Dynamic heat dissipation adjustment method and system for wind direction predictor of wind energy energy storage cabinet

Abstract

The present invention discloses a dynamic heat dissipation adjustment method and system for a wind direction predictor of a wind energy energy storage cabinet, belonging to the technical field of environmental protection energy equipment. The actual working temperature of the wind direction predictor and relevant environmental parameter data are obtained in real time and saved to a constructed parameter database. By analyzing the data in the parameter database, a temperature model of the wind direction predictor is constructed. The environmental parameter data in the parameter database is read, short-term prediction of the environmental parameters is carried out, an environmental prediction model is constructed, and an environmental prediction sequence is generated. The environmental prediction sequence is input into the temperature model to calculate the predicted working temperature of the wind direction predictor and formulate a heat dissipation adjustment table. The actual working temperature of the wind direction predictor is continuously collected, the deviation between the actual working temperature and the predicted working temperature is calculated, and the heat dissipation adjustment table is updated, reducing unnecessary energy consumption.

Description

Technical Field

[0001] The present invention relates to a dynamic heat dissipation adjustment method and system for a wind energy storage cabinet wind direction predictor, and belongs to the technical field of environmental protection energy equipment. Background Art

[0002] In wind energy storage, the heat dissipation structures of various devices quickly discharge the heat inside the devices, so as to keep the devices operating within the normal working temperature range, reduce the increase in energy consumption caused by overheating, thereby reducing carbon emissions. Through heat dissipation, frequent repairs and replacements caused by overheating of the devices can be avoided, and the goal of energy conservation and environmental protection can be achieved.

[0003] The existing Chinese patent with the publication number CN117350159A discloses a generator heat dissipation performance early warning method, device, equipment and storage medium. By obtaining the generator heat dissipation data within a historical time period, then preprocessing the generator heat dissipation data to obtain the processed heat dissipation data, then training a preset temperature rise model according to the processed heat dissipation data to obtain a target temperature rise model, and then determining a heat dissipation performance coefficient based on the target temperature rise model, and performing early warning on the generator heat dissipation performance according to the heat dissipation performance coefficient. This invention effectively trains a preset temperature rise model according to the processed heat dissipation data, and the obtained target temperature rise model can estimate the heat dissipation performance of the generator. Then, a heat dissipation performance coefficient is determined based on the target temperature rise model, and early warning of the generator heat dissipation performance is carried out in advance according to the heat dissipation performance coefficient.

[0004] Although the prior art processes the generator heat dissipation system in advance to avoid power generation losses caused by temporary shutdown of the generator, it does not consider the actual temperature value of the equipment during the power generation process of the generator, especially the coping strategy when there is a large difference between the predicted value and the actual value. Therefore, the present application provides a dynamic heat dissipation adjustment method and system for a wind energy storage cabinet wind direction predictor, which predicts future heat dissipation requirements, formulates a heat dissipation adjustment strategy, and dynamically adjusts and optimizes the heat dissipation adjustment strategy according to the actual heat generation situation of the equipment, so as to improve the heat dissipation effect and save energy resources. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a dynamic heat dissipation adjustment method and system for a wind energy storage cabinet wind direction predictor. By predicting the working temperature of the wind direction predictor, constructing a heat dissipation adjustment table, comparing the predicted working temperature with the safe temperature range of the wind direction predictor, calculating the expected target flow rate of the coolant in the heat dissipation device, controlling the flow rate of the coolant when the time reaches the predicted point, and real-time monitoring the actual working temperature, and making real-time adjustments to the current heat dissipation adjustment plan based on the actual working temperature.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A dynamic heat dissipation regulation method for a wind direction predictor of a wind energy energy storage cabinet, comprising:

[0008] Step S1: Using a high-precision temperature sensor configured in the wind direction predictor, obtain the actual working temperature of the wind direction predictor. At the same time, collect relevant environmental parameter data according to an external environment detection device and save it to a constructed parameter database. During the data collection process, set a unified initial collection time, sampling interval and sampling points , and record the data collection time in real time, so that the data has a unified time stamp to achieve time synchronization of the data;

[0009] Step S2: Based on the data in the parameter database, analyze the relationship between the actual working temperature of the wind direction predictor and the environmental parameters, use the regression analysis method to construct a temperature model of the wind direction predictor, and train the temperature model to predict the working temperature of the wind direction predictor under given environmental parameters;

[0010] Step S3: Read the environmental parameter data in the parameter database, make a short-term prediction of the environmental parameters by analyzing the trend of historical environmental parameters, construct an environmental prediction model, and generate an environmental prediction sequence;

[0011] Step S4: Input the environmental prediction sequence into the trained temperature model, calculate the predicted working temperature of the wind direction predictor, construct a heat dissipation regulation table based on the predicted working temperature, and compare the predicted working temperature with the safe temperature range of the wind direction predictor to formulate a heat dissipation regulation strategy, so that the wind direction predictor can maintain an appropriate temperature during operation;

[0012] Step S5: Continuously collect the actual working temperature of the wind direction predictor, compare the actual working temperature with the corresponding predicted working temperature, calculate the deviation, and make local adjustments and updates to the heat dissipation regulation table according to the size of the deviation.

