In modern power systems, the integration of distributed generation sources, particularly photovoltaic (PV) systems, has become increasingly prevalent. Solar inverters play a critical role in converting DC power from PV panels to AC power compatible with the grid. However, grid disturbances such as voltage sags and swells pose significant challenges to the stable operation of these solar inverters. Voltage ride-through (VRT) capability, including low-voltage ride-through (LVRT) and high-voltage ride-through (HVRT), is essential to prevent sudden disconnection of solar inverters during transient faults, which could exacerbate grid instability. In this article, I propose a comprehensive control strategy for single-phase solar inverters to achieve robust voltage ride-through by real-time adjustment of active and reactive power. The strategy incorporates an instantaneous value prediction algorithm for rapid grid voltage detection and tracking, eliminating delays from A/D sampling and output filter networks. Extensive experimental validation demonstrates the feasibility and effectiveness of this approach, ensuring that solar inverters can maintain grid connection during faults and contribute to grid support functions.
The proliferation of solar inverters in residential and commercial applications underscores the need for advanced grid-friendly features. Traditional solar inverters often lack the ability to withstand voltage deviations, leading to frequent tripping and reduced grid reliability. My work focuses on single-phase solar inverters, which are common in distributed PV systems, and addresses their VRT requirements through innovative control techniques. The core idea is to dynamically modulate the inverter’s output voltage magnitude and phase angle to regulate active and reactive power injection or absorption, thereby stabilizing the point of common coupling (PCC) voltage. This not only enhances the resilience of solar inverters but also aligns with grid codes that mandate VRT capabilities for distributed energy resources.
Grid codes worldwide, such as China’s GB/T 19964-2012 and IEEE standards, specify VRT requirements for PV power stations. For LVRT, solar inverters must remain connected during voltage dips to a certain percentage of nominal voltage (e.g., down to 20%) for a defined duration (e.g., 0.625 seconds). For HVRT, although less standardized, solar inverters should tolerate voltage rises up to 135% of nominal voltage for short periods (e.g., 2 seconds) if recovery is prompt. These requirements ensure that solar inverters do not aggravate grid faults by disconnecting en masse. My control strategy is designed to meet these benchmarks, enabling solar inverters to provide ancillary services like reactive power support during disturbances, which is crucial for modern smart grids.
The fundamental control principle of the proposed solar inverter system involves a two-stage topology: a DC-DC converter for maximum power point tracking (MPPT) and a DC-AC inverter for grid connection. The inverter utilizes an LCL filter to attenuate switching harmonics, and the control system is implemented on a digital signal controller (DSC) for precise real-time processing. During normal operation, the solar inverter operates at near-unity power factor, injecting maximum active power into the grid. When a grid fault is detected, the control strategy shifts to VRT mode, adjusting the inverter’s output to prioritize reactive power compensation while limiting active power as needed. This is achieved by manipulating the power angle and voltage magnitude relative to the grid, based on the equivalent circuit model of the solar inverter connected to the grid through an impedance.
To elaborate, the equivalent circuit of a solar inverter tied to the grid can be represented as a voltage source \( U_o \angle \delta \) (inverter output) connected to the grid voltage \( U_s \angle 0 \) via a series impedance \( X = R + j\omega L \), where \( R \) is the resistance and \( L \) is the inductance. The active power \( P \) and reactive power \( Q \) delivered to the grid are given by:
$$ P = \frac{U_o U_s}{X} \sin \delta $$
$$ Q = \frac{U_o U_s}{X} \cos \delta – \frac{U_s^2}{X} $$
From these equations, it is evident that by controlling the power angle \( \delta \) and the inverter output voltage magnitude \( U_o \), one can regulate both \( P \) and \( Q \). During voltage sags, for instance, the solar inverter can increase \( U_o \) and reduce \( \delta \) to inject reactive power, thereby supporting the grid voltage. Conversely, during voltage swells, the solar inverter can decrease \( U_o \) and adjust \( \delta \) to absorb reactive power. The limits of reactive power capability are constrained by the inverter’s apparent power rating, as described by:
$$ P^2 + \left( Q + \frac{U_s^2}{X} \right)^2 = \left( \frac{U_o U_s}{X} \right)^2 $$
This relationship implies that to increase reactive power output, the active power must be reduced, which is a key aspect of the VRT strategy for solar inverters. The control system continuously monitors the grid voltage and computes the required adjustments to maintain compliance with grid standards while protecting the inverter hardware.
