I designed a three-phase off-grid solar inverter by using a model-based workflow rather than a purely handwritten software path. The central motivation for my work was simple: a modern solar inverter contains many control, protection, sampling, modulation, and communication functions, and those functions are often difficult to verify when they are written directly as large blocks of C code. In my design flow, I used graphical models in Matlab and Simulink to describe the solar inverter behavior, I simulated the power stage and controller together, and I generated target code for a digital signal processor with Embedded Coder. This allowed me to move from concept to prototype faster and with fewer manual coding errors. The solar inverter I studied is a three-phase off-grid voltage-source inverter based on instantaneous reactive power theory, a voltage-current double closed-loop controller, and space-vector pulse-width modulation. I verified the model through simulation and then validated it on a laboratory prototype. The results showed a stable three-phase sinusoidal output, good total harmonic distortion, and close agreement between simulation and experiment.
I treated the solar inverter as a cyber-physical system in which the power circuit, the sampling chain, the control algorithm, and the modulation stage must be co-designed. In a traditional workflow, I would first build a simulation, then translate the algorithm into C, then debug the C implementation on hardware. That workflow often creates a gap between the simulation model and the embedded implementation. In the model-based workflow I used, the simulation model and the embedded controller share the same graphical structure. I parameterized the controller, tested it in simulation, and then generated code for the target processor. This does not remove the need for engineering judgment, but it does reduce the amount of repetitive code and makes the solar inverter control logic easier to inspect, document, and maintain.
| Symbol | Meaning | Unit |
|---|---|---|
| \(u\) | Unfiltered inverter output voltage | V |
| \(i\) | Inductor current | A |
| \(u_{R1}\) | Load or capacitor voltage | V |
| \(i_{R1}\) | Load current | A |
| \(L\) | LC filter inductance | H |
| \(C\) | LC filter capacitance | F |
| \(\omega\) | Angular frequency | rad/s |
| \(u_d,u_q\) | Voltages in the rotating dq frame | V |
| \(i_d,i_q\) | Currents in the rotating dq frame | A |
| \(u_d^*,u_q^*\) | Voltage references in the dq frame | V |
| \(i_d^*,i_q^*\) | Current references in the dq frame | A |
| \(T_s\) | Switching or sampling period | s |
I selected a three-phase full-bridge topology because it gives a good balance between component count, control flexibility, and output quality for a solar inverter. The bridge uses six fully controlled metal-oxide-semiconductor field-effect transistors. I chose power MOSFETs because their drive circuits are relatively simple, their required drive power is small, and their switching speed is high. In the prototype, I used IRF3710 devices, a 50 V DC input, and a three-phase LC filter. A large DC-link capacitor was placed across the DC bus to reduce voltage ripple and to limit the effect of DC-link variation on the solar inverter output. The inverter converts the DC input into a three-phase AC output, and the LC filter attenuates the switching harmonics so that the load sees a nearly sinusoidal voltage.
The main circuit is balanced and symmetrical, so I first wrote the equations for one phase and then extended them to the others. For phase a, Kirchhoff’s voltage law and Kirchhoff’s current law give the following continuous-time model:
$$L\frac{di}{dt}=u-u_{R1}$$
$$C\frac{du_{R1}}{dt}=i-i_{R1}$$
The same structure applies to phases b and c with the corresponding phase variables. These equations form the foundation of the solar inverter model. They show that the inductor current is driven by the difference between the inverter voltage and the capacitor voltage, while the capacitor voltage is driven by the difference between the inductor current and the load current. When I design a controller for a solar inverter, I must consider both the inner current dynamics and the outer voltage dynamics.