[0013] Specifically, the step S2 includes:

[0014] S2.1: Use the regression analysis method to construct a temperature model, define the working temperature of the wind direction predictor as the dependent variable, and the environmental parameters as the independent variables; the objective function expression of the temperature model is as follows:

[0015]

[0016] In the formula, is the dependent variable of the model, is the number of independent variables of the model, is the th environmental parameter in the independent variables, is the parameter regression coefficient;

[0017] S2.2: Construct the target parameter function ;

[0018] S2.3: Initialize the parameter group , and set the initial number of iterations ;

[0019] S2.4: Solve the partial derivative of the parameters in the target parameter function , and calculate the gradient value of the parameter ; ;

[0020] S2.5: Update the value of the parameter based on the gradient value, and the expression is as follows:

[0021]

[0022] In the formula, is the learning rate.

[0023] Specifically, the step S2 further includes:

[0024] S2.6: Set the iteration interval threshold for learning rate update to , and calculate the remainder between the current number of iterations and the iteration interval threshold ; ;

[0025] S2.7: Determine whether to perform learning rate decay calculation based on the remainder ; if , when the current number of iterations reaches the predetermined iteration interval threshold, update the learning rate to ; if , when the current number of iterations does not reach the predetermined iteration interval threshold, do not update the learning rate; where is the learning decay factor;

[0026] S2.8: Update the number of iterations to , obtain the value of the target parameter function before parameter update and the value after parameter update , and calculate the change rate of the target parameter function ;

[0027] S2.9: Set the convergence threshold of the target parameter function to , the change rate threshold to , and determine whether the target parameter function converges; if and , the target parameter function converges and enters S2.10; if or , the target parameter function does not converge, and returns to S2.4 to continue updating the parameters;

[0028] S2.10: Obtain the converged parameter group , and the temperature model training is completed.

[0029] Specifically, the step S3 includes:

[0030] S3.1: Construct an environmental parameter matrix ; where is the th environmental parameter sequence, is the number of collected samples;

[0031] S3.2: Use a recurrent neural network to construct an environmental prediction model, and the objective function expression is as follows:

[0032]

[0033] In the formula, is the output matrix of the environmental prediction model, is the activation function, is the connection weight matrix between the input layer and the hidden layer, is the input matrix of the environmental prediction model, is the bias matrix;

[0034] S3.3: Based on the environmental parameter matrix , define the input matrix and the theoretical output matrix of the environmental prediction model; where is the sample size of the input matrix;

[0035] S3.4: Use the ant colony optimization algorithm to solve and optimize the connection weight matrix and the bias matrix in the environmental prediction model;

[0036] S3.5: Real-time collect the environmental sequence at time , and output the environmental prediction sequence at time through the environmental prediction model.

[0037] Specifically, the step S4 includes:

[0038] S4.1: Input the environmental prediction sequence into the temperature model to calculate the predicted working temperature of the wind direction predictor and save the predicted working temperature into the constructed heat dissipation regulation table;

[0039] S4.2: Set the safety temperature range of the wind direction predictor to ;

[0040] S4.3: Determine whether the predicted working temperature is within the safety temperature range; if , the predicted working temperature is within the safety temperature range; if or , the predicted working temperature is not within the safety temperature range, and a warning mark is made for the predicted working temperature at the moment in the heat dissipation regulation table.

[0041] Specifically, step S4 further includes:

[0042] S4.4: Obtain the predicted working temperature with a warning mark in the heat dissipation regulation table , and calculate the predicted target flow rate of the coolant at the moment ;

[0043] S4.5: Save the predicted target flow rate of the coolant at the moment into the heat dissipation regulation table.

[0044] Specifically, step S5 includes:

[0045] S5.1: When the time reaches the moment, adjust the coolant pump so that the flow rate of the coolant is , and obtain the actual working temperature at the moment ;

[0046] S5.2: Calculate the deviation value between the predicted working temperature at the moment and the actual working temperature ;

[0047] S5.3: Set the temperature deviation threshold to , and determine whether the deviation at the moment is within the acceptable range; if , the deviation at the moment is within the acceptable range; if , the deviation at the

[0048] ​Specifically, the adjustment of the heat dissipation adjustment table in S5.3 further includes:

[0049] Calculating the proportional coefficient of the flow rate adjustment at a moment based on the deviation value and the temperature deviation threshold ; ;

[0050] If , adjust the coolant pump so that the flow rate of the coolant is ;

[0051] If , adjust the coolant pump so that the flow rate of the coolant is .

