With the global shift towards renewable energy, photovoltaic (PV) power generation has emerged as a key technology for sustainable electricity production. As PV systems become more integrated into distribution grids, the efficiency, cost, and reliability of solar inverters—the critical interface between PV arrays and the grid—have gained paramount importance. Traditional solar inverters often employ standard boost converters and three-phase full-bridge inverters, which may not optimally handle wide voltage ranges or rapidly changing environmental conditions. These limitations can lead to reduced energy harvest, higher system costs, and complex control requirements. In this context, we propose a novel solar inverter topology that integrates a Super-Re-Lift Luo-Boost circuit with a three-phase four-switch inverter unit, coupled with a feedforward power predictive control strategy based on partitioned optimal searching. This approach aims to significantly enhance the performance of solar inverters by improving efficiency, lowering costs, and simplifying control, thereby advancing the deployment of PV systems.
The core innovation lies in the unique structure of the solar inverter, which combines high-gain power conversion with a reduced-switch-count inverter design. The Super-Re-Lift Luo-Boost circuit, derived from advanced voltage-lift techniques, offers a much higher voltage conversion ratio compared to conventional boost circuits, enabling efficient operation even when PV array outputs are low. Meanwhile, the three-phase four-switch inverter unit reduces component count and cost while maintaining grid compatibility. To optimize power extraction, we introduce a feedforward control method that predicts the maximum power point (MPP) of the PV array using a partitioned environmental condition search algorithm. This eliminates the need for traditional DC-link voltage control loops, streamlining the overall control architecture and improving dynamic response. Throughout this paper, we will delve into the design, analysis, and validation of this solar inverter system, demonstrating its superiority through mathematical models, simulations, and experimental results.
The increasing penetration of solar inverters in power grids necessitates advancements in both hardware and control strategies. Typically, solar inverters must perform maximum power point tracking (MPPT), DC-AC conversion, and grid synchronization, often requiring complex multi-loop controls. Common topologies, such as those based on classic boost converters, have limited voltage gain, which can constrain system design when PV voltages fluctuate widely. Moreover, traditional MPPT algorithms, like perturb-and-observe or incremental conductance, involve iterative calculations that may slow response times. Our proposed solar inverter addresses these issues by leveraging a high-step-up converter and a predictive feedforward scheme, reducing losses and enhancing reliability. By focusing on the integration of innovative components and intelligent control, this work contributes to the ongoing evolution of solar inverter technology for broader renewable energy adoption.
The proposed solar inverter topology is depicted in a conceptual diagram, showcasing the integration of the Luo-Boost circuit and the inverter unit. This solar inverter design begins with a PV array composed of series-parallel connected cells, typically generating a DC voltage ranging from 200 V to 400 V. The Super-Re-Lift Luo-Boost circuit, consisting of clamping diodes, intermediate capacitors, and two positive-output Luo-Boost stages, elevates this voltage to a higher DC level. The DC link utilizes two series-connected capacitors, with the midpoint forming the third phase output for the inverter. The inverter section employs a three-phase four-switch configuration, where only two bridge arms are used, reducing the number of switching devices. Filter inductors connect the inverter outputs to the grid via a contactor, ensuring smooth current injection. This structure not only enhances the voltage gain but also cuts down on component costs, making the solar inverter more economical for distributed generation applications.
