The essence of temperature adaptive control is multi-objective closed-loop optimization of electric heating, heat dissipation and power generation, which has evolved from traditional fixed threshold passive over-temperature protection to prediction, intelligence, multi-objective coordination, device life optimization and cluster global thermal equilibrium. The following is divided into six directions:
1. Algorithm level: From feedback control to predictive adaptive intelligent control.
Model predictive control (MPC) has become the mainstream direction, and it is no longer necessary to wait for the temperature to exceed the limit. Based on the loss thermal resistance model, the junction temperature changes of IGBT and SiC are predicted in advance, and the heat dissipation, carrier frequency and output power are adjusted in advance to suppress temperature fluctuation (thermal cycle) and reduce the risk of thermal fatigue aging.
Artificial intelligence/data-driven adaptive algorithm rapidly develops LSTM, BP neural network, reinforcement learning for junction temperature prediction and nonlinear modeling of environmental loss temperature; The controller can self-adjust the temperature control parameters online, adapt to complex outdoor working conditions such as dust, high humidity, salt spray and large temperature difference between day and night, and get rid of the limitation of fixed PID parameters.
Multi-input compound adaptive (feedforward+feedback) introduces irradiance, ambient temperature, wind speed and output power as feedforward quantities to predict calorific value in advance; Combined with temperature closed-loop correction, the response speed of temperature control under sudden light change and load jump is greatly improved.
Second, control objectives: from single over-temperature prevention to multi-objective comprehensive optimization.
The traditional goal is only: the temperature does not exceed the upper limit, avoiding shutdown and load reduction. A new generation of adaptive strategy simultaneously weighs four objectives:
Upper limit of device safety (junction temperature, capacitor temperature)
Maximize power generation and minimize unnecessary power derating.
The cooling system itself consumes the least power (fans and pumps save energy).
Reduce the amplitude of temperature fluctuation and prolong the life of power devices (restraining thermal cycle is the current hot spot)
Derivative strategy: dynamic derating adaptation. Instead of using a single fixed cooling threshold, the load shedding curve is dynamically modified according to the aging degree of the device and environmental conditions, and the power is reduced slightly and smoothly at high temperature, rather than hard cutting protection.
3. Perception mode: from shell temperature measurement to junction temperature observation+multi-source information fusion.
It is difficult to measure the junction temperature of IGBT/SiC chip directly with the popularization of sensor-free on-line estimation technology. It is the basis of the next generation temperature control to establish a thermal network model using electrical parameters and estimate the junction temperature adaptively.
Multi-dimensional sensing fusion acquisition: environmental temperature and humidity, wind speed, irradiance, radiator temperature, power module temperature, bus capacitor temperature, winding temperature; Multi-measuring points cross-check, identify local hot spots, cooling duct blockage, fan failure and other anomalies, and adaptively correct the control strategy.
4. Electro-thermal cooperative self-adaptation: the operation parameters of the inverter are linked with the temperature.
The temperature control no longer only controls the fan water pump, but actively adjusts the electrical parameters of the inverter to control the temperature, and the electrothermal depth coupling is adaptive;
Adaptive adjustment of carrier frequency: appropriately reduce switching frequency at high temperature, reduce switching loss, and reduce heating from the source; Improving the waveform quality of frequency optimization at low temperature.
MPPT temperature adaptive compensation: dynamically correct MPPT voltage and tracking step with the temperature of inverter and photovoltaic module, giving consideration to power generation efficiency and thermal safety.
Adaptive temperature control of reactive power output: limit reactive power output at high temperature and reduce conduction loss.
This direction is the core trend of temperature control of high power density and SiC inverter.
V. Architecture evolution: single machine adaptation → edge cloud collaboration+thermal balance of power station cluster.
Edge local fast adaptation: The inverter DSP/MCU completes temperature closed loop and predictive control locally, with millisecond response to ensure real-time.
Long-term optimization of cloud big data: upload long-term thermal operation data, get seasonal adaptive temperature control parameters in this area through AI training, and issue optimization thresholds remotely; Realize predictive thermal maintenance (filter cleaning, fan aging warning).
Coordinated adaptive temperature of power station cluster: when multiple series/centralized inverters are connected in parallel, the power station floor will dispatch loads, and the inverter with high temperature will reduce the load moderately, and the inverter with sufficient temperature margin will issue additional power, so as to realize full-field thermal equilibrium and reduce the risk of over-temperature shutdown of a single inverter.
VI. Innovation Trend of Driving Temperature Control Strategy for Wide Band Gap Devices (SiC/GaN)
SiC devices have low switching loss and higher allowable operating temperature, but the nonlinear characteristics of switching loss temperature are stronger, the thermal transient is faster, and hot issues are prominent, which brings new requirements:
Adaptive prediction of high-speed junction temperature:
Suppression of temperature fluctuation in short time scale;
Point-to-point fine temperature control of local hot spots (such as TEC local refrigeration adaptation);
Intelligent tradeoff optimization strategy between cooling power consumption and high frequency loss.
Seven, the main research hotspots and challenges in the future
On-line junction temperature adaptive observer with low computation and engineering landing;
Multi-objective optimization adaptive temperature control considering the life of power generation, heat dissipation and power consumption devices;
Robust adaptive temperature control in extreme environment (desert high temperature, high altitude low temperature, coastal salt fog);
Combined temperature adaptive management of inverter batteries in optical storage integrated system.
