Fuzzy Logic-Based Power Management System for 10KW Hybrid Photovoltaic Generation System

In the field of renewable energy, photovoltaic (PV) generation systems have gained significant attention due to their clean and sustainable characteristics. However, the intermittent and unstable nature of solar power poses challenges for efficient power output management. For 10KW-level hybrid PV systems, a fuzzy logic-based power management system provides an intelligent solution that handles system uncertainties through rule-based reasoning.

The core of fuzzy logic control lies in its ability to process uncertainties using fuzzy rules. Compared to traditional PID control, it is better suited for nonlinear, time-varying systems. In hybrid PV systems, the fuzzy controller can comprehensively consider multiple variables such as light intensity, load demand, and battery status to dynamically adjust the coordination between power generation and energy storage. For instance, when sunlight is insufficient, the system can smoothly switch to energy storage units, while the fuzzy algorithm optimizes switching thresholds to avoid frequent start-stop cycles that cause equipment wear. The implementation typically involves fuzzification of input variables, rule-based inference, and defuzzification to generate precise control signals.

The system generally consists of three main modules: input variable fuzzification, rule base inference, and defuzzification output. Parameters such as PV array output power and battery SOC (State of Charge) are first converted into fuzzy sets. These are then processed through predefined "IF-THEN" rules (e.g., "IF sunlight is strong AND battery is not full, THEN increase charging current") to generate control commands, which are ultimately defuzzified into specific PWM signals or relay actions. From a coding perspective, this can be implemented using fuzzy logic toolbox functions like fis (Fuzzy Inference System) with appropriate membership functions and rule weighting.

Looking ahead, such systems can be integrated with MPPT (Maximum Power Point Tracking) technology to further improve energy efficiency, or connected via IoT for remote monitoring. As algorithms continue to optimize and hardware costs decrease, fuzzy logic control will find broader applications in distributed energy systems, potentially incorporating adaptive rule tuning and machine learning enhancements for improved performance.