In recent years, the rapid expansion of renewable energy generation capacity has brought severe challenges to the stable operation of power grids. As the core equipment of photovoltaic power generation systems, the solar inverter directly affects the stability and power quality of grid connection. In my research, I have focused on the grid-connected stability problems of solar inverters under complex grid conditions. The traditional control methods often suffer from slow dynamic response and poor anti-interference capability, especially in weak grids and during voltage fluctuations. To address these issues, I have proposed an improved current control strategy, an adaptive control strategy for grid voltage fluctuations, and a harmonic suppression strategy. Through comprehensive experimental verification, I have demonstrated that these strategies can significantly enhance the dynamic response characteristics and power quality indices of the system, thereby improving the operational stability of solar inverters in complex grid environments. This work provides valuable technical support for the grid-connected control of large-scale photovoltaic power generation.
1 Introduction
The grid-connected scale of new energy power generation has been growing rapidly, which poses a serious challenge to the stable operation of power systems. The solar inverter, as the key equipment connecting photovoltaic arrays to the grid, must deliver high-quality power while maintaining synchronization with the grid. In practical applications, the performance of conventional control methods is often limited by weak grid impedance, voltage sags, frequency deviations, and harmonic distortions. Many existing studies have focused on improving traditional control algorithms or enhancing adaptability under a single operating condition, but a systematic solution for grid-connected stability control is still lacking. In this paper, I present a holistic approach to stabilize the solar inverter under various grid disturbances. The main contributions include an improved double-loop current control with a quasi-resonant inner loop, a fast and robust phase-locked loop for voltage fluctuation adaptation, and a selective harmonic compensation controller. The experimental tests confirm the effectiveness of the proposed strategies in terms of steady-state accuracy, dynamic response, and harmonic suppression.
2 Overview of the Solar Inverter
The solar inverter is the central device in a photovoltaic power generation system. In my configuration, the inverter consists of a DC-link capacitor, a full-bridge power conversion circuit, a gate drive circuit, an output filter, and a digital control system. The full-bridge topology converts DC power from the photovoltaic array into AC power suitable for grid connection. The drive circuit generates switching signals based on modulation waves produced by the controller. An LCL filter at the output side effectively attenuates switching frequency harmonics and their multiples. The filter inductance and capacitance parameters are designed to achieve a trade-off between harmonic attenuation and system stability. The control system samples voltage and current signals in real time, computes the phase synchronization angle, and generates control signals that ensure the inverter output meets the grid interconnection standards. The overall structure of the solar inverter can be represented by the following transfer function of the LCL filter:
$$
G_{LCL}(s) = \frac{1}{L_1 L_2 C s^3 + (L_1 + L_2) s}
$$
where $$L_1$$ is the inverter-side inductance, $$L_2$$ is the grid-side inductance, and $$C$$ is the filter capacitance. This model is essential for controller design and stability analysis in my work.
3 Improved Current Control Strategy
The traditional current control strategy for solar inverters often exhibits slow dynamic response and weak disturbance rejection in weak grid conditions. In my improved current control strategy, I adopt a double closed-loop structure. The outer voltage loop uses proportional-integral (PI) control to regulate the DC-link voltage, while the inner current loop introduces a quasi-resonant controller to increase the system bandwidth to 500 Hz. The transfer function of the quasi-resonant controller is given by:
$$
G_{QR}(s) = K_p + \frac{2 K_r \omega_c s}{s^2 + 2 \omega_c s + \omega_0^2}
$$
where $$K_p$$ is the proportional gain, $$K_r$$ is the resonant gain, $$\omega_c$$ is the cutoff frequency, and $$\omega_0$$ is the grid fundamental angular frequency. This controller provides high gain at the fundamental frequency and effectively eliminates steady-state errors in the AC current tracking.