To simplify the controller, I transformed the three-phase stationary variables into a two-phase stationary frame and then into a two-phase synchronous rotating frame. The Clarke transformation from abc to \(\alpha\beta\) is:
$$\begin{bmatrix}u_\alpha\\u_\beta\end{bmatrix}
=
\sqrt{\frac{2}{3}}
\begin{bmatrix}
1 & -\frac{1}{2} & -\frac{1}{2}\\
0 & \frac{\sqrt{3}}{2} & -\frac{\sqrt{3}}{2}
\end{bmatrix}
\begin{bmatrix}u_a\\u_b\\u_c\end{bmatrix}$$
The Park transformation from \(\alpha\beta\) to dq is:
$$\begin{bmatrix}u_d\\u_q\end{bmatrix}
=
\begin{bmatrix}
\cos\omega t & -\sin\omega t\\
-\sin\omega t & \cos\omega t
\end{bmatrix}
\begin{bmatrix}u_\alpha\\u_\beta\end{bmatrix}$$
After applying these transformations to the voltage and current equations, I obtained the dq-frame state equations for the solar inverter:
$$L\frac{di_d}{dt}=u_d-u_{R1d}+L\omega i_q$$
$$L\frac{di_q}{dt}=u_q-u_{R1q}+L\omega i_d$$
$$C\frac{du_{R1d}}{dt}=i_d-i_{R1d}+\omega C u_{R1q}$$
$$C\frac{du_{R1q}}{dt}=i_q-i_{R1q}+\omega C u_{R1d}$$
These equations reveal the cross-coupling between the d-axis and q-axis. In a solar inverter controller, this coupling must be compensated, otherwise the d-axis and q-axis loops interact and the dynamic response becomes less predictable. I used the dq model as the basis for the double closed-loop controller and for decoupling terms in the current inner loop.
| Frame | Variables | Purpose |
|---|---|---|
| abc | \(u_a,u_b,u_c\), \(i_a,i_b,i_c\) | Physical three-phase quantities |
| \(\alpha\beta\) | \(u_\alpha,u_\beta\), \(i_\alpha,i_\beta\) | Orthogonal stationary representation |
| dq | \(u_d,u_q\), \(i_d,i_q\) | DC-like quantities for PI control |
| PWM | Duty cycles and switching states | Gate signals for the solar inverter bridge |
For the control strategy, I used a voltage outer loop and a current inner loop. The voltage outer loop samples the AC output voltage, transforms it into the dq frame, and compares the measured d-axis and q-axis voltages with their references. The error is passed through a proportional-integral regulator to produce current references. The current inner loop then samples the output currents, transforms them into the dq frame, and compares them with the references from the voltage loop. A second proportional-integral regulator produces the voltage commands that are eventually sent to the modulation stage. This cascaded structure is widely used in a solar inverter because it separates the slower voltage dynamics from the faster current dynamics.
I expressed the voltage-loop PI control as:
$$i_d^* = K_{pv}(u_d^*-u_d) + K_{iv}\int (u_d^*-u_d)\,dt$$
$$i_q^* = K_{pv}(u_q^*-u_q) + K_{iv}\int (u_q^*-u_q)\,dt$$
I expressed the current-loop PI control with decoupling as:
$$u_d = K_{pi}(i_d^*-i_d) + K_{ii}\int (i_d^*-i_d)\,dt – \omega L i_q + u_{R1d}$$
$$u_q = K_{pi}(i_q^*-i_q) + K_{ii}\int (i_q^*-i_q)\,dt + \omega L i_d + u_{R1q}$$
The q-axis voltage reference can be set to zero when I want unity power factor operation. In that case, the q-axis current reference determines whether the solar inverter exchanges reactive power with the load or not. This is one reason I used instantaneous reactive power theory: it provides a clear physical interpretation of active and reactive power in the dq frame.
I used the following instantaneous power expressions in the dq frame:
$$p = \frac{3}{2}(u_d i_d + u_q i_q)$$
$$q = \frac{3}{2}(u_q i_d – u_d i_q)$$
When \(q=0\), the solar inverter operates with unity power factor and the output current is aligned with the output voltage. When the q-axis reference is nonzero, the solar inverter can provide or absorb reactive power. In my design, the voltage outer loop regulates the AC voltage magnitude, while the current inner loop provides fast current limiting and improves the transient response.
