Abstract
With the rapid integration of renewable energy sources into power systems, energy storage inverters have become critical components for stabilizing grid operations. This paper presents a novel fuzzy adaptive control strategy combined with Virtual Synchronous Generator (VSG) technology to enhance the dynamic stability of energy storage inverters in photovoltaic (PV)-battery systems. By adopting a three-level Neutral-Point-Clamped (NPC) topology, the proposed system reduces the Total Harmonic Distortion (THD) of output voltage and current to 0.02% and 0.01%, respectively, significantly improving power quality. The fuzzy adaptive controller dynamically adjusts the virtual inertia and damping coefficients under constraints of energy storage characteristics and operational stability, ensuring robust frequency support and reduced power oscillations. Simulations on Matlab/Simulink validate the superiority of the proposed strategy over conventional methods, demonstrating its effectiveness in minimizing frequency overshoot and enhancing transient response.
1. Introduction
The transition toward carbon neutrality has accelerated the adoption of distributed energy resources (DERs), such as PV and wind power. However, the inherent low inertia and intermittent nature of these resources threaten grid stability. Energy storage inverters, acting as interfaces between DERs and the grid, must emulate synchronous generator behaviors to provide inertia and damping. Virtual Synchronous Generator (VSG) technology addresses this challenge by mimicking rotor dynamics through adjustable virtual inertia (JJ) and damping coefficients (DD). While existing studies focus on optimizing JJ or DD independently, they often neglect the coupling effects of energy storage constraints. This work integrates fuzzy logic with VSG control to achieve dual-parameter adaptation, ensuring compliance with energy storage limitations while enhancing system stability.
2. System Configuration and VSG Modeling
2.1 Three-Level NPC Energy Storage Inverter
The energy storage inverter employs a three-level NPC topology (Figure 1), which offers reduced switching losses and improved harmonic performance compared to traditional two-level inverters. The DC side integrates a PV array and battery storage through Boost and bidirectional Buck/Boost converters, respectively. Key parameters include:
| Parameter | Value |
|---|---|
| DC-link voltage | 750 V |
| Battery voltage | 400 V |
| Filter inductance | 3.2 mH |
| Filter capacitance | 20 µF |
| Rated angular frequency (ω0ω0) | 314 rad/s |
The NPC inverter ensures midpoint voltage balance and minimizes harmonic distortion, critical for grid compliance.
2.2 VSG Dynamic Model
The VSG algorithm replicates the swing equation of a synchronous generator:{dωdt=1J(Pm−Pe−D(ω−ω0))dδdt=ω−ω0{dtdω=J1(Pm−Pe−D(ω−ω0))dtdδ=ω−ω0
where PmPm and PePe represent mechanical and electromagnetic power, respectively. The second-order transfer function for active power regulation is derived as:G(s)=Pe(s)Pref(s)=MtJω0s2+Dω0+KwJω0s+MtJω0G(s)=Pref(s)Pe(s)=s2+Jω0Dω0+Kws+Jω0MtJω0Mt
Here, MtMt denotes the torque coefficient, while the natural frequency (ωnωn) and damping ratio (ξξ) are:ωn=MtJω0,ξ=D2JMt+Kw2JMtωn=Jω0Mt,ξ=2JMtD+2JMtKw
Adjusting JJ and DD directly impacts system stability: higher JJ increases overshoot, while larger DD prolongs response time.
3. Fuzzy Adaptive Control Design
3.1 Constraints on Virtual Inertia and Damping
The energy storage inverter must adhere to:
- Frequency Rate-of-Change (RoCoF) Limit:
J>ΔPmax2πω0⋅RoCoFmaxJ>2πω0⋅RoCoFmaxΔPmax
- Energy Storage Capacity:
J≤Jmax(to prevent battery overcharge/discharge)J≤Jmax(to prevent battery overcharge/discharge)
3.2 Fuzzy Logic Framework
The fuzzy controller uses angular speed deviation (E=ω−ω0E=ω−ω0) and its derivative (Ec=dE/dtEc=dE/dt) as inputs to adjust JJ and DD. Key components include:
- Fuzzification: Triangular/S-shaped membership functions for inputs (Figure 2).
- Inference Rules: 25 fuzzy rules for JJ and DD adjustment (Tables 1–2).
- Defuzzification: Center-of-gravity method to compute outputs (ΔJΔJ, ΔDΔD).
Table 1: Fuzzy Rules for Virtual Inertia (JJ)
| E\EcE\Ec | NL | NS | ZO | PS | PL |
|---|---|---|---|---|---|
| NL | PL | PL | PS | ZO | NS |
| NS | PL | PS | ZO | NS | NS |
| ZO | NS | PS | ZO | PS | NS |
| PS | NS | NS | ZO | PS | PL |
| PL | NS | ZO | PS | PL | PL |
Table 2: Fuzzy Rules for Damping Coefficient (DD)
| E\EcE\Ec | NL | NS | ZO | PS | PL |
|---|---|---|---|---|---|
| NL | PL | PS | ZO | PS | NS |
| NS | PS | PL | ZO | PS | NS |
| ZO | PS | PL | ZO | PS | NS |
| PS | PS | ZO | PS | PS | PL |
| PL | PS | ZO | PS | PS | PL |
The final adaptive parameters are:J=J0+kJ⋅ΔJ,D=D0+kD⋅ΔDJ=J0+kJ⋅ΔJ,D=D0+kD⋅ΔD
where kJ=0.05kJ=0.05 and kD=0.3kD=0.3 ensure parameter boundaries.
4. Simulation and Results
4.1 Comparative Analysis
Three control strategies—fixed-parameter VSG, conventional adaptive VSG, and fuzzy adaptive VSG—were tested under a 2-second load step change (20 kW → 10 kW → 20 kW). Key metrics include:
| Metric | Fixed VSG | Conventional Adaptive | Fuzzy Adaptive |
|---|---|---|---|
| Frequency overshoot (Hz) | 0.21 | 0.14 | 0.12 |
| Power settling time (s) | 0.8 | 0.6 | 0.4 |
| THD (Voltage/Current) | 0.5%/0.3% | 0.1%/0.08% | 0.02%/0.01% |
The fuzzy adaptive controller reduced frequency fluctuations by 43% compared to fixed-parameter VSG, with nearly zero power overshoot (Figure 3).
4.2 Energy Storage Performance
The PV-battery system maintained a stable DC-link voltage (750 V ± 2.67%) under MPPT operation (Figure 4). The energy storage inverter seamlessly balanced power between PV generation and battery output, validating its robustness.
5. Conclusion
This study proposes a fuzzy adaptive control strategy for energy storage inverters, integrating VSG dynamics with real-time parameter adaptation. The three-level NPC topology minimizes harmonic distortion, while the fuzzy controller optimizes virtual inertia and damping under energy storage constraints. Simulations confirm the strategy’s superiority in enhancing frequency stability, reducing THD, and improving transient response. Future work will explore hardware implementation and multi-objective optimization for large-scale grids.