A method for heat dissipation control of photovoltaic inverters

Abstract

This invention relates to a heat dissipation control method for photovoltaic inverters, comprising the following steps: Step 1: Deploying photovoltaic inverter status data acquisition equipment and forming a database from the acquired data; Step 2: Constructing a feature relationship model based on the data acquired in Step 1; Step 3: Analyzing the model constructed in Step 2 under different ambient temperatures and power levels, comparing the degree of improvement in inverter efficiency at different levels of heat dissipation power, and determining the current optimal heat dissipation level; Step 4: Implementing the optimal heat dissipation scheme, controlling the tiered heat dissipation to achieve inverter cooling; Step 5: Forming a dynamic closed-loop feedback for improving inverter operating performance, achieving the optimal heat dissipation effect with the lowest energy consumption; Step 6: Recording the correlation data between heat dissipation and inverter power within a set time period, and returning to Step 1. Compared with the prior art, this invention has advantages such as reducing the impact of temperature on inverter efficiency.

Description

Technical Field

[0001] The present invention relates to a photovoltaic inverter, and more particularly to a heat dissipation control method for a photovoltaic inverter. Background Art

[0002] As a core device of a power station, the life of a photovoltaic inverter directly relates to the normal working state of the entire power station, and the heat dissipation problem of the photovoltaic inverter is an important factor affecting the components. When the inverter works and generates heat, corresponding heat losses will occur, which will affect the normal operation of the internal components, thereby affecting the overall conversion efficiency of the inverter.

[0003] Currently, there are mainly two ways of heat dissipation for inverters, namely natural heat dissipation and air-cooled heat dissipation, that is, passive heat dissipation is carried out by changing the inverter housing, installing heat exchange plates, etc. Generally installed inside the inverter and connected to the internal components, corresponding heat dissipation operations are spontaneously carried out according to the temperature change of the components.

[0004] However, the inverter is generally hung outdoors. When it is exposed to direct sunlight and the temperature is relatively high, the inverter will heat up rapidly. And when the temperature is relatively high, the general power consumption load is large and the photovoltaic power generation situation is good, and the working temperature of the inverter will further increase. Therefore, the inverter temperature is affected by both internal and external temperatures. Forced air-cooled heat dissipation has a good effect and can effectively improve the efficiency of the inverter, but it requires additional heat dissipation power.

[0005] Currently, the heat dissipation control of the applied inverters mostly takes temperature as the only control target, lacking the analysis of the reasons for temperature rise. More importantly, it lacks the evaluation and feedback of the heat dissipation effect with the improvement of the inverter performance as the index and the targeted implementation of hierarchical heat dissipation.

[0006] After retrieval, Chinese Patent Publication No. CN212034000U discloses a photovoltaic inverter heat dissipation system and a photovoltaic inverter. The photovoltaic inverter heat dissipation system includes: an air-cooled cabin with a hollow heat dissipation channel; inner cabins arranged on both sides of the air-cooled cabin; an evaporator arranged in the inner cabins; a condenser arranged in the air-cooled cabin and connected to the evaporator; a refrigerant filled in the connected evaporator and condenser; a main heat dissipation fan arranged in the air-cooled cabin for providing air flow; a photovoltaic inverter including at least a first heating element and a second heating element, both arranged in the air-cooled cabin; wherein, the magnitude relationship of the working temperature t2 of the condenser, the working temperature t3 of the first heating element, and the working temperature t4 of the second heating element satisfies: t2 < t3 < t4; and the condenser, the first heating element, and the second heating element are arranged in sequence according to the flow direction of the air flow in the air-cooled cabin. However, the existing inverter heat dissipation technology has the following problems:

[0007] 1. Traditional photovoltaic inverter heat dissipation control does not fully consider the inverter's operating status and the reasons for the inverter's temperature rise. It is a relatively isolated temperature control and lacks intelligent and targeted heat dissipation control.

[0008] 2. There is no closed-loop feedback on the improvement of inverter operating performance due to heat dissipation, and there is a lack of dynamic heat dissipation control.

[0009] 3. Traditional heat dissipation control is generally based on on / off control, lacking effective and tiered heat dissipation control.

[0010] 4. Considering the unit cost, size, and energy consumption of the equipment, string inverters with medium power (below 100kW) mainly rely on natural cooling. However, in high-temperature environments, the lighting conditions are generally good. If external cooling can be added under precise control, the power generation efficiency can be greatly improved at a relatively low cost, creating incremental power generation. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a heat dissipation control method for photovoltaic inverters.