| Loop | Input | Output | Main function |
|---|---|---|---|
| Voltage outer loop | AC voltage error in dq | Current references \(i_d^*,i_q^*\) | Regulates output voltage and sets power flow |
| Current inner loop | Current error in dq | Voltage commands \(u_d,u_q\) | Provides fast current control and decoupling |
| Modulation stage | \(u_d,u_q\) | SVPWM gate signals | Generates switching pattern for the bridge |
For modulation, I selected space-vector pulse-width modulation because it makes better use of the DC bus than sinusoidal pulse-width modulation and produces lower current harmonic content. In SVPWM, the reference voltage vector is synthesized from adjacent active vectors and zero vectors. The reference vector can be written as:
$$V_{ref}=\frac{2}{3}\left(v_a+a v_b+a^2 v_c\right), \qquad a=e^{j2\pi/3}$$
For a reference vector located in a sector, the dwell times of the two adjacent active vectors are calculated from:
$$T_1=\frac{\sqrt{3}T_s |V_{ref}|}{V_{dc}}\sin(60^\circ-\theta)$$
$$T_2=\frac{\sqrt{3}T_s |V_{ref}|}{V_{dc}}\sin(\theta)$$
$$T_0=T_s-T_1-T_2$$
I used the zero-vector time \(T_0\) to complete the switching period. The SVPWM stage converts the dq voltage commands into six gate pulses for the three-phase bridge. This gives the solar inverter a higher DC-bus utilization and a more favorable harmonic spectrum than simpler modulation methods. In my model, the SVPWM block was connected directly to the current-loop outputs after the inverse Park transformation.
I implemented the complete solar inverter model in Matlab 2017a and Simulink. The simulation model included the DC source, the three-phase bridge, the LC filter, the abc-to-\(\alpha\beta\) transformation, the \(\alpha\beta\)-to-dq transformation, the voltage outer loop, the current inner loop, the inverse transformations, and the SVPWM modulator. I used the simulation to check the steady-state waveform quality, the transient response, and the correctness of the coordinate transformations. The simulation parameters are summarized in Table 4.
| Parameter | Value | Unit |
|---|---|---|
| DC bus voltage reference | 50 | V |
| Off-grid phase voltage peak | 20 | V |
| Output frequency | 50 | Hz |
| DC-link capacitance | 3300 | \(\mu\)F |
| Filter inductance | 3 | mH |
| Filter capacitance | 10 | \(\mu\)F |
| Switching frequency | 10 | kHz |
After running the simulation, I observed that the AC-side voltage and current retained good sinusoidal shape and that their phases were aligned. I then performed a fast Fourier transform analysis on the AC current. The fundamental current amplitude was 3.268 A, and the total harmonic distortion was 0.39%. This is a good result for a solar inverter of this scale. I also examined the output voltage waveform and found that the phase voltage peak was approximately 20 V. The line-to-line voltage was calculated from:
$$U_{ab}=U_m \times \sqrt{3}=20\times \sqrt{3}\approx 34.64\ \text{V peak}$$
and the rms line voltage was:
$$U_{ab,\text{rms}}=\frac{20\times \sqrt{3}}{\sqrt{2}}\approx 24.49\ \text{V}$$
This calculation matched the expected value from the simulation model. The solar inverter output was therefore consistent with the design target and the modulation strategy.
| Quantity | Result | Unit |
|---|---|---|
| Fundamental current amplitude | 3.268 | A |
| Current THD | 0.39 | % |
| Phase voltage peak | 20 | V |
| Line voltage rms | 24.49 | V |
| Output frequency | 50 | Hz |
| Steady-state waveform quality | Good sinusoidal shape | — |
The next step in my workflow was to move from simulation to embedded implementation. I installed the C2000 hardware support package in Matlab 2017a, configured the DSP-related settings, and built a control model that corresponded to the simulation controller. The model used an ADC interrupt triggered by the PWM period. Inside the ADC interrupt, the controller sampled the output voltage and current, performed the abc-to-dq transformation, executed the voltage outer loop and current inner loop, computed the SVPWM duty cycles, and updated the PWM registers. I used a TMS320F28335 DSP because it has sufficient computational speed, floating-point capability, and peripheral integration for a three-phase solar inverter.
