Unit excitation system fan energy-saving control method based on multi-parameter dynamic adjustment

Abstract

The invention relates to a unit excitation system fan energy-saving control method based on multi-parameter dynamic adjustment. The method comprises the following steps: acquiring joint regulation and control data of a target fan; inputting the combined regulation and control data into a trained dynamic weight prediction model, wherein the dynamic weight prediction model outputs a dynamic weight corresponding to each type of data based on the relevance between each type of data in the combined regulation and control data and the fan energy-saving effect of the excitation system of the target fan; based on the dynamic weight corresponding to each type of data in the joint regulation and control data, determining a comprehensive regulation coefficient; based on a preset first mapping relation table, determining a target fan rotating speed matched with the comprehensive adjustment coefficient; and controlling the target fan to operate according to the target fan rotating speed. According to the scheme, the fan energy consumption is effectively reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of control of excitation systems of hydropower station units, and in particular to an energy-saving control method for fans of unit excitation systems based on multi-parameter dynamic regulation. Background Art

[0002] In the related technology, the fan of the traditional unit excitation system usually uses the unit start-up command to start the excitation fan to run at a fixed speed or rated speed, or simple temperature control start and stop, resulting in the following problems: energy waste: the fan runs at full speed for a long time, and continues to consume energy even when the temperature of the excitation power cabinet is low or the unit load is small; temperature control lag: only relying on the temperature threshold to trigger the fan start and stop, the response speed is slow, which may affect the heat dissipation efficiency of the excitation power cabinet; failure to consider operating conditions: the fan operation strategy is not optimized in combination with dynamic parameters such as unit load and reactive power. Summary of the Invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a fan energy-saving control method of a unit excitation system based on multi-parameter dynamic adjustment.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for controlling fan energy saving in a unit excitation system based on dynamic adjustment of multiple parameters is provided, comprising:

[0005] Acquire joint control data of the target fan; the joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity;

[0006] Inputting the joint control data into the trained dynamic weight prediction model to obtain the dynamic weight corresponding to each data output by the dynamic weight prediction model based on the correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target wind turbine;

[0007] Determining a comprehensive adjustment coefficient based on the dynamic weight corresponding to each type of data in the joint regulation data;

[0008] Determining a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed;

[0009] The target fan is controlled to operate according to the target fan speed, and the process returns to the step of obtaining the joint control data of the target fan.

[0010] In some embodiments of the present disclosure, determining the comprehensive adjustment coefficient based on the dynamic weight corresponding to each type of data in the joint control data includes:

[0011] Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain the comprehensive adjustment coefficient.

[0012] In some embodiments of the present disclosure, the first mapping relationship table includes mapping relationships between multiple comprehensive adjustment coefficient intervals and different fan speed adjustment methods;

[0013] The determining, based on a preset first mapping relationship table, a target fan speed that matches the comprehensive adjustment coefficient includes:

[0014] When the comprehensive coefficient is less than the first preset coefficient, multiplying the rated speed of the target fan by the first proportion value to obtain the target fan speed;

[0015] When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, determining the target fan speed according to a preset linear proportional relationship;

[0016] When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

[0017] In some embodiments of the present disclosure, the dynamic weight prediction model is trained by the following steps:

[0018] Obtaining historical joint control data of the target wind turbine and actual energy consumption corresponding to the historical joint control data; the historical joint control data includes historical excitation power cabinet temperature, historical unit load, historical reactive power and historical ambient humidity;

[0019] Normalizing the historical joint regulation data to obtain normalized historical joint regulation data;

[0020] Inputting the normalized historical joint control data into the neural network model to be trained, obtaining the predicted dynamic weight corresponding to each type of historical data in the historical joint control data output by the neural network model, and the predicted energy consumption corresponding to the predicted dynamic weight;

[0021] Calculating a loss value according to a preset loss function based on the predicted energy consumption, the actual energy consumption, the historical excitation power cabinet temperature, and the excitation power cabinet safety temperature;

[0022] When the loss value is greater than or equal to the preset loss value, the neural network model is adjusted, and the step of inputting the normalized historical joint control data into the neural network model to be trained is returned to execute until the loss value is less than the preset loss value, thereby obtaining the dynamic weight prediction model.

