The core of intelligent control of active heat dissipation of solar inverter lies in realizing heat dissipation on demand through multi-source perception and intelligent algorithm, taking into account cooling efficiency, equipment life and system energy consumption. The following are three mainstream and efficient strategies:
I. Intelligent control of core hardware (executive layer)
This is the basis of active heat dissipation, which is realized by accurately controlling the heat dissipation components.
Intelligent fan/fan speed regulation (air cooling)
Multi-stage variable frequency speed regulation: abandon the traditional extensive mode of "one start and all start" and adopt PWM (pulse width modulation) technology to adjust the fan speed steplessly according to the real-time temperature and load rate. For example, when the temperature or load is low, reduce the speed to save energy and reduce noise; When the temperature is close to the threshold, run at full speed to cool down quickly.
N+1 redundant rotation: configure multiple cooling fans for high-power inverter, and realize polling through intelligent algorithm to avoid failure caused by long-term high-load operation of a single fan and prolong the overall life.
Intelligent pump control of liquid cooling system (liquid cooling)
Adaptive flow control: the intelligent water pump dynamically adjusts the coolant flow according to the temperature difference between the inlet and outlet of the radiator and the ambient temperature. When the load is high, the heat is exchanged quickly with large flow, and when the load is low, the energy is saved with small flow.
Combined cooling and heating: Intelligent switching between natural cooling and liquid cooling modes in combination with ambient wind speed. For example, in the windy low temperature environment, air cooling is given priority to reduce the operating power consumption of liquid-cooled pumps.
Local precision refrigeration (semiconductor/TEC)
TEC (thermoelectric cooler) is attached to the core heating points such as IGBT module and capacitor. Through the intelligent temperature control chip, point-to-point accurate refrigeration is carried out for local hot spots to avoid heat diffusion inside the equipment and solve the problem of local overheating.
Second, intelligent algorithm and control strategy (decision-making layer)
This is the "brain", which realizes predictive and synergistic regulation through algorithms.
Model predictive control (MPC)
Forward-looking control: combining weather forecast (illumination and temperature), historical operation data and current load, predict the temperature rise trend in the future. For example, before the high temperature comes at noon, precool or reduce part of the power in advance to avoid temperature overshoot.
Multi-objective optimization: with the goal of maximizing the net income (power generation income-cooling power consumption), the fan speed, liquid cooling flow rate, switching frequency and other parameters are cooperatively optimized to achieve global optimization.
AI adaptive algorithm
Fuzzy control and reinforcement learning: AI can learn and adjust the cooling strategy independently according to complex working conditions (such as extreme environment such as dust and high humidity) without accurate mathematical model. For example, when the heat dissipation efficiency decreases due to dust accumulation, the algorithm can automatically increase the fan speed to compensate.
Feed-forward-feedback compound control: combining prediction (feed-forward) and real-time deviation correction (feedback), it can quickly respond to sudden changes in the environment and ensure that the temperature is stable in a safe range.
Power derating and load management
Intelligent derating operation: When the temperature of the core device (such as IGBT) exceeds the safety threshold (such as 65℃), it will automatically reduce the output power by 5%-15%, reduce the calorific value from the source, ensure the safety of the equipment and avoid downtime.
Cluster load balancing: In a power station with multiple inverters connected in parallel, the load is distributed intelligently, so that the machine with higher temperature can reduce the output, and the machine with lower temperature can bear more load, thus achieving overall heat dissipation balancing.
Third, collaborative perception and system integration (perception layer)
Accurate regulation depends on comprehensive data.
Multi-dimensional sensor fusion
Integrated temperature (chassis, heat sink, IGBT junction temperature), humidity, wind speed, solar irradiance and other sensors. By analyzing multi-dimensional data, we can judge the cooling demand more accurately and avoid misjudgment.
Intelligent terminal temperature sensing: through RFID and other wireless technologies, the terminal temperature can be monitored in real time to prevent local overheating caused by poor contact, and the cooling strategy can be adjusted in conjunction.
Edge computing and cloud collaboration
Edge-side fast response: data processing and control decisions are completed on the local chip of the inverter to ensure the response speed.
Cloud big data optimization: upload operation data to the cloud, analyze long-term operation trend through AI model, perform predictive maintenance (such as reminding to clean the filter), and remotely optimize control parameters.