[0052] A dynamic heat dissipation adjustment system for a wind energy storage cabinet wind direction predictor, comprising: a data acquisition module, a prediction module, and an adjustment module;

[0053] The data acquisition module is used to obtain the actual working temperature of the wind direction predictor and relevant environmental parameter data in real time, preprocess the acquired data, and save it to the constructed parameter database;

[0054] The prediction module is used to predict the environmental parameters, generate an environmental prediction sequence, analyze the relationship between the working temperature of the wind direction predictor and the environmental parameters, and predict the predicted working temperature of the environmental prediction sequence;

[0055] The adjustment module is used to construct a heat dissipation adjustment table according to the predicted working temperature, compare the predicted working temperature with the safe temperature range of the wind direction predictor, and formulate a heat dissipation adjustment strategy; continuously collect the actual working temperature of the wind direction predictor, and make a local adjustment to the heat dissipation adjustment table by comparing it with the predicted working temperature.

[0056] Specifically, the prediction module includes an environmental prediction unit and a temperature prediction unit;

[0057] The environmental prediction unit is used to construct an environmental prediction model, and input the environmental sequence at moment into the environmental prediction model to generate the environmental prediction sequence at moment ; ;

[0058] The temperature prediction unit is used to construct a temperature model, update the parameters by calculating the gradient values of the parameters in the temperature model; and output the predicted working temperature of the environmental prediction sequence .

[0059] Specifically, the adjustment module is configured with a heat dissipation adjustment strategy and an update strategy;

[0060] The heat dissipation adjustment strategy determines whether to perform heat dissipation adjustment on the predicted working temperature of the wind direction predictor by setting the safe temperature range of the wind direction predictor, and calculates the target flow rate of the expected coolant. Save the target flow rate to the heat dissipation adjustment table.

[0061] The update strategy continuously collects the actual working temperature of the wind direction predictor, calculates the deviation between the actual working temperature and the predicted working temperature, and updates the heat dissipation adjustment table.

[0062] Advantages of the present invention:

[0063] 1. Based on the constructed temperature model and environmental prediction model, the future working temperature can be predicted, and a heat dissipation adjustment table can be formulated accordingly, which helps to take measures in advance to optimize heat dissipation management and prevent the device from consuming more energy to maintain normal operation due to overheating. At the same time, by continuously monitoring the deviation between the actual working temperature and the predicted working temperature and updating the heat dissipation adjustment table in a timely manner, it can be ensured that the wind direction predictor always operates within an appropriate working temperature range.

[0064] 2. Through accurate temperature prediction and heat dissipation management, unnecessary energy waste can be avoided. For example, when it is predicted that the future temperature will not be too high, the power consumption of the heat dissipation system can be appropriately reduced, thus achieving the goal of energy conservation and consumption reduction. Description of the Drawings

[0065] Figure 1 It is a schematic diagram of a dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet;

[0066] Figure 2 It is a temperature prediction flowchart of a dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet;

[0067] Figure 3 It is an environmental prediction flowchart of a dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet;

[0068] Figure 4 It is a heat dissipation adjustment flowchart of a dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet;

[0069] Figure 5 It is a heat dissipation adjustment update flowchart of a dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet;

[0070] Figure 6 It is a structural diagram of a dynamic heat dissipation adjustment system for a wind direction predictor of a wind energy energy storage cabinet. Detailed Embodiments

[0071] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0072] Embodiment 1

[0073] Reference Figures 1 to 5 As shown, this embodiment introduces a dynamic heat dissipation adjustment method for the wind direction predictor of a wind energy energy storage cabinet, including the following steps:

[0074] Step S1: Use the high-precision temperature sensor configured in the wind direction predictor to obtain the actual working temperature of the wind direction predictor. At the same time, according to external environment detection devices such as anemometers, wind vanes, temperature and humidity meters, and barometers, collect relevant environmental parameter data, preprocess the collected data, and save it to the constructed parameter database; during the data collection process, set a unified initial collection time, sampling interval and sampling points , and record the data collection time in real time, so that the data has a unified time stamp, realizing the time synchronization of the data, which is convenient for subsequent statistics and analysis;

[0075] Step S2: Based on the data in the parameter database, analyze the relationship between the actual working temperature of the wind direction predictor and the environmental parameters, use the regression analysis method to construct a temperature model of the wind direction predictor, and train the temperature model until the temperature model predicts the working temperature of the wind direction predictor under given environmental parameters;

[0076] Step S3: Read the environmental parameter data in the parameter database, make a short-term prediction of the environmental parameters by analyzing the trend of historical environmental parameters, construct an environmental prediction model, and generate an environmental prediction sequence; in addition, environmental prediction helps to optimize the layout of wind energy generation equipment and improve energy utilization efficiency;

