1、 The core objective of the control strategy for solar inverters
Before choosing a control strategy, it is necessary to clarify its core role, which is the basis for judging the applicability of the strategy:
Efficient energy conversion: Maximum Power Point Tracking (MPPT) of the photovoltaic array to minimize energy loss;
Output waveform quality: Ensure that the voltage, frequency, and harmonic distortion (THD) of the output AC power meet the grid standards (such as grid connected inverters requiring THD ≤ 5%);
Grid synchronization capability: Grid connected inverters need to be accurately synchronized with grid voltage/frequency to avoid islanding effects (with the need for anti islanding protection);
Stability and robustness: Maintain stable system operation during drastic changes in lighting/temperature, load fluctuations, or grid disturbances (such as voltage dips);
Dynamic response speed: Quickly respond to photovoltaic power fluctuations or load changes, reducing energy loss during the transition process.
2、 Common control strategies and applicable scenarios for solar inverters
The current mainstream control strategies can be divided into three categories: traditional linear control, nonlinear control, and intelligent control. The principles, advantages, disadvantages, and applicable scenarios of each strategy are as follows:
1. Traditional linear control: simple structure, low cost, suitable for basic scenarios
Represented by PID control (proportional integral derivative control), it is currently one of the most widely used strategies in small and medium-sized inverters.
Principle: By using the proportional term (P) to quickly respond to errors, the integral term (I) to eliminate steady-state errors, and the differential term (D) to suppress overshoot, closed-loop regulation of output voltage/current is achieved.
Advantages: Simple algorithm, easy implementation (without complex hardware support), low cost, mature debugging, suitable for simple systems with good linearity.
Disadvantage: Relying on accurate system models, control accuracy and stability may decrease in common "nonlinear characteristics" of photovoltaic systems (such as changes in photovoltaic output characteristics caused by sudden changes in lighting/temperature), "parameter time-varying" (such as component aging), or "strong interference" (such as grid fluctuations) scenarios.
Applicable scenarios:
Small household grid connected inverters (power ≤ 10kW) and cost sensitive off grid small systems;
Areas with gentle changes in light/temperature (such as low latitude stable climate zones);
Scenarios with stable loads and minimal power grid disturbances.
2. Nonlinear control: Strong robustness, suitable for complex dynamic scenarios
Regarding the nonlinearity and uncertainty of photovoltaic systems, nonlinear control strategies can provide better stability and anti-interference capabilities. Common types include sliding mode control (SMC), model predictive control (MPC), adaptive control, etc.
(1) Sliding Mode Control (SMC)
Principle: By designing a "sliding mode", the system state is forced to move along a predetermined trajectory, even if parameters change or disturbances exist, it can still remain stable.
Advantages: Strong robustness (insensitive to parameter changes and external disturbances), fast dynamic response, suitable for scenarios such as sudden changes in lighting/temperature and load fluctuations in photovoltaic systems.
Disadvantages: There is a "chattering phenomenon" (high-frequency fluctuations in control quantities), which may lead to increased losses in switching devices; Accurate design of the sliding surface is required, making debugging difficult.
Applicable scenarios:
Off grid system (with drastic load changes, such as sudden access to high-power devices);
High latitude or climate changing areas (with large fluctuations in light/temperature);
Scenarios that require high dynamic response speed, such as microgrid grid integration.
(2) Model Predictive Control (MPC)
Principle: Based on a system mathematical model to predict future states, the optimal control variable can be selected through rolling optimization to directly handle multiple constraints (such as current limiting and voltage range).
Advantages: It can balance energy conversion efficiency and waveform quality, with extremely fast dynamic response (millisecond level), and can achieve multi-objective optimization (such as simultaneously controlling current harmonics and MPPT efficiency).
Disadvantages: High dependence on model accuracy, huge computational complexity (requiring real-time optimization problems), high performance processor support, and high cost.
Applicable scenarios:
Large centralized inverters (power ≥ 100kW) or string inverters (requiring high-precision control);
Scenarios with strict grid connection requirements (such as mandatory standards for harmonic and reactive power compensation in the power grid);
Industrial and commercial photovoltaic power plants that require extremely high efficiency and dynamic performance.
(3) Adaptive control
Principle: By identifying changes in system parameters online (such as photovoltaic panel aging and temperature drift), the control parameters are automatically adjusted to maintain the optimal state of the system at all times.
Advantages: No need to preset fixed parameters, can adapt to environmental changes, and has strong long-term stability.
Disadvantages: The parameter recognition process may introduce delays, and the dynamic response may be slightly slower than SMC or MPC.
Applicable scenarios:
Long term operating photovoltaic systems (such as large power plants with a lifecycle of over 25 years);
There are various types of photovoltaic modules (such as mixing modules of different brands/powers) or scenarios with aging differences;
The situation where the load characteristics are unknown or dynamically changing in an off grid system.
3. Intelligent control: No need for precise models, suitable for complex nonlinear scenarios
Based on artificial intelligence algorithms such as fuzzy control, neural networks, and genetic algorithms, there is no need to rely on precise mathematical models of the system, making it suitable for complex nonlinear problems that are difficult to model in photovoltaic systems.
(1) Fuzzy control
Principle: Imitate human decision-making logic and process uncertain information through fuzzy rules (such as "increasing output power under strong lighting").
Advantages: Low requirement for model accuracy, strong anti-interference ability, suitable for scenarios where accurate models cannot be established (such as photovoltaic energy storage hybrid systems).
Disadvantages: Rule design relies on experience, which may result in insufficient steady-state accuracy; It needs to be optimized by combining PID and other strategies.
Applicable scenarios:
Off grid small-scale systems (such as photovoltaic water pumps, remote area power supply);
Scenarios with complex load characteristics, such as mixed AC/DC loads.
(2) Neural Network Control
Principle: By training neural networks to fit system characteristics, adaptive control or MPPT optimization can be achieved (such as MPPT algorithms based on neural networks).
Advantages: It can handle highly nonlinear relationships and has strong generalization ability (suitable for different types of photovoltaic modules).
Disadvantages: The training process is complex and requires a large amount of sample data; Poor real-time performance (calculation delay) and high cost.
Applicable scenarios:
Distributed photovoltaic system (with a large number of components, scattered layout, and significant differences in characteristics);
Smart microgrid combined with energy storage (requiring coordination of photovoltaic, energy storage, and load variables).
3、 Summary
Simple scenarios (small household, stable environment): prioritize PID control to balance cost and basic performance;
Complex dynamic scenarios (off grid, variable climate): Choose sliding mode control or adaptive control to enhance robustness;
High performance demand scenarios (large power plants, strict grid connection): Choose Model Predictive Control (MPC) to balance efficiency and accuracy;
Highly nonlinear scenarios (hybrid systems, distributed photovoltaics): Attempt intelligent control (fuzzy+PID, neural networks) to flexibly respond to complex characteristics.
Ultimately, the optimal strategy is often a "hybrid strategy" (such as PID+fuzzy control, MPC+sliding mode control), which combines the advantages of different methods to find a balance between performance, cost, and reliability.