The generated code was compiled into a binary output file that could be loaded onto the DSP. I connected the simulation model to the target through the embedded code generation toolchain. This flow allowed me to test the same control algorithm that I had already simulated. The DSP then produced the SVPWM signals that drove the three-phase bridge. The modulation stage converted the DC input into a three-phase AC output at 50 Hz.
| Stage | Tool or Target | Function |
|---|---|---|
| Modeling and simulation | Matlab 2017a, Simulink | Power-stage and control-loop simulation |
| Code generation | Embedded Coder | Automatic C code generation for the controller |
| Hardware support | C2000 support package | DSP peripheral configuration |
| Target processor | TMS320F28335 | Real-time control and PWM generation |
| IDE and debugger | CCS 5.5 | Loading and debugging the generated binary |
I structured the embedded control model around a periodic interrupt. The PWM module generated a carrier, and the period interrupt triggered the ADC conversion. The ADC results were scaled and filtered, then used as feedback for the voltage and current loops. The controller calculated the dq voltage commands, applied inverse Park transformation, and produced the three-phase modulation references. The SVPWM block then computed the compare values for the PWM registers. Because the model was graphical, I could inspect the signal flow from the ADC input to the PWM output without manually tracing hundreds of lines of C code.
| Task | Description | Execution context |
|---|---|---|
| ADC sampling | Sample output voltage and current | PWM period interrupt |
| Clarke transformation | Convert abc to \(\alpha\beta\) | ADC interrupt |
| Park transformation | Convert \(\alpha\beta\) to dq | ADC interrupt |
| Voltage loop | Generate \(i_d^*,i_q^*\) | ADC interrupt |
| Current loop | Generate \(u_d,u_q\) | ADC interrupt |
| Inverse Park | Convert dq to \(\alpha\beta\) | ADC interrupt |
| SVPWM | Compute switching times and update PWM | ADC interrupt |
After the model-generated code was loaded into the DSP, I built a laboratory prototype of the solar inverter. The prototype consisted of a DC power supply, a three-phase bridge inverter, an LC filter, signal conditioning circuits, a DSP core board, and isolated gate-drive circuits. I used a programmable AC/DC source for the DC input, and I used precision measurement equipment to verify the output. The switching devices were IRF3710 MOSFETs, and the DSP was a TMS320F28335. The LC filter used a 3 mH inductor and a 10 \(\mu\)F capacitor per phase. The load resistance range was 4.6 to 13.8 \(\Omega\).
| Component | Choice | Purpose |
|---|---|---|
| DC source | Programmable AC/DC source | Stable DC input for the inverter |
| Power switches | IRF3710 MOSFETs | Three-phase bridge switching |
| Controller | TMS320F28335 DSP | Real-time control and modulation |
| Filter inductor | 3 mH | Current ripple attenuation |
| Filter capacitor | 10 \(\mu\)F | Voltage ripple attenuation |
| Gate drive | Isolated drivers | Safe MOSFET gate control |
| Load | 4.6 to 13.8 \(\Omega\) | AC load testing |
I followed a clear sequence during the experimental validation. First, I built and compiled the Matlab model to produce the DSP executable file. Second, I configured the debugger and connected it to the DSP target. Third, I loaded the generated binary into the DSP. Fourth, I powered the DC bus and observed the AC output through isolated oscilloscope probes and a precision power analyzer. I adjusted the load and checked the steady-state voltage, current, frequency, and harmonic content. The experimental output voltage was stable at approximately 24.5 V when the DC input was 50 V.
| Parameter | Value | Unit |
|---|---|---|
| Rated DC voltage | 50 | V |
| Rated output voltage | 25.00 | V |
| Rated output frequency | 50.00 | Hz |
| Filter capacitance | 10 | \(\mu\)F |
| Filter inductance | 3 | mH |
| Load resistance | 4.6 to 13.8 | \(\Omega\) |
| Measured output voltage | 24.5 | V |
| Output waveform | Stable three-phase sine wave | — |
The measured three-phase AC waveform was smooth and close to the ideal sine wave. The phase relationship between voltage and current remained correct, and the output frequency stayed at 50 Hz. I compared the simulation and experimental results and found that they were in good agreement. The small differences were consistent with practical effects such as device voltage drops, dead time, sensor scaling, and parasitic elements. These differences did not change the overall conclusion that the model-based solar inverter design was reliable.