To understand the operation of the Super-Re-Lift Luo-Boost circuit, we analyze its steady-state behavior. Let the PV array output voltage be \(U_{pv}\), the intermediate capacitor voltage be \(U_M\), and the DC-link voltage be \(U_{dc}\). The switch in the Luo-Boost circuit operates with a switching period \(T\) and an average duty cycle \(k\). For the first inductor \(L_1\), the voltage during the switch-on period \(kT\) is \(U_{pv}\), and during the switch-off period \((1-k)T\) is \(U_M – 2U_{pv}\). Applying volt-second balance:
$$ \frac{U_{pv}}{L_1} kT = \frac{U_M – 2U_{pv}}{L_1} (1-k)T $$
Solving for \(U_M\):
$$ U_M = U_{pv} \left( \frac{2 – k}{1 – k} \right) $$
Similarly, for the second inductor \(L_2\), with voltage \(U_M\) during \(kT\) and \(U_{dc} – 2U_M\) during \((1-k)T\):
$$ \frac{U_M}{L_2} kT = \frac{U_{dc} – 2U_M}{L_2} (1-k)T $$
Solving for \(U_{dc}\):
$$ U_{dc} = U_M \left( \frac{2 – k}{1 – k} \right) $$
Substituting \(U_M\) from the first equation, the overall voltage conversion ratio of the Super-Re-Lift Luo-Boost circuit is:
$$ \frac{U_{dc}}{U_{pv}} = \left( \frac{2 – k}{1 – k} \right)^2 $$
This formula reveals a significant advantage: for duty cycles between 0.5 and 0.8, the boost ratio ranges from 9 to 36, vastly exceeding the typical 2 to 5 ratio of conventional boost converters used in many solar inverters. This high gain allows the solar inverter to efficiently handle low PV voltages, reducing switching losses and improving overall system efficiency. Moreover, the capacitors and inductors in this circuit primarily serve energy transfer functions, lowering component stress and cost compared to traditional designs.
The three-phase four-switch inverter unit further optimizes the solar inverter by minimizing device count. Assuming a balanced grid, the inverter outputs phase voltages \(e_{aN}\), \(e_{bN}\), and \(e_{cN}\) relative to the grid neutral point \(N\), with output currents \(i_a\), \(i_b\), and \(i_c\). The DC-link midpoint \(n\) is used to derive the third phase. From Kirchhoff’s voltage law and balanced system conditions, the relationships can be expressed as:
$$ e_{aN} = \frac{2u_{an} – u_{bn}}{3}, \quad e_{bN} = \frac{2u_{bn} – u_{an}}{3}, \quad e_{cN} = -\frac{u_{an} + u_{bn}}{3} $$
where \(u_{an}\) and \(u_{bn}\) are the inverter output voltages relative to the DC-link midpoint. The output currents are governed by the filter inductance \(L_s\):
$$ i_a = \frac{e_{aN} – u_{aN}}{j\omega L_s}, \quad i_b = \frac{e_{bN} – u_{bN}}{j\omega L_s}, \quad i_c = \frac{e_{cN} – u_{cN}}{j\omega L_s} $$
Here, \(u_{aN}\), \(u_{bN}\), and \(u_{cN}\) are the grid voltages. By controlling only the A and B phase currents, the inverter can regulate its output power, simplifying the control scheme for this solar inverter. This reduction in control complexity aligns with the goal of enhancing reliability and response speed.
To quantify the benefits of the proposed solar inverter topology, Table 1 compares key parameters between the Super-Re-Lift Luo-Boost circuit and a traditional boost converter, highlighting the advantages for solar inverter applications.
| Parameter | Traditional Boost Converter | Super-Re-Lift Luo-Boost Circuit |
|---|---|---|
| Voltage Gain | \(\frac{1}{1-k}\) (typically 2-5) | \(\left( \frac{2-k}{1-k} \right)^2\) (typically 9-36) |
| Component Count | Lower | Moderate (additional diodes/capacitors) |
| Efficiency at Low PV Voltage | Reduced due to high duty cycles | Improved due to lower duty cycles |
| Cost Impact | Lower component cost but may need higher-rated devices | Higher gain allows smaller PV arrays, reducing overall system cost |
| Suitability for Wide Voltage Ranges | Limited | Excellent, enhancing solar inverter flexibility |
The control strategy for this solar inverter centers on maintaining DC-link voltage balance while achieving MPPT. Instead of conventional feedback loops, we employ a feedforward power predictive control based on partitioned optimal searching. This method predicts the PV array’s maximum power output \(P_{ref}^*\) as a command signal for the inverter, enabling rapid energy transfer to the grid. The Luo-Boost circuit’s switch is controlled to keep the DC-link voltage constant at a reference \(U_{dc-ref}\), effectively performing MPPT without additional loops. This approach reduces computational burden and improves the solar inverter’s dynamic response.