[0077] Step S4: Input the environmental prediction sequence into the trained temperature model, calculate the predicted working temperature of the wind direction predictor, construct a heat dissipation adjustment table based on the predicted working temperature, and compare the predicted working temperature with the safe temperature range of the wind direction predictor to formulate a heat dissipation adjustment strategy, so that the wind direction predictor can maintain an appropriate temperature during operation. For example, in an overheated environment, the device needs to consume more energy to maintain normal operation, resulting in increased energy consumption, avoiding performance degradation or damage of the wind direction predictor caused by overheating during operation, and reducing the maintenance and replacement costs caused by equipment failures;

[0078] Step S5: Continuously collect the actual working temperature of the wind direction predictor to ensure the real-time nature of the data. Compare the actual working temperature with the corresponding predicted working temperature, calculate the deviation, and locally adjust and update the heat dissipation adjustment table according to the magnitude of the deviation to ensure the effectiveness of the heat dissipation adjustment table, while avoiding unnecessary energy consumption, thereby achieving the goal of energy conservation and emission reduction.

[0079] Specifically, the specific steps of step S2 include:

[0080] S2.1: Use the regression analysis method to construct a temperature model, and define the working temperature of the wind direction predictor as the dependent variable of the model; since the working temperature is affected by various environmental factors, for example, high humidity will increase the heat load of the wind direction predictor and thus cause the working temperature to rise, and low air pressure results in low air density and reduced heat dissipation ability, leading to an increase in the working temperature. Therefore, define each environmental parameter as an independent variable of the model, such as environmental temperature, humidity, wind speed, wind direction, and air pressure; the expression of the objective function of the model is as follows:

[0081]

[0082] In the formula, is the dependent variable of the model, is the number of independent variables of the model, and is the th environmental parameter among the independent variables, is the parameter 's regression coefficient, is the parameter group to be solved in the temperature model. By solving and optimizing the parameter group, the temperature model can accurately predict the working temperature of the wind direction predictor, denoted as the predicted working temperature of the wind direction predictor;

[0083] S2.2: In order to solve the specific values of the parameter group, construct the target parameter function , and the target parameter function is used to measure the difference between the predicted working temperature and the actual working temperature; the expression is as follows:

[0084]

[0085] In the formula, is the number of samples of the collected historical data, is the actual working temperature of the wind direction predictor in the th sample, is the predicted working temperature output by the environmental parameter through the temperature model in the th sample, and , , , , are respectively the The values of each independent variable in the samples;

[0086] S2.3: Set an initial value for the parameter group and set the initial number of iterations , for example, set ;

[0087] S2.4: Solve the partial derivative of the parameter in the target parameter function and calculate the gradient value of the parameter , and the expression is as follows:

[0088]

[0089] In the formula, is the value of the th independent variable in the th sample, and , and at the same time set ;

[0090] S2.5: Update the value of the parameter based on the gradient value, and the expression is as follows:

[0091]

[0092] In the formula, is the learning rate, and the initial learning rate is set. In this embodiment, is taken. A smaller initial learning rate helps prevent the temperature model from skipping the optimal value of the parameter due to too large a step size at the initial stage of training;

[0093] S2.6: Set the iteration interval threshold for learning rate update to , so that the learning rate is updated every iterations. For example, set to 10, and the learning rate is updated every 10 iteration operations; and use the modulo operator to calculate the remainder between the current iteration number and the iteration interval threshold , and the expression is as follows:

[0094]

[0095] S2.7: Judge whether to perform the learning rate decay calculation based on the remainder ; if , when the current iteration number reaches the predetermined iteration interval threshold, update the learning rate to , where is the learning decay factor and​ , in this embodiment, take 0.9. When making the temperature model approach the optimal solution by periodically decreasing the learning rate, the step size of parameter update gradually decreases, which helps to adjust the parameters more precisely and improve the adaptability and robustness of the model; if , the current iteration number does not reach the predetermined iteration interval threshold, and the learning rate is not updated;

[0096] S2.8: Update the iteration number to , and obtain the value of the target parameter function before parameter update and the value after parameter update , and calculate the change rate of the target parameter function ;

[0097] S2.9: Set the convergence threshold of the target parameter function to , the change rate threshold to , and determine whether the target parameter function converges; if and , the target parameter function converges, and enter S2.10; if or , the target parameter function does not converge, and return to S2.4 to continue updating the parameters;

[0098] S2.10: Obtain the converged parameter group , substitute the parameter group into the temperature model. At this time, the training of the temperature model is completed, and the temperature model is output for subsequent prediction.