[0023] According to a second aspect of an embodiment of the present disclosure, there is provided a fan energy-saving control device for a unit excitation system based on multi-parameter dynamic adjustment, comprising:

[0024] An acquisition unit is used to acquire joint control data of a target wind turbine; the joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity;

[0025] A prediction unit, configured to input the joint control data into a trained dynamic weight prediction model, and obtain a dynamic weight corresponding to each data output by the dynamic weight prediction model based on a correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target wind turbine;

[0026] A first determining unit, configured to determine a comprehensive adjustment coefficient based on a dynamic weight corresponding to each type of data in the joint control data;

[0027] a second determining unit, configured to determine a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table; wherein the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed;

[0028] A control unit is used to control the target fan to operate according to the target fan speed, and return to execute the step of obtaining the joint control data of the target fan.

[0029] In some embodiments of the present disclosure, the first determining unit is specifically configured to:

[0030] Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain the comprehensive adjustment coefficient.

[0031] In some embodiments of the present disclosure, the first mapping relationship table includes mapping relationships between multiple comprehensive adjustment coefficient intervals and different fan speed adjustment methods; and the second determining unit is specifically configured to:

[0032] When the comprehensive coefficient is less than the first preset coefficient, multiplying the rated speed of the target fan by the first proportion value to obtain the target fan speed;

[0033] When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, determining the target fan speed according to a preset linear proportional relationship;

[0034] When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

[0035] In some embodiments of the present disclosure, the apparatus may further include a training unit, wherein the training unit is configured to:

[0036] Obtaining historical joint control data of the target wind turbine and actual energy consumption corresponding to the historical joint control data; the historical joint control data includes historical excitation power cabinet temperature, historical unit load, historical reactive power and historical ambient humidity;

[0037] Normalizing the historical joint regulation data to obtain normalized historical joint regulation data;

[0038] Inputting the normalized historical joint control data into the neural network model to be trained, obtaining the predicted dynamic weight corresponding to each type of historical data in the historical joint control data output by the neural network model, and the predicted energy consumption corresponding to the predicted dynamic weight;

[0039] Calculating a loss value according to a preset loss function based on the predicted energy consumption, the actual energy consumption, the historical excitation power cabinet temperature, and the excitation power cabinet safety temperature;

[0040] When the loss value is greater than or equal to the preset loss value, the neural network model is adjusted, and the step of inputting the normalized historical joint control data into the neural network model to be trained is returned to execute until the loss value is less than the preset loss value, thereby obtaining the dynamic weight prediction model.

[0041] According to a third aspect of an embodiment of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects is implemented.

[0042] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0043] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.

[0044] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by obtaining the joint control data of the target fan; the joint control data includes the excitation power cabinet temperature, unit load, reactive power and ambient humidity; inputting the joint control data into a trained dynamic weight prediction model, the dynamic weight prediction model outputting the dynamic weight corresponding to each data based on the correlation between each data in the joint control data and the energy-saving effect of the excitation system fan of the target fan; determining the comprehensive adjustment coefficient based on the dynamic weight corresponding to each data in the joint control data; determining the target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed; controlling the target fan to operate according to the target fan speed, and returning to the step of obtaining the joint control data of the target fan. The present disclosure dynamically adjusts the comprehensive control coefficient of the target fan by comprehensively adjusting the excitation power cabinet temperature, unit load, reactive power and ambient humidity, and dynamically adjusts the fan speed based on the comprehensive control coefficient, thereby improving the flexibility of the fan, effectively reducing the fan energy consumption while meeting the heat dissipation requirements.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0047] Figure 1 The present invention is a flow chart showing a method for controlling fan energy saving in a unit excitation system based on dynamic adjustment of multiple parameters according to an exemplary embodiment.

[0048] Figure 2 The present invention is a block diagram of a fan energy-saving control device for a unit excitation system based on multi-parameter dynamic adjustment according to an exemplary embodiment.