The partitioned optimal search algorithm operates by categorizing environmental conditions—specifically solar irradiance \(S\) and temperature \(T\)—into discrete intervals. For a given region, historical data of \(S\) and \(T\) are divided into partitions using constant resolution factors \(\xi_x\) and \(\xi_y\). Each partition \(n\) has average values \(S_n\) and \(T_n\), and a corresponding memory stack \(SP_n\) of length \(M\) stores historical maximum power values \(P_{nmy}\) for that partition. The stack elements are initialized as:
$$ P_{nmy} = P_{nm0} + y \Delta \varphi, \quad y = 0, 1, \dots, M-1 $$
where \(P_{nm0}\) is an initial power value for partition \(n\), and \(\Delta \varphi\) is a power increment step. A pointer variable \(SP_{n-pos}\) indicates the current stack top. The algorithm adjusts the predicted power based on the DC-link voltage error \(\Delta \varepsilon_{DC} = U_{dc-ref} – U_{dc}\). Let \(\varepsilon_0\) be the normal voltage tolerance, \(\varepsilon_{up-max}\) the maximum positive error, and \(\varepsilon_{down-max}\) the maximum negative error. The search rules are summarized in Table 2.
| Condition | Action | Description |
|---|---|---|
| \(\Delta \varepsilon_{DC} \geq 0\) and \(0 \leq |\Delta \varepsilon_{DC}| < \varepsilon_0\) | Decrement \(y\) by 1 | Small positive error: reduce predicted power slightly |
| \(\Delta \varepsilon_{DC} \geq 0\) and \(\varepsilon_0 \leq |\Delta \varepsilon_{DC}| < \varepsilon_{up-max}\) | Set \(SP_{n-pos} = y-1\), \(y=0\) | Moderate positive error: reset to lower power |
| \(\Delta \varepsilon_{DC} \geq 0\) and \(|\Delta \varepsilon_{DC}| \geq \varepsilon_{up-max}\) | Keep \(y = SP_{n-pos}\) | Large positive error: maintain current prediction |
| \(\Delta \varepsilon_{DC} < 0\) and \(0 \leq |\Delta \varepsilon_{DC}| < \varepsilon_0\) | Increment \(y\) by 1 | Small negative error: increase predicted power slightly |
| \(\Delta \varepsilon_{DC} < 0\) and \(\varepsilon_0 \leq |\Delta \varepsilon_{DC}| < \varepsilon_{down-max}\) | Set \(SP_{n-pos} = y+1\), \(y=0\) | Moderate negative error: reset to higher power |
| \(\Delta \varepsilon_{DC} < 0\) and \(|\Delta \varepsilon_{DC}| \geq \varepsilon_{down-max}\) | Keep \(y = SP_{n-pos}\) | Large negative error: maintain current prediction |
This algorithm minimizes multiplications, enabling fast execution on digital signal processors (DSPs), which is crucial for real-time solar inverter control. By tuning parameters like \(\xi_x\), \(\xi_y\), \(\Delta \varphi\), and \(\varepsilon_0\), the DC-link voltage can be regulated within about 1 V, ensuring high precision and reliability for the solar inverter. The feedforward control block diagram for the solar inverter is straightforward: the predicted power \(P_{ref}^*\) is divided by half the grid voltage amplitudes to generate current references, which are then multiplied by unit-phase signals from a phase-locked loop (PLL) to form command currents \(i_a^*\) and \(i_b^*\). These are compared with measured currents and processed through PI controllers to produce PWM signals for the inverter switches. This design eliminates the DC-voltage loop, reducing control delay and enhancing the solar inverter’s responsiveness.
To validate the proposed solar inverter and control method, we conducted simulations using MATLAB/Simulink and built an experimental prototype. The PV array was modeled with 15 series and 4 parallel cells, generating voltages between 250 V and 350 V. The grid voltage was 380 V at 50 Hz, with inverter filter inductors of 3 mH and equivalent resistance 0.02 Ω. The DC-link capacitors were 10 mF each, with \(U_{dc-ref} = 1200\) V. The Luo-Boost circuit used \(L_1 = L_2 = 2\) mH and \(C_1 = C_2 = C_M = 220\) μF, while the PV array output capacitor was 5 mF. Control parameters were set as \(\xi_x = 5\) W/m², \(\xi_y = 0.01\) °C, \(\varepsilon_0 = 1\) V, \(\varepsilon_{up-max} = \varepsilon_{down-max} = 5\) V, and \(\Delta \varphi = 5\) W.