[0099] Specifically, the specific steps of step S3 include:

[0100] S3.1: Read the environmental parameter data in the parameter database and construct the environmental parameter matrix ; where is the number of collected samples. Based on the set sampling interval and the sampling point , the prediction interval can be calculated, is the th environmental parameter sequence, and , , is the number of environmental parameters, is the th th environmental parameter value in the parameter sequence ;

[0101] S3.2: Use a recurrent neural network to construct an environmental prediction model. The model includes an input layer, a hidden layer, and an output layer; the objective function expression of the environmental prediction model is as follows:

[0102]

[0103] Wherein, is the output matrix of the environmental prediction model, is the activation function, is the connection weight matrix between the input layer and the hidden layer, and , is the weight value corresponding to the th environmental parameter value, is the input matrix of the environmental prediction model, is the bias matrix;

[0104] S3.3: Based on the environmental parameter matrix , define the input matrix and the theoretical output matrix of the environmental prediction model; wherein, is the sample size of the input matrix, and , when the input sequence is , the output sequence of the environmental prediction model is , realizing the numerical prediction of the environmental parameters every time periods;

[0105] S3.4: Use the ant colony optimization algorithm to solve and optimize the connection weight matrix and the bias matrix in the environmental prediction model to obtain the environmental prediction model;

[0106] S3.5: Collect the environmental sequence at the current moment in real time, and output the environmental prediction sequence at the moment through the environmental prediction model; wherein, is the th environmental parameter value at the moment , is the th environmental parameter value at the moment predicted by the environmental prediction model.

[0107] Specifically, the solution steps using the ant colony optimization algorithm in S3.4 also include:

[0108] S3.41: Initialize the parameters, including the connection weight matrix , the bias matrix , the number of ant colonies and the initial value of the number of iterations ;

[0109] S3.42: Based on the known input sequence and objective function, calculate the probability for each ant to select the next path, and move each ant to a new position according to the selection probability. Record in real time the parameter values corresponding to each ant moving to the new position, including , and ; where is the predicted output matrix of the environmental prediction model;

[0110] S3.43: Calculate the error between the theoretical output matrix and the predicted output matrix , and the expression is as follows:

[0111]

[0112] S3.44: Record the parameter values corresponding to the minimum error of the current iteration, mark them as the local optimal solution, and update the pheromone for the optimal solution of the current iteration;

[0113] S3.45: Set the number of optimization iterations to , and determine whether the number of optimization iterations is reached; if , the number of optimization iterations is not reached, let , return to S3.42, and start a new round of iterative operations; if , the number of optimization iterations is reached, output the and obtained in the current iteration.

[0114] Specifically, the specific steps of step S4 include:

[0115] S4.1: Input the environmental prediction sequence at the moment into the trained temperature model, calculate the predicted operating temperature of the wind direction predictor, and save the predicted operating temperature to the constructed heat dissipation adjustment table; where the heat dissipation adjustment table is used to save the predicted operating temperature of the wind direction predictor, the initial flow rate of the coolant, the expected target flow rate, the actual operating temperature collected in real time after a period of time, and the actual flow rate judged based on the actual operating temperature. In this embodiment, the heat dissipation device is a liquid cooling heat dissipation device. When the wind direction predictor works, the generated heat is transferred to the liquid cooling pipe through the radiator, and the heat is absorbed through the circulating flow of the coolant. Set the initial flow rate of the coolant to be constant, which is ;

[0116] S4.2: Set the safety temperature range of the wind direction predictor to ; where is the lower limit of the operating temperature of the wind direction predictor, is the upper limit of the operating temperature of the wind direction predictor;

[0117] S4.3: Determine the predicted operating temperature and check if it is within the safe temperature range; if the predicted operating temperature is within the safe temperature range; if or the predicted operating temperature is not within the safe temperature range, mark a warning for the predicted operating temperature at time in the heat dissipation adjustment table;

[0118] S4.4: Obtain the predicted operating temperature with a warning mark in the heat dissipation adjustment table , and calculate the predicted target flow rate of the coolant at time according to the heat balance equation, with the expression as follows:

[0119]

[0120] In the formula, is the specific time corresponding to the predicted operating temperature, is the power consumption of the wind direction predictor, is the heat generated during the operation of the wind direction predictor, is the heat transfer efficiency of the radiator, is the heat dissipation area of the radiator, is the density of the coolant, is the specific heat capacity, is the difference between the target temperature and the predicted ambient temperature, , is the predicted ambient temperature, which is extracted from the ambient prediction sequence at time ;

[0121] S4.5: Save the predicted target flow rate of the coolant at time to the heat dissipation adjustment table, so that the liquid cooling heat dissipation device can control the opening of the coolant pump or valve through the target flow rate at the corresponding time in the heat dissipation adjustment table. Specifically, the specific steps of step S5 include:

[0122] S5.1: Continuously collect the actual operating temperature of the wind direction predictor. When the time reaches

[0123] time, adjust the coolant pump so that the flow rate of the coolant is , and obtain the actual operating temperature at time , and obtain the actual operating temperature at time , since the liquid cooling pipe dissipates the heat transferred by the radiator to the air through the heat dissipation fins, causing slight changes in the external environmental parameters. At the same time, in order to cope with the error between the prediction model and the actual parameters and achieve dynamic adjustment of the heat dissipation regulation strategy;

[0124] S5.2: Calculate The predicted working temperature at the moment and the actual working temperature The deviation value between them , and the expression is as follows:

[0125]

[0126] S5.3: Set the temperature deviation threshold to , and judge whether The deviation at the moment is within the acceptable range; if , The deviation at the moment is within the acceptable range; if , The deviation at the moment is not within the acceptable range, and the heat dissipation regulation table is dynamically adjusted.

