Intelligent and complete implementation scheme of temperature adaptive control strategy for solar inverter
Intelligent core logic: multi-source perception → accurate modeling and prediction of thermal state →AI adaptive decision → multi-dimensional collaborative execution → digital twinning+cloud continuous self-optimization, completely get rid of the traditional fixed threshold temperature control, and realize the dynamic balance among device safety, maximum power generation and minimum cooling power consumption.
First, the first layer: global multi-parameter intelligent perception (data base, adaptive input)
1. Multi-node distributed temperature acquisition hardware.
Covering four categories of temperature measuring points: external environment, power devices, magnetic components and cooling system:
Environmental layer: air inlet temperature and humidity, sunshine irradiation and wind speed;
Power core: IGBT/SiC module shell temperature NTC, and the junction temperature of the chip is estimated by thermal resistance model (which can not be measured directly);
Passive heating parts: DC bus capacitance, inductor/transformer winding temperature;
Cooling system: temperature difference between hot and cold ends of radiator, water temperature at inlet and outlet of liquid cooling, and dust sensor in air duct.
2. Intelligent signal preprocessing (eliminating interference and ensuring adaptive accuracy)
ADC high-frequency sampling (50~100Hz)+ moving average filtering, amplitude limiting and de-hopping to suppress IGBT electromagnetic interference;
Multi-sensor cross-check: the sensor fault is automatically determined by single point temperature anomaly, and the neighborhood data interpolation compensation is adopted;
Time alignment of data: temperature, DC voltage and current, grid-connected power, irradiation synchronization timestamp, and construction of electrothermal coupling data set.
Second, the second layer: electrothermal coupling intelligent modeling and temperature prediction (predictive adaptive, different from traditional lag control)
Traditional temperature control is passive adjustment after the temperature exceeds the standard; Intelligently predict the heat load in advance and pre-regulate.
1. Off-line high-precision electrothermal model
2. Lightweight AI prediction model (edge deployment)
LSTM/attention time series network: input historical temperature, power, irradiation and ambient temperature, and predict the peak junction temperature 10~30min in advance, with an error of < 2℃; Sudden working conditions such as cloud cover and midday exposure are predicted in advance;
Digital twin thermal field mapping: local MCU reconstructs the three-dimensional temperature field inside the inverter in real time and identifies local hot spots, instead of just measuring temperature at a single point;
Input fused meteorological edge data (sunshine and temperature time series) to realize long-term thermal trend prediction across time periods.
3. Intelligent classification and identification of working conditions
Fuzzy logic/lightweight classification neural network automatically divides the scene into six working conditions: low temperature start-up, normal temperature steady state, high temperature full development, sudden irradiation, high altitude and dust accumulation, and switches the exclusive temperature control parameter set.
Third, the third layer: multi-dimensional intelligent adaptive decision-making algorithm (core brain)
Taking the multi-objective optimization of junction temperature safety upper limit, power generation income and heat dissipation energy consumption as the objective function, four kinds of collaborative control instructions are output.
1. Adaptive control of cooling system (active thermal management)
Abandoning fixed start-stop threshold and realizing stepless on-demand heat dissipation;
Multi-stage adaptive speed regulation of air-cooled PWM fan
Low temperature and low load: raise the fan start threshold, naturally dissipate heat at low speed/stop, and reduce auxiliary consumption;
High temperature near junction temperature threshold: linear lifting speed; Multi-fan N+1 rotation, balanced loss, extended life;
Determination of dust accumulation: at the same temperature, the fan speed continues to be high, which automatically triggers the fan to reverse and self-clean.
Intelligent pump control of liquid cooling system
According to the temperature difference of radiator, the coolant flow is dynamically adjusted, which saves energy with low load and small flow, and has strong heat exchange with large peak flow; Automatic switching between hot and cold modes (air cooling is preferred for windy low temperature).
Local hot spot TEC semiconductor refrigeration adaptation: point-to-point temperature control for capacitor and SiC hot spots.