In Simulation 1, we compared the proposed solar inverter (with Super-Re-Lift circuit) against a traditional solar inverter (with boost converter), both under constant irradiance \(S = 500\) W/m² and temperature \(T = 25\)°C. The results, summarized in Table 3, demonstrate the superiority of the new solar inverter in terms of output power and settling time.
| Metric | Traditional Solar Inverter (Boost) | Proposed Solar Inverter (Super-Re-Lift) |
|---|---|---|
| Output Current (RMS) | 1.8 A | 2.3 A |
| Output Power | 1200 W | 1500 W |
| DC-Link Voltage Settling Time | 6 s | 3 s |
| Efficiency Estimate | Lower due to higher losses | Higher due to reduced switching losses |
The proposed solar inverter achieved higher power output and faster dynamic response, highlighting its efficiency benefits. In Simulation 2, we tested the control strategy under a step change in irradiance from 1000 W/m² to 2000 W/m², with temperature constant. We compared the feedforward predictive control against traditional dual-PI control, both using the new solar inverter topology. The results, shown in Table 4, confirm the advantages of the feedforward approach.
| Metric | Traditional Dual-PI Control | Feedforward Predictive Control |
|---|---|---|
| Response Time to Step Change | 3-4 cycles (~60-80 ms) | Almost instantaneous |
| DC-Link Voltage Overshoot | Significant, with oscillations | Minimal, smooth regulation |
| Power Factor | Maintained at 1 | Maintained at 1 |
| Control Complexity | Higher due to multiple loops | Lower, simplifying solar inverter implementation |
The feedforward control enabled near-instantaneous adaptation to changing conditions, reducing voltage disturbances and improving the solar inverter’s reliability. For experimental validation, we constructed a 5 kW solar inverter prototype using Infineon BSM50GB170DN2 IGBT modules and a TI DSP2812 controller. Under stable midday conditions with about 4.5 kW output, measurements from a FLUKE power quality analyzer showed stable DC-link voltage and low-distortion grid currents, meeting grid-connection standards. This practical verification underscores the feasibility of the proposed solar inverter in real-world applications.
The integration of advanced components like the Luo-Boost circuit and the partitioned search algorithm sets this solar inverter apart from conventional designs. By achieving higher gain with lower duty cycles, the solar inverter minimizes switching losses, which is critical for efficiency in PV systems. Additionally, the reduced switch count in the inverter unit lowers cost and failure rates, enhancing the economic viability of solar inverters for residential and commercial use. The control strategy’s computational efficiency allows for implementation on low-cost DSPs, making the solar inverter accessible for widespread deployment. These attributes collectively contribute to a more robust and high-performing solar inverter solution.
Further analysis of the solar inverter’s performance under partial shading or varying grid conditions would be valuable. The partitioned search algorithm could be extended to incorporate real-time weather forecasts, improving prediction accuracy. Moreover, the solar inverter topology could be adapted for hybrid systems integrating energy storage, broadening its applicability. As solar inverter technology evolves, such innovations will play a key role in maximizing renewable energy utilization and grid stability.
In conclusion, we have presented a novel solar inverter topology combining a Super-Re-Lift Luo-Boost circuit and a three-phase four-switch inverter unit, along with a feedforward power predictive control strategy based on partitioned optimal searching. This solar inverter offers significant advantages: higher voltage gain improves efficiency at low PV voltages, reduced component count lowers cost, and the predictive control simplifies implementation while enhancing dynamic response. Simulations and experiments validate the design, showing superior performance compared to traditional solar inverters. This work advances solar inverter technology by addressing key challenges in efficiency, cost, and control, paving the way for more effective PV integration into power grids. Future efforts could focus on scaling the solar inverter for larger systems and integrating smart grid functionalities.