[0127] Specifically, the specific steps of adjusting the heat dissipation regulation table in S5.3 include:

[0128] Calculate based on the deviation value and the temperature deviation threshold The proportional coefficient of the flow rate adjustment at the moment , and the expression is as follows:

[0129]

[0130] If , adjust the coolant pump so that the flow rate of the coolant is ;

[0131] If , adjust the coolant pump so that the flow rate of the coolant is .

[0132] Embodiment 2

[0133] Please refer to Figure 6 , another embodiment provided by the present invention: A dynamic heat dissipation regulation system for a wind energy storage cabinet wind direction predictor, including: a data acquisition module, a prediction module, and an adjustment module;

[0134] The data acquisition module is used to obtain the actual working temperature of the wind direction predictor according to the high-precision temperature sensor configured in the wind direction predictor. At the same time, according to external environmental detection devices such as an anemometer, a wind vane, a thermometer, and a barometer, collect relevant environmental parameter data, preprocess the collected data, and save it to the constructed parameter database;

[0135] The prediction module is used to analyze the data in the parameter database, make short-term predictions on environmental parameters, construct an environmental prediction model, generate an environmental prediction sequence, and at the same time analyze the relationship between the actual working temperature of the wind direction predictor and the environmental parameters, predict the working temperature of the wind direction predictor under given environmental parameters, combine to generate a predicted working temperature according to the environmental prediction sequence, and transmit the prediction result to the adjustment module in real time;

[0136] The adjustment module is used to construct a heat dissipation adjustment table according to the prediction result, compare the predicted working temperature with the safe temperature range of the wind direction predictor, formulate a heat dissipation adjustment strategy, and update the heat dissipation adjustment table in real time, so that the wind direction predictor can maintain an appropriate temperature during operation, avoid performance degradation or damage caused by overheating during the operation of the wind direction predictor, reduce the maintenance and replacement costs caused by equipment failures, continuously collect the actual working temperature of the wind direction predictor, make local adjustments to the heat dissipation adjustment table by comparing with the predicted working temperature, ensure the effectiveness of the heat dissipation adjustment table, and avoid unnecessary energy consumption at the same time, so as to achieve the goal of energy conservation and emission reduction.

[0137] Specifically, the prediction module includes an environmental prediction unit and a temperature prediction unit;

[0138] The environmental prediction unit uses a recurrent neural network to construct an environmental prediction model, and uses a training set to train the environmental prediction model, adjust and optimize the parameters in the environmental prediction model, so that the environmental prediction model can achieve short-term prediction of environmental parameters; obtain the environmental sequence at time and use the trained environmental prediction model to output the environmental prediction sequence at time ;

[0139] The temperature prediction unit is used to analyze the relationship between the actual working temperature of the wind direction predictor and the environmental parameters, use the regression analysis method to construct a temperature model, in order to solve the unknown parameters in the temperature model, construct an objective parameter function and calculate the gradient values of each parameter, update each parameter based on the gradient value, and calculate the change rate of the objective parameter function before and after the update, judge whether the objective parameter function converges, if it does not converge, continue to update the parameters, if it converges, output the specific parameter values, at this time, the temperature model training is completed; and input the environmental prediction sequence into the trained temperature model to output the predicted working temperature of the wind direction predictor .

[0140] Specifically, an ant colony optimization strategy is configured in the environmental prediction unit. The ant colony optimization strategy is used to solve the parameters in the environmental prediction model, move the ants to new positions according to the probability of each ant choosing a path, record the parameter values and the objective function values in real time, calculate the error between the theoretical value and the actual value, and update the pheromone for the value of the minimum error until the iteration is completed, and output the optimal parameter value of the current iteration.

[0141] Specifically, a heat dissipation regulation strategy and an update strategy are configured in the adjustment module;

[0142] The heat dissipation regulation strategy judges whether to perform heat dissipation regulation on the predicted working temperature of the wind direction predictor by setting the safe temperature range of the wind direction predictor, and calculates the predicted target flow rate of the coolant at the corresponding moment by using the heat balance equation and saves the predicted target flow rate to the corresponding position in the heat dissipation regulation table, so that the liquid cooling heat dissipation device controls the opening of the coolant pump or valve through the target flow rate at the corresponding moment in the heat dissipation regulation table;

[0143] The update strategy continuously obtains the actual working temperature of the wind direction predictor, compares it with the predicted working temperature at the corresponding moment, calculates the deviation, and makes local adjustments and updates to the heat dissipation regulation table according to the size of the deviation to cope with unexpected situations during the actual operation of the wind direction predictor.