| Quantity | Simulation | Experiment | Observation |
|---|---|---|---|
| Phase voltage peak | 20 V | Approximately 20 V | Close agreement |
| Line voltage rms | 24.49 V | 24.5 V | Close agreement |
| Output frequency | 50 Hz | 50 Hz | Stable |
| Current THD | 0.39% | Low distortion | Consistent trend |
| Waveform quality | Good sine | Good sine | Validated |
From a control perspective, the voltage-current double closed-loop structure gave me several advantages. The voltage outer loop provided output voltage regulation and generated the current references. The current inner loop limited the current during transients and improved the dynamic response. The dq transformation converted the sinusoidal references into DC quantities, which made the PI regulators much easier to tune. The instantaneous reactive power method gave me a direct way to set active and reactive power through the d and q axes. For unity power factor operation, I set the q-axis voltage reference to zero. This made the solar inverter deliver active power with a current that was essentially in phase with the voltage.
I also examined how the model-based approach affected development efficiency. In a traditional handwritten-code solar inverter project, the engineer must manually implement the coordinate transformations, PI regulators, SVPWM calculations, ADC scaling, and peripheral initialization. Each of these steps can introduce errors. In my model-based flow, the graphical model itself served as the specification. The simulation model and the embedded model shared the same controller structure. Generated code preserved the numerical behavior of the model more closely than a manual translation would. I still had to verify the hardware, but the verification effort shifted from low-level code debugging to higher-level system validation.
| Aspect | Traditional handwritten code | Model-based design I used |
|---|---|---|
| Algorithm representation | C code and flowcharts | Graphical Simulink model |
| Simulation link | Separate from embedded code | Controller model reused for code generation |
| Documentation | Manually written | Model can be inspected and documented |
| Verification | Often hardware-dependent | Early simulation and model checks |
| Development time | Longer for complex control | Shorter for validated control structures |
| Maintenance | Code-centric | Model-centric and easier to review |
The solar inverter I studied used a 50 V DC bus, and the output stage produced a three-phase AC voltage with a fundamental frequency of 50 Hz. The DC-link capacitor reduced the voltage ripple on the bus. The LC filter attenuated the switching harmonics. The SVPWM modulator generated the gate signals. The DSP sampled the feedback signals and executed the control loop. Each part of this chain matters for the final performance of the solar inverter. If the DC bus is noisy, the output voltage contains low-frequency distortion. If the filter is poorly designed, the switching ripple appears at the load. If the current loop is too slow, the solar inverter cannot limit current during load changes. If the modulation is not synchronized with sampling, the control loop can produce subharmonics or irregular switching.
I paid particular attention to the interaction between the sampling instant and the PWM cycle. In my implementation, the ADC interrupt was triggered by the PWM period. This ensured that the sampling was synchronized with the switching period. Synchronization reduces the effect of switching noise and helps the controller produce a clean modulation reference. I also used anti-aliasing and scaling in the signal conditioning path so that the DSP ADC received signals within its allowed range. The generated code then used these samples in the dq transformations and PI loops. The output of the current loop was transformed back to the stationary frame and passed to the SVPWM block.
The voltage and current equations in the dq frame include coupling terms proportional to \(\omega L\) and \(\omega C\). I compensated the \(\omega L i_q\) and \(\omega L i_d\) terms in the current loop. Without compensation, a step change in the d-axis current would disturb the q-axis current and vice versa. With compensation, each axis behaves more like an independent first-order system. This made the PI tuning process more systematic. I tuned the inner current loop for a faster response than the outer voltage loop. The outer loop then saw the inner loop as a nearly ideal current source. This separation of time scales is a standard and effective strategy for a three-phase solar inverter.
| Axis | Coupling term | Compensation sign | Effect |
|---|---|---|---|
| d-axis | \(\omega L i_q\) | Subtract from d-axis voltage | Reduces q-to-d coupling |
| q-axis | \(\omega L i_d\) | Add to q-axis voltage | Reduces d-to-q coupling |
| d-axis voltage | \(\omega C u_{R1q}\) | Included in plant model | Improves voltage-loop decoupling |
| q-axis voltage | \(\omega C u_{R1d}\) | Included in plant model | Improves voltage-loop decoupling |
I used the instantaneous reactive power theory to define the active and reactive power references. In the dq frame, the active power depends mainly on \(u_d i_d\), and the reactive power depends mainly on \(u_q i_d – u_d i_q\). When the q-axis voltage reference is zero, the reactive power reference can be set to zero by setting the q-axis current reference to zero. This gives unity power factor operation. When reactive power is required, the q-axis current reference can be adjusted. This flexibility is useful in a solar inverter that may need to support local loads with reactive components or to participate in voltage support functions.
