• Zhejiang Zhilu Transmission and Distribution Equipment Co., Ltd

Research on Power Conversion Control Strategies of Photovoltaic Grid - Connection Cabinets

Abstract

1. Introduction

2. Key Control Objectives of PV Grid - Connection Cabinets

2.1 Maximum Power Point Tracking (MPPT)

• Objective: MPPT aims to optimize the power output of PV panels by continuously adjusting the operating voltage to match the maximum power point (MPP) under varying environmental conditions (e.g., irradiance, temperature). In PV grid - connection cabinets, MPPT control is typically integrated into the inverter to maximize the overall system efficiency.

• Challenges: Fluctuations in solar irradiance, partial shading, and temperature variations can cause the MPP to shift. Traditional MPPT algorithms (e.g., Perturb and Observe, Incremental Conductance) may face issues such as slow response speed or oscillation around the MPP, reducing power conversion efficiency.

2.2 Grid - Synchronization

• Objective: Grid - synchronization ensures that the AC output of the PV system matches the grid's voltage magnitude, frequency, and phase before connection. Precise synchronization minimizes inrush currents and voltage transients, preventing damage to grid - connection equipment and maintaining grid stability.

• Challenges: Grid voltage fluctuations, frequency variations, and phase - angle differences require real - time monitoring and rapid adjustment of the PV system's output. Inverters in grid - connection cabinets must incorporate advanced synchronization algorithms to adapt to dynamic grid conditions.

2.3 Power Quality Regulation

• Objective: Power quality regulation focuses on maintaining a high - quality AC output with low total harmonic distortion (THD), stable power factor, and balanced three - phase currents. This is crucial for preventing interference with other grid - connected devices and complying with grid - code standards (e.g., IEEE 1547, IEC 61727).

• Challenges: Non - linear loads in the grid, switching losses in inverters, and unbalanced PV array configurations can degrade power quality. Effective control strategies are needed to mitigate harmonic generation and optimize power factor correction.

3. Advanced Power Conversion Control Strategies

3.1 MPPT Control Strategies

• Adaptive MPPT Algorithms: Adaptive algorithms, such as the Model Predictive Control (MPC) - based MPPT, utilize real - time PV system models and future irradiance predictions to track the MPP more accurately. MPC can consider multiple constraints (e.g., voltage limits, power grid requirements) and optimize control actions over a prediction horizon, reducing power losses and improving response speed.

• Artificial Intelligence - Enabled MPPT: Machine learning techniques, including neural networks and fuzzy logic, have been applied to MPPT control. Neural networks can learn complex relationships between environmental factors and the MPP, while fuzzy logic can handle imprecise data and provide robust control under varying conditions. These AI - based methods enhance MPPT accuracy, especially in partial - shading scenarios.

3.2 Grid - Synchronization Control Strategies

• Phase - Locked Loop (PLL) Optimization: Advanced PLL algorithms, such as the Software - Based PLL (SPLL) or the Synchronous Reference Frame PLL (SRF - PLL), improve grid - synchronization performance by accurately estimating the grid voltage phase and frequency. SRF - PLL, for example, can filter out harmonic components and provide fast dynamic response, enabling seamless grid connection even under unbalanced or distorted grid conditions.

• Virtual Synchronous Generator (VSG) Control: VSG control mimics the characteristics of conventional synchronous generators, providing inertia and damping to the PV system. This helps improve grid stability during transient events (e.g., grid faults) and enhances the PV system's ability to participate in grid frequency regulation, making it more grid - friendly.

3.3 Power Quality Regulation Strategies

• Active and Reactive Power Control: By independently controlling the active and reactive power output of the inverter, PV grid - connection cabinets can regulate power factor and compensate for reactive power demands in the grid. Vector control techniques, such as the Direct Quadrature (DQ) - axis control, enable precise adjustment of active and reactive power components, improving power quality.

• Harmonic Compensation: Advanced control strategies, including the use of Active Power Filters (APFs) integrated into the grid - connection cabinet, can actively suppress harmonic currents generated by the PV system. APFs inject counter - harmonic currents to cancel out the harmonics in the output, reducing THD and ensuring compliance with grid - code requirements.

4. Simulation and Experimental Verification

• Simulation Studies: MATLAB/Simulink or PSCAD/EMTDC platforms are commonly used to simulate PV grid - connection systems with different control strategies. By modeling PV panels, inverters, and grid - connection cabinets, researchers can evaluate the performance of control algorithms under various scenarios, such as sudden irradiance changes, partial shading, or grid voltage sags.

• Experimental Validation: Laboratory - scale or field - scale experiments are conducted to verify the effectiveness of control strategies. Key performance indicators, including power conversion efficiency, grid - synchronization accuracy, and power quality metrics, are measured using instruments such as power analyzers, oscilloscopes, and frequency meters. The experimental results help validate the simulation findings and identify areas for further improvement.

5. Case Studies and Practical Applications

• Large - Scale PV Power Plants: In large - scale PV projects, advanced control strategies are essential for optimizing power generation and grid integration. For example, a 100MW PV power plant may adopt MPC - based MPPT and VSG control to enhance power output and grid - stability, reducing the impact on the regional power grid.

• Distributed PV Systems: Distributed PV systems, such as rooftop installations, require control strategies that can adapt to limited space and variable load conditions. AI - enabled MPPT and power quality regulation algorithms can improve the self - sufficiency and grid - compatibility of these systems, making them more suitable for residential and commercial applications.

6. Conclusion