[0144] In summary of the above embodiments, the present invention analyzes the environmental parameters and the actual working temperature of the wind direction predictor, constructs an environmental prediction model to realize the short-term prediction of environmental parameters, and at the same time constructs a temperature model to analyze the relationship between environmental parameters and the actual working temperature, so as to obtain the predicted working temperature of the wind direction predictor at the corresponding time according to the result of the short-term environmental prediction, and formulates a heat dissipation regulation strategy based on the predicted working temperature to avoid the performance degradation or damage of the wind direction predictor caused by overheating during operation. At the same time, continuously collect the actual working temperature of the wind direction predictor, compare it with the predicted working temperature at the corresponding time, and adjust the heat dissipation regulation strategy in real time to avoid unnecessary energy waste.

[0145] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet, characterized in that, including: Step S1: Use the high-precision temperature sensor configured in the wind direction predictor to obtain the actual working temperature of the wind direction predictor. At the same time, collect relevant environmental parameter data according to the external environment detection device and save it to the constructed parameter database. During the data collection process, set a unified initial collection time, sampling interval and sampling points , and record the data collection time in real time so that the data has a unified timestamp, realizing the time synchronization of the data; Step S2: Based on the data in the parameter database, analyze the relationship between the actual working temperature of the wind direction predictor and the environmental parameters, construct a temperature model of the wind direction predictor using regression analysis, and train the temperature model to predict the working temperature of the wind direction predictor under given environmental parameters; Step S3: Read the environmental parameter data in the parameter database, perform short-term prediction on the environmental parameters by analyzing the trend of historical environmental parameters, construct an environmental prediction model, and generate an environmental prediction sequence; Step S4: Input the environmental prediction sequence into the trained temperature model, calculate the predicted working temperature of the wind direction predictor, construct a heat dissipation adjustment table based on the predicted working temperature, and compare the predicted working temperature with the safe temperature range of the wind direction predictor to formulate a heat dissipation adjustment strategy to keep the wind direction predictor at an appropriate temperature during operation; Step S5: Continuously collect the actual working temperature of the wind direction predictor, compare the actual working temperature with the corresponding predicted working temperature, calculate the deviation, and locally adjust and update the heat dissipation adjustment table according to the magnitude of the deviation.

2. The dynamic heat dissipation adjustment method of a wind energy storage cabinet wind direction predictor according to claim 1, characterized in that, The said Step S2 includes: S2.1: Construct a temperature model using regression analysis, define the working temperature of the wind direction predictor as the dependent variable and the environmental parameters as the independent variables; the objective function expression of the temperature model is as follows: ； In the formula, is the dependent variable of the model, is the number of independent variables of the model, is the th environmental parameter in the independent variables, is the parameter 's regression coefficient; S2.2: Construct the target parameter function ; S2.3: Initialize the parameter group , and set the initial number of iterations ; S2.4: For the parameters in the target parameter function perform partial derivative solution and calculate the gradient value of the parameter ; ; S2.5: Update the value of the parameter based on the gradient value, and the expression is as follows: ； In the formula, is the learning rate.

3. The dynamic heat dissipation adjustment method of a wind direction predictor for a wind energy energy storage cabinet according to claim 2, characterized in that, The said Step S2 also includes: S2.6: Set the iteration interval threshold for learning rate update to be , and calculate the current iteration number and the remainder between the iteration interval threshold ; ; S2.7: Based on the remainder determine whether to perform the decay calculation of the learning rate; if , when the current iteration number reaches the predetermined iteration interval threshold, update the learning rate to ; if , when the current iteration number does not reach the predetermined iteration interval threshold, do not update the learning rate; where is the learning decay factor; S2.8: Update the iteration count to , and obtain the value of the target parameter function before parameter update and the value after parameter update , and calculate the change rate of the target parameter function ; S2.9: Set the convergence threshold of the target parameter function to , and the change rate threshold to , and determine whether the target parameter function converges; if and , the target parameter function converges, and proceed to S2.10; if or , the target parameter function does not converge, and return to S2.4 to continue updating the parameters; S2.10: Obtain the converged parameter set , and the temperature model training is completed.

4. The dynamic heat dissipation adjustment method of a wind direction predictor for a wind energy energy storage cabinet according to claim 3, wherein, The said Step S3 includes: S3.1: Construct the environmental parameter matrix ; where is the th environmental parameter sequence, is the number of collected samples; S3.2: Construct an environmental prediction model using a recurrent neural network, and the objective function expression is as follows: ； wherein, is the output matrix of the environmental prediction model, is the activation function, is the connection weight matrix between the input layer and the hidden layer, is the input matrix of the environmental prediction model, is the bias matrix; S3.3: Based on the environmental parameter matrix , define the input matrix and the theoretical output matrix of the environmental prediction model; where is the sample size of the input matrix. S3.4: Use the ant colony optimization algorithm to solve and optimize the connection weight matrix and the bias matrix in the environmental prediction model; S3.5: Real-time acquisition of the environmental sequence at a moment , and output through the environmental prediction model the environmental prediction sequence at a moment .