The simulation model allowed me to test several operating conditions without damaging hardware. I tested balanced loads, load steps, and reference changes. I checked that the voltage loop recovered quickly after a load change and that the current loop did not saturate. I also checked the harmonic spectrum after the filter. The current THD of 0.39% showed that the combination of SVPWM and LC filtering was effective. The output voltage had a stable peak of about 20 V per phase, and the line voltage was about 24.49 V rms. These results gave me confidence to move to the prototype.
| Check | What I inspected | Result |
|---|---|---|
| Steady-state voltage | Phase and line voltage amplitude | Within target |
| Steady-state current | Fundamental amplitude and phase | In phase with voltage |
| Harmonic distortion | FFT of AC current | THD 0.39% |
| Transient response | Load-step recovery | Stable and fast |
| Coordinate transformations | abc, \(\alpha\beta\), dq consistency | Correct |
| PWM generation | SVPWM dwell times | Correct switching pattern |
On the hardware side, I used isolated oscilloscope channels to measure the three-phase output. The displayed waveform was smooth and balanced. I measured the output voltage with a high-precision multimeter and a power analyzer. The measured line voltage was approximately 24.5 V, which matched the simulation value of 24.49 V. The output frequency remained at 50 Hz. The solar inverter handled the allowed load range without losing regulation. These observations validated both the power-stage design and the control algorithm.
I found that the model-based design method reduced the effort required to implement the control algorithm. Instead of writing the coordinate transformation and SVPWM routines by hand, I configured them in Simulink and generated code. I still verified the generated code on the target, but the verification was faster because the model was already tested. The toolchain also made it easier to change controller gains, filter parameters, and modulation settings. I could modify the model, regenerate code, and reload the DSP. This rapid iteration is valuable in solar inverter development, where control requirements often change during testing.
| Parameter | Effect on performance | Design response |
|---|---|---|
| DC-link capacitance | Affects DC voltage ripple | Use large capacitor to reduce ripple |
| Filter inductance | Affects current ripple and dynamic response | Choose 3 mH for attenuation |
| Filter capacitance | Affects voltage ripple and reactive current | Choose 10 \(\mu\)F for output quality |
| Current-loop gain | Affects speed and stability | Tune for fast but damped response |
| Voltage-loop gain | Affects voltage regulation | Tune slower than current loop |
| SVPWM frequency | Affects harmonic spectrum and losses | Use 10 kHz for balance |
I also considered the role of the solar inverter in a larger energy system. An off-grid solar inverter must maintain a stable AC bus for local loads. In such a system, the DC input may come from a photovoltaic array, a battery, or a DC-DC converter stage. The inverter must regulate the AC voltage and frequency while respecting the available DC power. If the load demands more power than the source can provide, the DC bus voltage will fall unless the controller reduces the output or sheds load. In my design, the voltage outer loop provides the AC voltage regulation, and the current inner loop provides fast current limiting. This structure is suitable for a solar inverter that operates in an off-grid mode with a defined load range.