2. Power loop electrothermal cooperative adaptation (reducing heat source and controlling temperature from source)
Temperature adaptive MPPT
High temperature: reduce MPPT disturbance step to avoid voltage fluctuation and increase switching loss; Increasing the step size at low temperature improves the tracking speed; According to the component temperature coefficient, the MPP voltage reference is dynamically modified to offset the high temperature voltage drop loss.
PWM switching frequency adaptation
High junction temperature: reduce switching frequency and switching loss; Low temperature and light load: increase the frequency and reduce the volume loss of filter inductance; Real-time balance between switching loss and total heating caused by magnetic loss.
Flexible step-by-step load shedding (instead of traditional hard shutdown)
Three-level adaptive protection (no sudden power failure to maximize power generation):
First-class early warning: the junction temperature is close to the threshold, the active power is slightly reduced, and the reactive power is appropriately added to dissipate heat;
Secondary thermal limit: linearly reduce the power to 70%~80% of the rating, and synchronously radiate at full load;
Three-level criticality: limit power by 50%, only maintain safe operation, and avoid thermal runaway and burning devices.
3. Self-tuning of control parameters (adaptive PID/fuzzy/reinforcement learning)
Fuzzy adaptive PID
Taking junction temperature deviation and temperature rise rate as inputs, the PID proportional, integral and differential coefficients are corrected in real time on line; High-temperature large deviation amplification P quickly cools down, and the steady state slightly suppresses the shock;
On-line optimization of DQN for deep reinforcement learning
In the long-term operation, the comprehensive benefit of "power generation-cooling power consumption-device temperature rise" is taken as the reward function, and the optimal fan speed, load shedding threshold and switching frequency combination are continuously iterated to adapt to long-term working conditions in different regions and seasons;
Fractional PID improves the response speed of lagging temperature control and greatly reduces the temperature overshoot.
4. Self-adaptive strategy for low temperature scene
Environment < 0℃: start the preheating loop of the whole machine, and raise the threshold of fan start to avoid the aging of the device accelerated by cold air blowing; Low temperature correction capacitor voltage protection threshold to prevent overvoltage caused by low temperature capacity attenuation.
Fourth, the fourth layer: multi-actuator collaborative closed-loop control (intelligent hardware execution layer)
Power loop: DC/DC and DC/AC driver chips dynamically adjust PWM duty ratio and carrier frequency;
Heat dissipation execution: brushless fan PWM driver, variable frequency water pump and TEC refrigeration drive closed-loop speed regulation;
Grid-connected cooperation: when the temperature is too high, the reactive power is adjusted adaptively, and the reactive current is used to assist heat dissipation without sacrificing too much active power generation;
Self-adaptation of protection logic: dynamically correct the threshold values of over-temperature, over-current and over-voltage protection with the ambient temperature to avoid low-temperature misoperation and high-temperature protection lag.
Fifth, the fifth layer: cloud+edge collaborative continuous self-evolution (long-term intelligent iteration)
Edge local online learning
The local MCU of inverter collects all-day temperature-power-heat dissipation data, updates the weight of local lightweight neural network regularly, and adapts to the sunshine, ventilation and dust accumulation characteristics of the station itself;
Cloud federated learning
Multiple inverters upload desensitization operation data, train the global temperature control model in the cloud, and send the optimization parameters to the local area, so that different climate zones (desert, cold and coastal) form exclusive adaptive strategy libraries;
Thermal health prediction and maintenance early warning
Based on the long-term temperature cycle data, the aging life of IGBT and capacitor is estimated. Push the operation and maintenance reminder in advance under continuous high temperature conditions to avoid overheating and early failure;
Digital twin remote visualization: the platform displays the temperature field of the whole machine in real time, self-adaptive adjustment records, high temperature load shedding time statistics, and supports remote parameter optimization and debugging.
Six, the traditional temperature control vs intelligent temperature adaptive core differences
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