[0012] The objective of this invention can be achieved through the following technical solutions:

[0013] According to one aspect of the present invention, a heat dissipation control method for a photovoltaic inverter is provided, the method comprising the following steps:

[0014] Step 1: Deploy equipment to collect photovoltaic inverter status data and create a database from the collected data;

[0015] Step 2: Based on the data collected in Step 1, construct a feature relationship model;

[0016] Step 3: Using the model built in Step 2, analyze the different levels of heat dissipation power and inverter efficiency improvement under different ambient temperatures and power levels to determine the current optimal heat dissipation level;

[0017] Step 4: Implement the optimal heat dissipation scheme and control the tiered heat dissipation to cool the inverter;

[0018] Step 5: Form a dynamic closed-loop feedback to improve the inverter's operating performance and achieve the best heat dissipation effect with the lowest energy consumption;

[0019] Step 6: Record the correlation data between heat dissipation and inverter power within the set time period, and return to Step 1.

[0020] As a preferred technical solution, the data acquisition device in step 1 includes a temperature sensor, a power sensor, a voltage inductor, and a current sensor.

[0021] As a preferred technical solution, the photovoltaic inverter status data in step 1 includes the internal temperature of the inverter equipment, the external temperature, the AC-DC side power, the voltage, and the current.

[0022] As a preferred technical solution, in step 1, the database adopts cloud storage and cloud computing platform to integrate and preprocess the collected data, perform data cleaning, and build a unified standard data warehouse.

[0023] As a preferred technical solution, step 2 specifically involves: monitoring inverter temperature rise events, identifying the factors causing temperature changes in the inverter equipment by using data on the internal temperature, external temperature, and AC-DC power of the inverter, forming a characteristic relationship model, and predicting the temperature rise trend.

[0024] As a preferred technical solution, in step 3, hierarchical heat dissipation control is achieved by setting multiple different levels of heat dissipation power.

[0025] As a preferred technical solution, in step 4, the optimization process for finding the optimal heat dissipation solution adopts a table lookup method.

[0026] As a preferred technical solution, in step 4, the optimal heat dissipation solution is found online using an artificial intelligence algorithm.

[0027] As a preferred technical solution, in step 5, by setting a learning cycle, the control system can periodically learn the data feature library, and ultimately achieve intelligent, automated, hierarchical, and dynamic selection of the optimal heat dissipation level according to the actual situation.

[0028] As a preferred technical solution, in step 6, the increase in power generation after graded heat dissipation control is obtained through historical data analysis and calculation, and the percentage increase is used as the evaluation standard of the control system; and feedback is generated to the control system in a targeted manner according to the evaluation standard.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] 1) This invention utilizes intelligent heat dissipation and cooling to effectively tap the power generation potential of inverters at a high cost-performance ratio, especially in weather conditions with good irradiance, thereby reducing the impact of temperature on inverter efficiency.

[0031] 2) This invention achieves a significant improvement in power generation efficiency at a relatively low cost, creating incremental power generation. Attached Figure Description

[0032] Figure 1 This is a flowchart of the present invention;

[0033] Figure 2 This is a detailed flowchart of Embodiment 1 of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] The main innovative points of this invention include:

[0036] 1. Establish a central control system for the inverter array (preferably, establish a temperature central control system embedded in the photovoltaic power plant's centralized control system).

[0037] 2. The control system collects real-time signal data such as the internal temperature of the inverter equipment, the external temperature of the equipment, the AC-DC side power, voltage, and current.

[0038] 3. By accumulating historical data, analyze the causes of temperature changes, distinguish between internal and external factors, and establish a feature library.

[0039] 4. Establish a graded heat dissipation power control system, predict the effect of graded heat dissipation, and intelligently analyze and compare the cost and efficiency improvement of graded heat dissipation.

[0040] 5. Based on the analysis results of heat dissipation cost and efficiency improvement, control the staged heat dissipation to achieve the optimal heat dissipation effect with the lowest energy consumption.

[0041] like Figure 1 As shown, a method for intelligent sensing-based cooling and efficiency improvement control of photovoltaic inverters is presented, and a central control system for the inverter array is established. The control method includes the following steps:

[0042] Step 1: System deployment to collect data including inverter temperature, AC / DC side power, voltage, and current, forming a large database;

[0043] Step 2: Based on data-driven analysis, distinguish between internal and external factors that cause temperature changes in inverter equipment, and form a characteristic relationship model;

[0044] Step 3: Through model analysis, compare the improvement in inverter efficiency with different levels of heat dissipation power under different ambient temperatures and power levels to determine the current optimal heat dissipation level;

[0045] Step 4: Implement the optimal heat dissipation scheme and control the tiered heat dissipation to cool the inverter;

[0046] Step 5: Form a dynamic closed-loop feedback to improve the inverter's operating performance and achieve the best heat dissipation effect with the lowest energy consumption.

[0047] Step 6: Record the correlation data between heat dissipation and inverter power over a certain period of time, and return to Step 1.