A critical component of the VRT strategy is the instantaneous value prediction algorithm for grid voltage detection. Solar inverters must respond swiftly to grid faults—typically within milliseconds—to prevent maloperation. Conventional voltage sensing methods suffer from delays due to A/D conversion and filter dynamics, which can impede rapid response. My algorithm predicts the grid voltage value half a carrier cycle ahead, enabling real-time tracking with minimal latency. Assuming a carrier frequency \( f_c = 12.8 \) kHz (giving a carrier period \( T_c = 78.125 \mu s \)) and a grid frequency of 50 Hz, the algorithm samples the voltage at each carrier interval. Using Taylor series expansion, the predicted voltage at time \( t + T_c/2 \) is computed from three consecutive samples \( n_1, n_2, n_3 \) taken at times \( t_1, t_2, t_3 \) (with \( t_i – t_{i-1} = T_c \)):
$$ f(x_3 + \Delta t) = f(x_3) + f'(x_3)\Delta t + \frac{1}{2} f”(x_3)\Delta t^2 + O(\Delta t^3) $$
With \( \Delta t = T_c/2 \), and approximating derivatives linearly:
$$ f'(x_3) = n_3 – n_2 $$
$$ f”(x_3) = (n_3 – n_2) – (n_2 – n_1) = n_3 – 2n_2 + n_1 $$
Substituting and simplifying yields the predicted value:
$$ U_s(t_3 + T_c/2) = n_3 + \frac{3}{4}(n_3 – n_2) – \frac{1}{8}(n_3 – n_1) $$
This algorithm allows the solar inverter to detect voltage deviations within one carrier cycle, facilitating immediate control actions. The error \( E = U_b(t_0) – U_s(t_0) \), where \( U_b \) is the nominal voltage, is used to trigger the VRT mode. This fast detection is pivotal for the performance of solar inverters during transient faults.
The control implementation for the solar inverter’s inverter stage employs a deadbeat control scheme to achieve rapid voltage tracking. Deadbeat control is a discrete-time method that computes the required duty cycle for the next switching period to eliminate output error in one step. For the LCL-filtered inverter, the state-space model is derived considering the inverter-side inductor current \( i_{L1} \), grid-side inductor current \( i_{L2} \), and capacitor voltage \( u_c \). The state equations are:
$$ \begin{bmatrix} \dot{u}_c \\ \dot{i}_{L1} \end{bmatrix} = \begin{bmatrix} 0 & \frac{1}{C} \\ -\frac{1}{L_1} & 0 \end{bmatrix} \begin{bmatrix} u_c \\ i_{L1} \end{bmatrix} + \begin{bmatrix} 0 \\ \frac{1}{L_1} \end{bmatrix} u_k + \begin{bmatrix} -\frac{1}{C} \\ 0 \end{bmatrix} i_{L2} $$
where \( u_k \) is the inverter bridge output voltage. Discretizing with period \( T \) and solving for the capacitor voltage at the next sample gives:
$$ u_c(K+1) = \cos(\omega T) u_c(K) + L_1 \omega \sin(\omega T) i_{L1}(K) – L_1 \omega \sin(\omega T) i_{L2}(K) + \frac{\omega}{2} u_{dc} \sin(\omega T/2) \Delta T(K) $$
with \( \omega = 1/\sqrt{C L_1} \). By setting \( u_c(K+1) \) equal to the reference voltage \( u_{ref}(K+1) \), the required pulse width \( \Delta T(K) \) is computed as:
$$ \Delta T(K) = \frac{1}{\frac{\omega}{2} u_{dc} \sin(\omega T/2)} \left\{ u_{ref}(K+1) – \cos(\omega T) u_c(K) + L_1 \omega \sin(\omega T) [i_{L2}(K) – i_{L1}(K)] \right\} $$
The reference voltage \( u_{ref} \) is synthesized based on the grid voltage conditions. During normal operation, \( u_{ref} \) matches the grid voltage plus the drop across the coupling inductance. During faults, \( u_{ref} \) is adjusted in magnitude and phase to achieve the desired reactive power flow. Specifically, \( u_{ref} = U_o \sin(\omega t + \phi + \delta) \), where \( U_o \) is controlled for reactive support and \( \delta \) is the power angle for active power regulation. This approach enables seamless transition between normal and fault modes for solar inverters.