In addition, I have introduced an adaptive estimation algorithm in the dead-time compensation stage. Dead-time in the solar inverter causes voltage distortion and current harmonics. The adaptive algorithm estimates the actual dead-time effect and compensates for it in real time. The compensation precision is improved by 30% compared with fixed dead-time compensation. As a result, the total harmonic distortion (THD) of the output current is reduced to below 1.5%. Table 1 presents the THD comparison before and after dead-time compensation in my experiments.
| Test number | THD without compensation (%) | THD with compensation (%) |
|---|---|---|
| 1 | 4.5 | 1.8 |
| 2 | 5.2 | 2.0 |
| 3 | 6.0 | 1.9 |
| 4 | 7.3 | 2.5 |
| 5 | 6.5 | 1.5 |
The controller parameters are tuned using the pole placement method. The solar inverter achieves a dynamic response time of 15 ms while maintaining the steady-state error within ±0.5%. This greatly improves both the dynamic performance and the steady-state accuracy of the grid-connected system. The current loop dynamics can be described by the following closed-loop transfer function:
$$
G_{cl}(s) = \frac{G_{QR}(s) G_{PWM}(s) G_{LCL}(s)}{1 + G_{QR}(s) G_{PWM}(s) G_{LCL}(s) H_f(s)}
$$
where $$G_{PWM}(s)$$ is the PWM converter gain and $$H_f(s)$$ is the feedback filter transfer function. The improved strategy ensures that the solar inverter maintains high performance even when the grid impedance varies.
4 Grid Voltage Fluctuation Adaptive Control Strategy
Grid voltage fluctuations, such as voltage sags and swells, are common disturbances that affect the stability of solar inverters. To enhance the adaptability of the solar inverter under these conditions, I have developed an adaptive control strategy based on an improved phase-locked loop (PLL). The PLL bandwidth is increased to 125 Hz, which enables faster detection of grid voltage disturbances. The phase detection time is reduced to 5 ms. The transfer function of the improved PLL can be expressed as:
$$
G_{PLL}(s) = K_{p,pll} + \frac{K_{i,pll}}{s}
$$
where $$K_{p,pll}$$ and $$K_{i,pll}$$ are the proportional and integral gains of the PLL loop filter, respectively. With the high-bandwidth PLL, the solar inverter can quickly detect voltage sag amplitude and phase information.
When a voltage sag is detected, the solar inverter switches to low voltage ride-through (LVRT) mode. In this mode, the reactive current is injected to support the grid voltage. My controller can deliver reactive power up to 80% of the rated value within 50 ms. The reactive current reference during LVRT is calculated as:
$$
I_{q,ref} = K_{lvrt} \cdot (1 – V_{g,pu}) \cdot I_{rated}
$$
where $$K_{lvrt}$$ is the LVRT gain coefficient, $$V_{g,pu}$$ is the per-unit grid voltage, and $$I_{rated}$$ is the rated current of the solar inverter. The active current is maintained stable during the sag to avoid power oscillation.
During the voltage recovery phase, I employ a piecewise smooth switching algorithm to avoid abrupt changes in current. The current distortion during the voltage recovery process is controlled below 3%. This control scheme maintains stable operation over a voltage fluctuation range of ±20%, significantly improving the grid adaptability of the solar inverter. The following equation describes the smooth switching logic:
$$
I_{ref}(t) = \begin{cases} I_{lvrt}(t) & V_g < V_{th1} \\ \alpha(t) I_{lvrt}(t) + (1-\alpha(t)) I_{normal}(t) & V_{th1} \le V_g \le V_{th2} \\ I_{normal}(t) & V_g > V_{th2} \end{cases}
$$
where $$\alpha(t)$$ is a smoothing factor that changes linearly from 1 to 0 during the recovery interval. This design avoids current overshoot and reduces mechanical and electrical stress on the solar inverter.
