With the increasing integration of distributed energy resources like photovoltaics and wind power, energy storage inverters have become critical interfaces for grid connectivity. However, the inherent low inertia and insufficient damping of power electronic converters threaten grid stability. This study proposes a fuzzy adaptive control strategy combined with virtual synchronous generator (VSG) technology to enhance the dynamic response and stability of grid-connected energy storage inverters.
System Configuration and Modeling
The energy storage inverter employs a three-level neutral-point-clamped (NPC) topology to reduce harmonic distortion. The DC side integrates photovoltaic arrays and battery storage through Boost and bidirectional Buck/Boost converters. The VSG algorithm emulates synchronous generator dynamics using the rotor motion equation:
$$ J \frac{d\omega}{dt} = \frac{P_m}{\omega_0} – \frac{P_e}{\omega_0} – D(\omega – \omega_0) $$
$$ \frac{d\delta}{dt} = \omega $$
where \( J \) = virtual inertia, \( D \) = damping coefficient, \( \omega_0 \) = rated angular frequency, and \( \delta \) = power angle. The reactive power-voltage relationship is modeled as:
$$ E = E_N – K_q Q + \left( Q_{\text{ref}} – Q \right) \left( k_{p1} + \frac{k_{i1}}{s} \right) \frac{1}{1 + T_a s} $$
Fuzzy Adaptive VSG Control Design
The fuzzy controller adjusts \( J \) and \( D \) based on angular velocity deviation (\( \Delta\omega \)) and its rate of change (\( d\omega/dt \)). Constraints for energy storage characteristics include:
| Parameter | Constraint |
|---|---|
| Virtual Inertia (\( J \)) | \( J_{\text{min}} \leq J \leq J_{\text{max}} \) |
| Damping Coefficient (\( D \)) | \( D \geq D_{\text{min}} \) |
Fuzzy rules for inertia adjustment:
| \( \Delta\omega \) | \( d\omega/dt \) | \( \Delta J \) |
|---|---|---|
| Negative Large | Positive Large | Increase |
| Positive Small | Negative Small | Decrease |
Membership functions use triangular and S-shaped curves with normalized domains \([-1, 1]\). The output scaling factors ensure:
$$ J = J_0 + k_J \cdot \Delta J $$
$$ D = D_0 + k_D \cdot \Delta D $$
Simulation Results
A comparative analysis of fixed-parameter VSG, conventional adaptive VSG, and fuzzy adaptive VSG demonstrates superior performance in frequency stabilization:
| Control Method | Frequency Overshoot | THD (Voltage) |
|---|---|---|
| Fixed VSG | 0.21 Hz | 1.8% |
| Adaptive VSG | 0.14 Hz | 0.9% |
| Fuzzy Adaptive | 0.12 Hz | 0.02% |
The three-level NPC topology reduces harmonic distortion to:
$$ \text{THD}_V = 0.02\%,\ \text{THD}_I = 0.01\% $$
DC bus voltage maintains stability within 2.67% deviation under 50% SOC conditions, validating the energy storage inverter’s robustness.
Conclusion
The proposed fuzzy adaptive control strategy enhances grid-connected energy storage inverters by dynamically adjusting VSG parameters under storage constraints. This approach reduces frequency fluctuations by 42.8% compared to conventional methods while ensuring compliance with harmonic standards. Future work will explore multi-objective optimization for hybrid microgrid applications.