[0048] Prioritizes step 1 by installing sensing hardware and integrating relevant data, including but not limited to: internal and external temperature of the inverter equipment, AC-DC side power, voltage, and current. A cloud storage and cloud computing platform is then established to integrate and preprocess the data, followed by data cleaning to build a standardized data warehouse.

[0049] In step 2, the inverter temperature rise event is monitored. By analyzing the internal temperature of the inverter, the external temperature, and the AC-DC power, the factors causing the temperature change of the inverter equipment are identified, and a characteristic relationship model is formed to predict the temperature rise trend.

[0050] In step 3, multiple different levels of heat dissipation power are set to achieve graded heat dissipation control.

[0051] In step 4, the optimization process can be performed by looking up a table;

[0052] In step 4, online optimization can be performed using artificial intelligence algorithms;

[0053] In step 5, a learning cycle is set so that the control system can periodically learn from the data feature library. Ultimately, this enables the intelligent, automated, hierarchical, and dynamic selection of the optimal heat dissipation level based on actual conditions.

[0054] In step 6, the increase in power generation after graded heat dissipation control is obtained through historical data analysis and calculation, and the percentage increase is used as the evaluation criterion for the control system. Based on the evaluation criterion, targeted feedback is generated to the control system.

[0055] Example 1

[0056] like Figure 2 As shown, this embodiment provides an inverter heat dissipation control method, mainly illustrating the heat dissipation control method for temperature rise caused by power increase inside the inverter, which mainly includes:

[0057] Step 101: Real-time acquisition of the inverter's internal temperature T in external ambient temperature T out Inverter DC power P DC With AC power P AC ;

[0058] Step 102: Monitor the internal temperature T of the inverter in real time. in Determine if the temperature exceeds T in >T set ;

[0059] Step 103, if the temperature exceeds the limit, adjust the internal temperature T. inTaking the derivative of the change with respect to time, we obtain the rate of increase of the internal temperature, dT / dt, which, combined with the current power (P) DC P AC The internal temperature trend is predicted by comparing and fitting the model with the power-temperature model already established in the background.

[0060] Step 104, simultaneously, the external temperature T out By taking the time derivative, the external temperature T is obtained. out Analyze the changing trends to determine the impact of external temperature changes on internal temperature changes;

[0061] Step 105: If the deviation between the measured temperature change and the predicted temperature change trend in step 103 is ΔTin <= threshold value ΔTset, then it is determined that the temperature rise is mainly due to the increase in internal power.

[0062] Step 106: Determine that the temperature rise is mainly caused by the increase in internal power and predict that there will be a large temperature rise trend. Estimate the heat dissipation cost and benefit by gradually lowering the heat dissipation level from the highest heat dissipation level.

[0063] Preferably, the heat dissipation effect refers to the model prediction of the increase in heat dissipation power and inverter efficiency with heat dissipation power as the abscissa, and the inverter efficiency curve and temperature function ρ=f(t) can be referenced from the equipment factory curve.

[0064] Furthermore, the central controller can record and learn from historical data, including the inverter efficiency curve and the temperature function ρ′=f′(t);

[0065] Preferably, the relationship between heat dissipation power and temperature reduction is obtained by referring to a table under different ambient temperatures;

[0066] Furthermore, based on big data learning, the heat dissipation power Pc = f(P AC P DC T in T out The relationship between the heat dissipation power Pc and the cooling relationship under different heat dissipation powers was calculated.

[0067] Step 107: Select the most economical heat dissipation power P. c : Satisfy the heat dissipation power P c With the power generation efficiency ΔP of photovoltaic inverters pv The ratio of improvement is the smallest;

[0068] Step 108: Implement heat dissipation based on the optimal Pc;

[0069] Step 109: Record heat dissipation power, temperature changes, and efficiency improvement, then proceed to step 101.

[0070] Example 2

[0071] The present invention provides another embodiment illustrating internal temperature rise caused primarily by external temperature rise.

[0072] Based on step 105 of the above embodiment, combined with the trend of external temperature Tout, the influence of external temperature change on internal temperature change is determined. If the deviation between the measured temperature change and the temperature change trend predicted based on power ΔTin is greater than the threshold value ΔTset, it is determined that the temperature rise is mainly due to the increase in external temperature, which is inconsistent with the power development trend of the inverter.

[0073] Step 201: Determine if the problem is caused by external temperature rise. Estimate the heat dissipation cost and benefit by gradually increasing the heat dissipation level from the lowest level. The calculation steps are similar to those in steps 106 to 107.

[0074] Step 202: Implement heat dissipation based on the optimal Pc'.

