporting the efficacy of the EDRL-MPPT approach in optimizing power extraction and reducing the impact of inter-harmonics. The COA fuzzified PLL synchronization system is designed to provide precise grid synchronization while mitigating the adverse effects of a 2.89% total harmonic distortion (THD) in grid current, particularly the influence of DC offset. The integration of many approaches presents notable improvements in terms of power quality, energy extraction efficiency, and system stability.

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Abbreviations

MPPT:

Maximum power point tracking

EDRL:

Ensembled deep reinforcement learning

COA:

Coati optimization algorithm

PV:

Photovoltaic

PLL:

Phase-locked loop

DC:

Direct current

SRF:

Synchronous reference frame

SOGI:

Second-order generalized integrator

ROGI:

Reduced-order generalized integrator

TOGI:

Third-order generalized integrator

P&O:

Perturb and observe

THD:

Total harmonic distortion

PWM:

Pulse width modulation

DQN:

Distinct neural network

DDPG:

Deep deterministic policy gradient

rlTD3:

Twin-delayed deep deterministic policy gradient

PPO:

Proximal policy optimization

LCL:

Inductor–capacitor–inductor filter

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Authors and Affiliations

  1. Department of Electrical Engineering, Delhi Technological University, New Delhi, India

    Sukhbir Singh & J. N. Rai

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Both authors were working in collaboration on the entire research work. All decisions and actions were mutually agreed to and done together.

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Correspondence to Sukhbir Singh.

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Singh, S., Rai, J.N. Enhancement of power quality in three-phase GC solar photovoltaics. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02304-z

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