To illustrate the parameter design and performance trade-offs, Table 1 summarizes key components and values used in the experimental solar inverter system. These parameters are typical for single-phase solar inverters in residential applications and were optimized for VRT performance.
| Component | Symbol | Value | Description |
|---|---|---|---|
| DC-DC Output Capacitor | C1 | 3300 μF | Filters DC link voltage |
| Inverter-side Inductor | L1 | 10.7 mH | Part of LCL filter |
| Grid-side Inductor | L2 | 1 mH | Coupling inductance |
| Filter Capacitor | C | 2.2 μF | LCL filter capacitor |
| Carrier Frequency | f_c | 12.8 kHz | Switching frequency |
| Grid Frequency | f_m | 50 Hz | Standard grid frequency |
| DC Link Voltage | u_dc | 450 V | Nominal DC voltage |
The experimental validation of the proposed VRT strategy for solar inverters was conducted using a hardware setup comprising a single-phase full-bridge inverter, LCL filter, and a programmable AC source to emulate grid faults. The control algorithms were implemented on a TMS320F28xxx DSC, which executed the instantaneous prediction and deadbeat control in real-time. The solar inverter was tested under various fault scenarios, including voltage dips to 81% of nominal (178 V from 220 V) and voltage surges to 126% (277 V from 220 V), simulating both LVRT and HVRT conditions. The results confirmed that the solar inverter could maintain connection and provide appropriate reactive power support within a few grid cycles.
During a voltage sag to 81%, the solar inverter rapidly adjusted its output voltage magnitude to be slightly higher than the grid voltage, while shifting the power angle from near 90° (maximum active power) towards 0° (maximum reactive power injection). This transition was completed within approximately 150 ms (7.5 cycles), as shown in the experimental waveforms. The fast response is attributed to the prediction algorithm, which enabled detection within one carrier cycle. Similarly, for a voltage swell to 126%, the solar inverter reduced its output voltage magnitude and adjusted the power angle to absorb reactive power, effectively performing HVRT. In both cases, the solar inverter successfully rode through the faults without disconnecting, and upon grid recovery, it returned to normal operation within a few cycles, demonstrating robustness.
The VRT capability of solar inverters is further enhanced by their ability to handle islanding conditions. When the grid is disconnected (simulating islanding), the solar inverter can detect the loss of grid voltage via the prediction algorithm and cease power injection immediately, preventing unsynchronized operation. Once grid voltage is restored, the solar inverter can seamlessly resynchronize and resume energy export. This feature is critical for safety and grid stability, as it avoids inadvertent islanding that could endanger utility workers or damage equipment.
The integration of solar inverters with energy storage systems, as depicted in the image above, can further augment VRT performance. Energy storage inverters can provide additional active power buffering during faults, allowing solar inverters to focus on reactive support. However, the focus of this work is on standalone solar inverters without storage, highlighting their inherent capabilities through advanced control.