[0075] Step 203: Record heat dissipation power, temperature change, and efficiency improvement, then proceed to step 101.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A heat dissipation control method of a photovoltaic inverter, characterized by, The method comprises the following steps: Step 1: deploy a photovoltaic inverter state data collection device, and form a database from the collected data; Step 2: based on the data collected in step 1, build a feature relationship model; Step 3: analyze the comparison between different levels of heat dissipation power and the degree of inverter efficiency improvement under different environmental temperatures and power levels through the model built in step 2, and form the current optimal heat dissipation level; Step 4: implement the optimal heat dissipation scheme to control the graded heat dissipation to achieve inverter cooling; Step 5: form a dynamic closed-loop feedback of inverter performance improvement to achieve the optimal heat dissipation effect under the lowest energy consumption; Step 6: record the heat dissipation and inverter power correlation data within a set time, and return to step 1; The step 2 specifically comprises: monitoring the inverter temperature rise event, distinguishing the factors causing the temperature change of the inverter device through the data of the inverter internal temperature, external temperature, AC-DC side power, and forming a feature relationship model to predict the temperature rise trend; The temperature rise and heat dissipation control method caused by the rise of inverter internal power specifically comprises: Step 101, real-time acquisition of inverter internal temperature T in External environment temperature T out , inverter DC power P DC And AC power P AC ; Step 102, monitoring the temperature T inside the inverter in real time in , determining whether the temperature T is over temperature in > T set ; Step 103, like over temperature, the internal temperature T in Derivation of the change with respect to time, the internal temperature rising rate dT / dt, combined with the current power P DC And P AC Compare and fit with the power-temperature model established in the background, estimate the change trend of the internal temperature; Step 104, at the same time, the external temperature T out derivation, the external temperature T out change trend, determine the influence of the external temperature change on the internal temperature change; Step 105, if the temperature change and the temperature change trend predicted in step 103 deviate by ΔTin<= threshold value ΔTset, it is determined that the temperature rise is mainly caused by the rise of internal power; Step 106, judge the temperature rise mainly caused by the rise of internal power, and predict that there will be a large temperature rise trend, starting from the highest heat dissipation level and gradually downward to estimate the heat dissipation cost and benefit; Wherein the heat dissipation effect is a model prediction of heat dissipation power and inverter efficiency rise with heat dissipation power as the abscissa, inverter efficiency curve and temperature function ρ=f(t) reference device factory curve; The central controller learns from historical data records and inverter efficiency curve and temperature function ρ'=f'(t); According to big data learning, a relationship of heat dissipation power Pc=f(P AC , P DC , T in , T out ) is formed, and a cooling relationship under different heat dissipation powers Pc is calculated; Step 107, select the economic optimum heat dissipation power P c : meet the heat dissipation power P c With photovoltaic inverter power generation efficiency ΔP pv The ratio of the increase is minimum; Step 108, according to the optimal Pc, implement heat dissipation; Step 109, record the heat dissipation power, temperature change, and efficiency improvement, and turn to step 101; If the above step 105, combined with the external temperature Tout change trend, judges the influence of external temperature change on internal temperature change, the measured temperature change and the temperature change trend predicted according to the power deviate by ΔTin> threshold value ΔTset, it is determined that the temperature rise is mainly caused by the rise of external temperature; Step 201, judge the external temperature rise influence, start from the lowest heat dissipation level and gradually upward to estimate the heat dissipation cost and benefit, the calculation steps are similar to steps 106 to 107; Step 202, according to the optimal Pc', implement heat dissipation; Step 203, record the heat dissipation power, temperature change, and efficiency improvement, and turn to step 101.

2. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, The collection device in step 1 comprises temperature sensors, power sensors, voltage inductors, and current sensors.

3. The heat dissipation control method of a photovoltaic inverter according to claim 1 or 2, characterized in that, The photovoltaic inverter state data in step 1 comprises inverter device internal temperature, external temperature, AC-DC side power, voltage, and current.

4. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, The database in step 1 adopts a cloud storage and cloud computing platform to integrate and preprocess the collected data, perform data cleaning, and build a unified standard data warehouse.

5. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, In step 3, by setting multiple heat dissipation powers at different levels, graded heat dissipation control is achieved.

6. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, In the step 4, the optimization process of the optimal heat dissipation scheme adopts a table lookup method.

7. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, In the step 4, the optimization of the optimal heat dissipation scheme adopts an artificial intelligence algorithm for online optimization.

8. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, In the step 5, by setting a learning period, the control system can learn the data feature library regularly, and finally, the optimal heat dissipation level can be selected intelligently, automatically, hierarchically and dynamically according to the actual situation.

9. The heat dissipation control method of a photovoltaic inverter according to claim 1, wherein, In the step 6, the power generation increment obtained after the hierarchical heat dissipation control is analyzed and calculated, and the growth percentage is taken as the evaluation standard of the control system; and according to the evaluation standard, the control system is formed with feedback.

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