The double closed-loop controller also gives good rejection of disturbances. If the load current changes suddenly, the voltage loop detects a voltage error and adjusts the current reference. The current loop then changes the modulation command quickly. Because the current loop is faster than the voltage loop, the disturbance is handled before the output voltage deviates too much. This is important for a solar inverter that supplies nonlinear or switched loads. The LC filter further smoothes the voltage and current, reducing the high-frequency content that reaches the load.
| Disturbance | Detection | Controller action | Observed result |
|---|---|---|---|
| Load step | Voltage error in dq | Voltage loop changes current reference | Stable recovery |
| DC bus ripple | Output voltage variation | Voltage loop adjusts modulation | Reduced output ripple |
| Parameter mismatch | Current error | Current loop compensates | Robust operation |
| Switching noise | ADC samples | Synchronized sampling and filtering | Clean control signals |
I used the generated code as the final embedded controller. The code was compiled and loaded into the DSP. The DSP executed the control loop in real time and produced the SVPWM signals. The three-phase bridge switched the DC input and created the AC output. The LC filter removed the switching harmonics. The measurement equipment confirmed that the output was a three-phase sine wave with stable amplitude and frequency. The prototype therefore demonstrated the complete model-based solar inverter design flow from simulation to hardware.
The results also showed the value of the voltage-current double closed-loop control algorithm. The voltage loop maintained the output voltage, and the current loop provided fast current control. The instantaneous reactive power method gave a clear framework for active and reactive power control. The SVPWM modulator gave good DC-bus utilization and a low harmonic output. The model-based design method shortened the path from simulation to embedded code. In my assessment, the combination of these methods is well suited to a three-phase off-grid solar inverter.
| Metric | Target or observed value | Comment |
|---|---|---|
| DC input | 50 V | Regulated DC source |
| Output phase voltage peak | 20 V | Balanced three-phase output |
| Output line voltage rms | 24.49 V simulation, 24.5 V experiment | Close agreement |
| Output frequency | 50 Hz | Stable |
| Current THD | 0.39% | Good harmonic quality |
| Control structure | Voltage outer loop, current inner loop | Effective regulation |
| Modulation | SVPWM | Good DC-bus utilization |
| Development method | Model-based design | Reduced manual coding effort |
I concluded that the model-based solar inverter design was correct and practical. The simulation model produced a clean three-phase output with low harmonic distortion. The generated embedded code ran on the DSP and controlled the prototype. The experimental output matched the simulation closely. The voltage-current double closed-loop controller provided stable voltage regulation and fast current control. The instantaneous reactive power method provided a clear basis for power control. The SVPWM stage produced an effective switching pattern for the three-phase bridge. The overall workflow reduced the difficulty of writing embedded code and shortened the development cycle.
For future work, I would extend the model to include a photovoltaic source and a maximum power point tracking stage, because a solar inverter often operates with a varying DC input. I would also test nonlinear loads, unbalanced loads, and grid-connected transitions. I would add protection functions such as overcurrent, overvoltage, undervoltage, and islanding detection. I would examine the effect of dead time on output distortion and compensate for it in the modulation stage. I would also investigate the use of model checking and automatic documentation to improve the reliability of the solar inverter software. These extensions would make the model-based design even more valuable for practical solar inverter products.
| Area | Review question | Status |
|---|---|---|
| Topology | Is the three-phase bridge correctly connected? | Verified |
| Filter | Are L and C values consistent with ripple targets? | Verified |
| Transformations | Are Clarke and Park transforms correct? | Verified |
| Voltage loop | Does it regulate the AC voltage? | Verified |
| Current loop | Does it track references and limit current? | Verified |
| Modulation | Are SVPWM dwell times correct? | Verified |
| Code generation | Does generated code match the model? | Verified |
| Hardware test | Does the prototype meet the output target? | Verified |
The model-based approach did not eliminate the need for careful engineering. I still had to select the topology, choose the filter, tune the controllers, configure the DSP, and validate the hardware. However, it changed where I spent my effort. I spent less time on repetitive coding and more time on system behavior, control design, and validation. For a solar inverter with complex control and many operating modes, that shift is valuable. The model becomes a living document that describes the intended behavior, and the generated code becomes a consistent implementation of that model.
In my final assessment, the three-phase off-grid solar inverter based on Matlab model design met the design goals. It produced a stable three-phase AC output from a 50 V DC input. It maintained good waveform quality and low current THD. It used a voltage-current double closed-loop controller with instantaneous reactive power theory. It used SVPWM for efficient modulation. It was implemented with automatically generated code on a DSP. The simulation and experimental results agreed well. I therefore consider the model-based design method to be a reliable and efficient path for developing a three-phase solar inverter.