To quantify the reactive power support capacity of solar inverters during faults, consider the maximum reactive power \( Q_{\text{max}} \) that can be injected or absorbed. From the power equations, with \( P = 0 \), the limits are:
$$ – \left( \frac{U_s U_o}{X} \right) \leq Q \leq \left( \frac{U_s U_o}{X} \right) $$
In practice, for a solar inverter with a rated apparent power \( S_{\text{rated}} \), the reactive power capability is constrained by \( Q \leq \sqrt{S_{\text{rated}}^2 – P^2} \). During a severe voltage sag, if the grid voltage drops to 0.2 per unit, the required reactive current injection can be substantial. Grid codes often mandate that solar inverters provide reactive current proportional to the voltage deviation, e.g., \( I_q = k (1 – U_s) I_{\text{rated}} \), where \( k \) is a gain (often 2 or more). My control strategy inherently accommodates such requirements by modulating \( U_o \) and \( \delta \) based on real-time voltage error.
A comparative analysis of VRT strategies for solar inverters is presented in Table 2, which contrasts different control methods in terms of response speed, complexity, and suitability for single-phase systems. The proposed strategy excels in fast detection and seamless power adjustment, making it ideal for single-phase solar inverters.
| Control Method | Response Time | Complexity | Single-Phase Suitability | Key Features |
|---|---|---|---|---|
| Dual Hysteresis Current Control | Fast (< 1 cycle) | Moderate | Good | Variable switching frequency |
| dq-Decoupling Control | Slow (~2/3 cycle delay) | High | Poor (requires transformation) | Accurate power regulation |
| Proposed Deadbeat with Prediction | Very Fast (< 1 carrier cycle) | Moderate | Excellent | No delay, real-time adjustment |
The mathematical foundation of the prediction algorithm can be extended to three-phase solar inverters, but its simplicity is particularly advantageous for single-phase systems where computational resources are limited. For solar inverters in distributed PV applications, the algorithm minimizes the need for additional hardware sensors, reducing cost and improving reliability. Moreover, the deadbeat control ensures that the inverter output tightly tracks the reference, even during rapid transients, which is vital for maintaining power quality.
In terms of implementation, the control code for the solar inverter was structured into modular tasks: grid voltage sampling, prediction computation, power reference generation, and PWM update. The DSC’s high-speed ADC and PWM peripherals facilitated precise timing, with the entire control loop executed within each carrier period. This real-time processing is key to the VRT performance of solar inverters, as delays could lead to instability or non-compliance with grid codes.
Future work on solar inverters could explore integration with advanced grid-support functions like frequency regulation and harmonic compensation. The proposed VRT strategy provides a foundation for these enhancements, as the fast voltage detection and power control mechanisms can be adapted for multi-objective operation. Additionally, the use of machine learning techniques to predict grid faults based on historical data could further improve the proactive capabilities of solar inverters.
In conclusion, the voltage ride-through control strategy presented herein offers a robust solution for single-phase solar inverters to withstand grid voltage disturbances. By combining an instantaneous value prediction algorithm with deadbeat power control, the strategy enables rapid detection and adjustment of active and reactive power, ensuring compliance with LVRT and HVRT requirements. Experimental results validate that solar inverters equipped with this strategy can maintain grid connection during faults, provide reactive support, and recover seamlessly upon grid restoration. This contributes to the reliability and stability of power systems with high penetration of solar energy, underscoring the evolving role of solar inverters as active grid participants.
The widespread adoption of such advanced solar inverters will be instrumental in achieving resilient and sustainable energy networks. As grid codes continue to evolve, mandating stricter VRT capabilities, the proposed approach provides a practical and effective means for manufacturers to enhance their products. Ultimately, solar inverters are not merely power converters but vital components of modern electricity infrastructure, and their ability to ride through voltage anomalies is paramount for a secure energy future.