The output stability of distributed photovoltaic (PV) systems is easily affected by interference factors such as changes in light intensity and sudden temperature fluctuations, leading to a decline in power quality. As a core device for harmonic suppression and power optimization, AHF (Active Harmonic Filter) directly determines the grid-connection reliability of PV systems. This article focuses on the application of AHF in distributed photovoltaic power supply, detailing the innovative strategy integrating RBF neural network and Active Disturbance Rejection Control (ADRC), and demonstrating how AHF breaks through the limitations of traditional control to achieve efficient anti-interference and low-harmonic output, providing technical support for the stable operation of PV systems.
I. AHF: The "Power Quality Guardian" of Distributed Photovoltaic Systems
Distributed PV systems have the problems of large output fluctuations and high harmonic distortion rate due to environmental interference. With flexible control and precise compensation characteristics, AHF has become the key to solving this pain point:
1. Core Role of AHF: Harmonic Suppression and Voltage Stabilization
AHF dynamically injects reverse harmonic current to offset harmonic pollution generated by PV systems by real-time detecting the grid current waveform, while stabilizing the DC bus voltage. In PV systems, AHF is deployed in the transmission link, which can specifically solve problems such as current distortion and voltage jump caused by sudden changes in light/temperature, ensuring that the grid-connected power meets national standards. Data from a PV project shows that without AHF, the current harmonic distortion rate reaches 8.3%; after enabling AHF, the distortion rate drops to below 1.4%.
2. Shortcomings of Traditional Control: Limited Performance of AHF
Traditional AHF mostly adopts PI control or sliding mode control. Still, in the face of complex disturbances of PV systems, there are obvious deficiencies: PI control has weak anti-interference ability, and the voltage transient value is likely to exceed 7V; sliding mode control has poor transient response and insufficient harmonic suppression accuracy. These defects prevent AHF from giving full play to its effectiveness, restricting the improvement of the power quality of PV systems.
3. Necessity of Innovative Control: Unleash Full Performance of AHF
To adapt to the dynamic operating conditions of PV systems, it is necessary to design a more intelligent control strategy for AHF. The scheme integrating RBF neural network and ADRC allows AHF to optimize parameters in real time and accurately offset disturbances, which not only improves anti-interference ability but also ensures power quality under transient and steady states, making AHF a "reliable backing" for PV systems.
II. AHF Innovative Control Strategy: Design Logic of RBF Active Disturbance Rejection Closed-Loop
The RBF active disturbance rejection closed-loop control strategy of AHF constructs a full-link precise control system through mathematical modeling, Park transformation, core design of ADRC, and RBF parameter optimization:
1. AHF Mathematical Modeling: Lay the Foundation for Control
The three-phase structure of AHF includes filter inductance, resistance, and switching tubes. Its mathematical model needs to reflect the correlation between voltage, current, and switching states. By analyzing the conduction logic of switching tubes, the three-phase dynamic equation of AHF is established, clarifying the relationship between grid-connected voltage, current, and DC bus voltage, providing theoretical support for the subsequent design of control strategies. At the same time, Park transformation is used to convert three-phase AC quantities into two-phase DC quantities, simplifying the control model and making it easier for AHF to achieve precise regulation.
2. Core of Active Disturbance Rejection Control: Improve AHF Anti-Interference Ability
Active Disturbance Rejection Control (ADRC) achieves stable control of the system by real-time estimating and compensating for system disturbances through Linear Extended State Observer (LESO) and Linear State Error Feedback (LSEF), which greatly enhances the anti-interference performance of AHF. In response to light and temperature disturbances of PV systems, the ADRC module of AHF can respond quickly, controlling the DC bus voltage transient value within 3V, which is far better than the traditional control of more than 5V. Its core logic is: through the coordinated adjustment of observation bandwidth and control bandwidth, AHF can offset the impact of disturbances in real time and maintain stable output.
3. RBF Neural Network Optimization: Unlock AHF Transient Performance
To solve the limitation of fixed parameters in ADRC, the RBF neural network is introduced to adaptively optimize the observation bandwidth and control bandwidth of AHF. Through the cooperative operation of the input layer, the hidden layer, and the output layer, the RBF neural network modifies the control parameters of AHF in real time: after 60 iterations, the parameter adjustment error can be reduced to within 2%, ensuring that AHF still maintains optimal performance during transient transitions. This dynamic optimization enables AHF to cope with steady-state harmonics and resist sudden disturbances, fully adapting to the complex operating conditions of PV systems.