5. The dynamic heat dissipation adjustment method of a wind direction predictor for a wind energy storage cabinet according to claim 4, characterized in that, The said Step S4 includes: S4.1: Input the environmental prediction sequence into the temperature model to calculate the predicted operating temperature of the wind direction predictor , and save the predicted operating temperature into the constructed heat dissipation adjustment table; S4.2: Set the safety temperature range of the wind direction predictor to be ; S4.3: Determine the predicted operating temperature whether it is within the safe temperature range; if , the predicted operating temperature is within the safe temperature range; if or , the predicted operating temperature is not within the safe temperature range, and a warning mark is made for the predicted operating temperature at time in the heat dissipation adjustment table.

6. The dynamic heat dissipation adjustment method of a wind direction predictor for a wind energy storage cabinet according to claim 5, characterized in that, The said Step S4 also includes: S4.4: Obtain the predicted working temperature with a warning mark in the heat dissipation adjustment table , calculate the predicted target flow rate of the coolant at the moment ; S4.5: Store the predicted target flow rate of the coolant at a moment in the heat dissipation adjustment table.

7. A dynamic heat dissipation adjustment method for a wind direction predictor of a wind energy energy storage cabinet according to claim 6, characterized in that, The said Step S5 includes: S5.1: When the time reaches moment, adjust the coolant pump so that the flow rate of the coolant is , and obtain the actual working temperature at the moment ; S5.2: Calculate The predicted working temperature at a moment and the actual working temperature The deviation value between them ; S5.3: Set the temperature deviation threshold to , and determine whether the deviation at is within the acceptable range; if the deviation at is within the acceptable range; if the deviation at is not within the acceptable range, dynamically adjust the heat dissipation adjustment table.

8. The dynamic heat dissipation adjustment method of a wind direction predictor for a wind energy energy storage cabinet according to claim 7, characterized in that, In the adjustment of the heat dissipation adjustment table in S5.3, it also includes: Calculation based on the deviation value and the temperature deviation threshold Proportional coefficient for adjusting the flow rate at a moment ; If , adjust the coolant pump so that the flow rate of the coolant is ; If , adjust the coolant pump so that the flow rate of the coolant is .

9. A dynamic heat dissipation regulation system for a wind direction predictor of a wind energy energy storage cabinet, which is used to implement the dynamic heat dissipation regulation method of a wind direction predictor of a wind energy energy storage cabinet as described in any one of claims 1-8, characterized in that, including: a data acquisition module, a prediction module, and an adjustment module; The data acquisition module is used to obtain the actual working temperature of the wind direction predictor and relevant environmental parameter data in real time, preprocess the collected data, and save it to the constructed parameter database; The prediction module is used to predict the environmental parameters, generate an environmental prediction sequence, and analyze the relationship between the working temperature of the wind direction predictor and the environmental parameters to predict the predicted working temperature of the environmental prediction sequence; The adjustment module is used to construct a heat dissipation adjustment table according to the predicted working temperature, compare the predicted working temperature with the safe temperature range of the wind direction predictor, and formulate a heat dissipation adjustment strategy; continuously collect the actual working temperature of the wind direction predictor, and locally adjust the heat dissipation adjustment table by comparing it with the predicted working temperature.

10. The dynamic heat dissipation regulation system of a wind energy storage cabinet wind direction predictor according to claim 9, characterized in that, The prediction module includes an environmental prediction unit and a temperature prediction unit; The environmental prediction unit is used to construct an environmental prediction model, and input the environmental sequence at time into the environmental prediction model to generate the environmental prediction sequence at time; ; The temperature prediction unit is used to construct a temperature model, update the parameters by calculating the gradient values of the respective parameters in the temperature model; and output the predicted working temperature of the environmental prediction sequence of the .

11. The dynamic heat dissipation regulation system of a wind energy storage cabinet wind direction predictor according to claim 9, wherein, The adjustment module is configured with a heat dissipation adjustment strategy and an update strategy; The heat dissipation adjustment strategy determines whether to perform heat dissipation adjustment on the predicted working temperature of the wind direction predictor by setting the safe temperature range of the wind direction predictor, and calculates the predicted target flow rate of the coolant , and saves the target flow rate to the heat dissipation adjustment table; The update strategy continuously collects the actual working temperature of the wind direction predictor, calculates the deviation between the actual working temperature and the predicted working temperature, and updates the heat dissipation adjustment table.

Images