5 Harmonic Suppression Strategy
Harmonic distortion is one of the most critical power quality issues in grid-connected solar inverters. In industrial grids, the voltage at the point of common coupling often contains significant 3rd, 5th, and 7th order harmonics. To mitigate these harmonics, I have developed a selective harmonic compensation controller that operates in parallel with the fundamental current control loop. The controller adds compensation branches for the 3rd, 5th, and 7th harmonics, with a compensation bandwidth covering up to 350 Hz. The transfer function of each harmonic compensator is:
$$
G_h(s) = \frac{2 K_h \omega_c s}{s^2 + 2 \omega_c s + (h \omega_0)^2}
$$
where $$h$$ is the harmonic order (3, 5, or 7) and $$K_h$$ is the corresponding resonant gain. The combined output of the harmonic compensators is added to the fundamental current reference. With this strategy, the individual harmonic content in the output current of the solar inverter is reduced to below 0.5%, and the overall THD drops to 2%.
In addition, I have implemented a virtual impedance optimization algorithm to improve the damping characteristics of the solar inverter. The virtual impedance provides active damping of resonance peaks in the LCL filter. The virtual impedance is defined as:
$$
Z_v(s) = R_v + s L_v
$$
where $$R_v$$ and $$L_v$$ are the virtual resistance and virtual inductance, respectively. The optimization algorithm adjusts these parameters in real time to maintain stable damping under different operating conditions. The resonance suppression effect is improved by 40% at load conditions above 50% of rated power. The solar inverter has been verified in an industrial grid with excessive harmonic content, maintaining the point of common coupling voltage distortion below 3%. This demonstrates the practical engineering value of the proposed harmonic suppression strategy.
6 Experimental Verification
6.1 Experimental Platform Setup
To verify the proposed control strategies, I built an experimental platform based on a 10 kW solar inverter. The hardware platform uses a TMS320F28335 digital signal processor as the main controller. The power devices are 1200 V / 50 A insulated gate bipolar transistor modules. The sampling frequency is 20 kHz, and the switching frequency is 10 kHz. For testing, I used a Chroma 61845 grid simulator to emulate different grid conditions, a YOKOGAWA WT1800 power analyzer to measure power quality parameters, and an HDO4024A oscilloscope to capture waveform data. The software environment is based on Code Composer Studio, and the control algorithms are implemented in C language. A human-machine interface via serial communication enables parameter monitoring and data recording.
6.2 Steady-State Performance Test
I first conducted steady-state performance tests under standard grid conditions with a grid voltage of 380 V and a frequency of 50 Hz. The test data show that when the solar inverter operates at rated power, the total harmonic distortion of the output current remains at 1.3%, which is much lower than the standard limit of 3%. The power factor reaches 0.995, and the maximum efficiency of the system reaches 98.2%. I performed continuous 8-hour operation tests at 30%, 50%, 75%, and 100% of the rated power. The results show that the individual harmonic currents are all below 50% of the national standard limits, and the power factor remains above 0.99. The temperature rise curve shows that the junction temperature of the insulated gate bipolar transistor modules reaches a maximum of 85 °C at rated power, which is lower than the safety limit of 125 °C. The inductor temperature rise is maintained below 45 °C. Table 2 summarizes the performance data at different power points.
| Output power percentage | Current THD (%) | Power factor | Efficiency (%) | Junction temperature (℃) |
|---|---|---|---|---|
| 30% | 1.40 | 0.991 | 97.50 | 65 |
| 50% | 1.30 | 0.993 | 97.90 | 72 |
| 75% | 1.30 | 0.994 | 98.10 | 78 |
| 100% | 1.30 | 0.995 | 98.20 | 85 |
The steady-state results indicate that the improved control strategy significantly enhances the steady-state operation performance of the solar inverter. The key component temperatures and efficiency data validate the rationality of the system design and provide a reliable basis for engineering applications.