4. Closed-Loop Control Process: Full-Link Working Logic of AHF
The RBF active disturbance rejection closed-loop control process of AHF is clear and efficient: first, collect the voltage and current signals of the PV system, obtain the DC model through AHF three-phase modeling and Park transformation; then use LESO to observe disturbances and LSEF to generate control signals; finally, optimize the bandwidth parameters through RBF neural network and output precise control commands to drive AHF operation. The entire process realizes a closed loop of "detection-modelling-disturbanc compensation-parameter optimization", synchronously improving the control accuracy and anti-interference ability of AHF.
III. Experimental Verification: Performance Advantages of AHF Innovative Strategy
To verify the effectiveness of the strategy, an AHF model of a PV system was built on the Matlab/Simulink platform, and comparative tests were conducted with traditional PI control and sliding mode control. The core results are as follows:
1. Anti-Interference: Significant Reduction of Voltage Transient Value
When light intensity and temperature change suddenly, the DC bus voltage transient value of AHF under PI control exceeds 7V, 5V under sliding mode control, while only 2.3V under RBF active disturbance rejection control, which is far below the safety threshold of 3V, effectively avoiding the impact of voltage fluctuations on the PV system.
2. Harmonic Suppression: Distortion Rate Reduced to Industry-Leading Level
In the test of grid-connected current harmonic distortion rate, the distortion rate under traditional control strategies is at least 3.6%, while after AHF adopts the innovative strategy, the distortion rate decreases from the initial 3.61% to 1.4% gradually, fully meeting the strict requirements of PV grid connection and significantly exceeding the industry average level.
3. Stability: Reliable Long-Term Operation Performance
The 72-hour continuous test shows that under the innovative strategy, AHF has a stable output current waveform, and the DC bus voltage fluctuation is controlled within ±2V without fault shutdown, showing strong operational stability and robustness.
IV. Application Scenarios and Practical Value of AHF Innovative Strategy
1. Distributed PV Power Plants: Ensure Grid-Connection Reliability
In large-scale distributed PV power plants, the innovative control strategy of AHF can cope with large-scale light changes, ensure the power quality when multiple PV inverters operate in parallel, reduce the impact of grid-connected harmonics on the power grid, and improve the acceptance capacity of the power plant.
2. Industrial and Commercial Rooftop PV: Adapt to Complex Load Environments
Industrial and commercial rooftop PV often coexists with nonlinear loads, resulting in more serious harmonic pollution. Through precise harmonic suppression and voltage stabilization, AHF can avoid the superposition of PV output and load harmonics, ensure the safe operation of plant equipment, optimize the power factor, and reduce electricity fines.
3. PV Power Supply in Remote Areas: Resist Extreme Environmental Interference
PV systems in remote areas are vulnerable to the impact of extreme weather such as heavy rain and sandstorms. The strong anti-interference ability of AHF can ensure the continuity of power supply, provide stable power for key scenarios such as communication base stations and rural power grids, and expand the application boundary of PV.
V. Conclusion: AHF Control Innovation Leads the Upgrade of PV Power Quality
As a core optimization device for distributed PV systems, the innovation of AHF (Active Harmonic Filter) control strategy directly promotes the leap of PV power quality. The scheme integrating RBF neural network and ADRC enables AHF to break through traditional limitations, achieving the triple advantages of "high anti-interference, low harmonics, and stable voltage", and providing a reliable guarantee for the large-scale grid connection of distributed PV.
In the future, with the integration of wide-bandgap semiconductor technology and AI algorithms, AHF will further achieve miniaturization and intelligent upgrading, adapting to more diverse PV application scenarios. If your PV project faces problems such as output fluctuations and excessive harmonics, please feel free to inform us of the system capacity, grid-connected voltage level, and core pain points. HengRong Electric CO., LTD. will customize an AHF solution based on the innovative control strategy to help your PV system operate efficiently, stably, and cleanly!