6.3 Dynamic Performance Test
Dynamic performance tests focus on the response characteristics of the solar inverter under grid disturbances. In my experiments, I compared the proposed improved control strategy with a conventional PI control strategy. The conventional PI controller uses fixed parameters and exhibits slower response and larger overshoot under grid disturbances. In the voltage sag test, the grid voltage drops from the rated value to 80%. The improved PLL algorithm detects the amplitude and phase information within 5 ms, and the controller immediately activates the low voltage ride-through mode. The test waveforms show that the reactive current component smoothly rises to 80% of the rated value within 50 ms, effectively supporting grid voltage recovery. At the same time, the active current remains stable without oscillation. In contrast, the conventional PI control strategy has a detection delay of 15 ms and a reactive response time exceeding 80 ms, with obvious current waveform oscillation.
For the load power step change test, I applied a 0% to 100% rated power step change. The current dynamic response time of the solar inverter is reduced to 15 ms, which is 30% faster than the conventional control. The current overshoot is controlled below 5%, and the steady-state error remains within ±0.5%. The conventional control shows a response time of about 22 ms, an overshoot of 20%, and a steady-state error of about 2%. In the grid frequency change test, the frequency is swept at a rate of 1 Hz/s. The solar inverter with the improved control strategy maintains synchronous operation and stable output power. The conventional control exhibits phase tracking deviation during rapid frequency changes, resulting in power fluctuations. The comparative test data fully demonstrate the superior dynamic response and disturbance rejection capability of my proposed control strategy.
6.4 Special Operating Condition Verification
Special operating condition tests are designed to verify the adaptability of the solar inverter under weak grid, harmonic pollution, and three-phase unbalance conditions. In the weak grid test, the short-circuit ratio is set to 2.5. The conventional control strategy tends to oscillate under this condition, while my control scheme maintains stable operation. The voltage distortion at the point of common coupling is controlled below 2.8%. In the harmonic condition test, 5.0% of the 5th harmonic and 3.0% of the 7th harmonic are superimposed on the grid voltage. The test results show that the output current contains only 0.8% of the 5th harmonic and 0.5% of the 7th harmonic, with the THD maintained below 2.0%. The variation of harmonic content over time is illustrated in the figure below.
The harmonic content measurements were taken at several consecutive time points during a three-hour test. Initially, after superimposing the 5% fifth harmonic and 3% seventh harmonic, the total harmonic content was about 8%. After one hour, the harmonic compensators had reduced the fifth harmonic to 1.5%, the seventh harmonic to 0.9%, and the total harmonic content to 2.4%. After two hours, the fifth harmonic was 1.0%, the seventh harmonic was 0.6%, and the total was 1.8%. At the end of the three-hour test, the fifth harmonic had decreased to 0.8%, the seventh harmonic to 0.5%, and the total harmonic content remained at around 1.9%. This confirms the long-term effectiveness of the harmonic suppression strategy.
For the three-phase unbalance test, I set the voltage unbalance degree to 5.0%. The output three-phase current unbalance degree does not exceed 3.0%, and the power fluctuation is less than 2.0%. The solar inverter operates continuously for 72 hours under these special conditions without any protection trip or failure. The test data fully verify that the proposed control strategies have strong grid adaptability and can meet the grid-connected requirements in various complex grid environments.
7 Conclusion
In this work, I have systematically investigated the grid-connected control strategies for solar inverters to enhance system stability in complex grid environments. The improved current control strategy combines a quasi-resonant inner loop with adaptive dead-time compensation, effectively improving the steady-state accuracy and dynamic response of the solar inverter. The grid voltage fluctuation adaptive control strategy, based on a high-bandwidth PLL and LVRT mode, enables rapid reactive power support during voltage sags and smooth recovery without large current distortion. The harmonic suppression strategy with selective compensation for the 3rd, 5th, and 7th harmonics significantly reduces the output current THD and improves power quality. The experimental results confirm that the solar inverter maintains stable operation under weak grid, voltage fluctuation, harmonic pollution, and three-phase unbalance conditions. The proposed control strategies provide a solid technical foundation for the grid integration of large-scale photovoltaic systems. Future research will focus on the coordinated control of multiple paralleled solar inverters to further improve the grid-connected performance of photovoltaic power plants.