[0049] Figure 3 The present invention is a block diagram of an apparatus for a fan energy-saving control method of a unit excitation system based on multi-parameter dynamic adjustment according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0051] The terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms "a", "an" and "the" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0052] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0053] Furthermore, the various forms of processes shown in the embodiments of this disclosure may be used to reorder, add, or delete steps. For example, the steps described in this application may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0054] In the related technology, the fan of the traditional unit excitation system usually uses the unit start-up command to start the excitation fan to run at a fixed speed or rated speed, or simple temperature control start and stop, resulting in the following problems: energy waste: the fan runs at full speed for a long time, and continues to consume energy even when the temperature of the excitation power cabinet is low or the unit load is small; temperature control lag: only relying on the temperature threshold to trigger the fan start and stop, the response speed is slow, which may affect the heat dissipation efficiency of the excitation power cabinet; failure to consider operating conditions: the fan operation strategy is not optimized in combination with dynamic parameters such as unit load and reactive power.

[0055] In order to solve the above problems, the present disclosure provides a method for controlling fan energy saving in a unit excitation system based on multi-parameter dynamic adjustment, by obtaining joint control data of a target fan; the joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity; the joint control data is input into a trained dynamic weight prediction model, the dynamic weight prediction model outputs a dynamic weight corresponding to each data based on the correlation between each data in the joint control data and the energy-saving effect of the fan in the excitation system of the target fan; based on the dynamic weight corresponding to each data in the joint control data, a comprehensive adjustment coefficient is determined; based on a preset first mapping relationship table, a target fan speed matching the comprehensive adjustment coefficient is determined; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed; the target fan is controlled to operate according to the target fan speed, and the step of obtaining the joint control data of the target fan is returned. The present disclosure dynamically adjusts the comprehensive control coefficient of the target fan by comprehensively adjusting the excitation power cabinet temperature, unit load, reactive power and ambient humidity, and dynamically adjusts the fan speed based on the comprehensive control coefficient, thereby improving the flexibility of the fan, effectively reducing the fan energy consumption while meeting the heat dissipation requirements.

[0056] Figure 1 is a flow chart showing a method for controlling fan energy saving in a unit excitation system based on dynamic adjustment of multiple parameters according to an exemplary embodiment. Figure 1 As shown, it should be noted that the energy-saving control method of the fan in the unit excitation system based on multi-parameter dynamic adjustment of the embodiment of the present disclosure is applied to the energy-saving control device of the fan in the unit excitation system based on multi-parameter dynamic adjustment. Figure 1 As shown, the method may include the following steps:

[0057] Step 101: Acquire joint control data of a target wind turbine.

[0058] The joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity.

[0059] In one embodiment, a temperature sensor may be installed on a thyristor heat sink of the excitation power cabinet of the unit to detect the temperature of the excitation power cabinet.

[0060] In addition, real-time unit load and reactive power data can be obtained from the unit PLC or active power and reactive power transmitters

[0061] In step 102, the joint control data is input into the trained dynamic weight prediction model to obtain the dynamic weight corresponding to each data output by the dynamic weight prediction model based on the correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target fan.

[0062] In one embodiment, before step 102 , the joint control data may be normalized, that is, each parameter may be mapped to the interval [0, 1] to eliminate the dimension effect.

[0063] Step 103: Determine a comprehensive adjustment coefficient based on the dynamic weight corresponding to each type of data in the joint control data.

[0064] In some embodiments of the present application, step 103 may specifically include the following steps:

[0065] Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain a comprehensive adjustment coefficient.

[0066] In one embodiment, the comprehensive adjustment coefficient K can be calculated using the following formula:

[0067] K=α·T+β·P+γ·Q+δ·T a

[0068] Among them, α, β, γ, δ are dynamic weights, T is the temperature of the excitation power cabinet, P is the active load of the unit, Q is the reactive power, T a is the ambient temperature.

[0069] Step 104 : Determine a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table.

[0070] The first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed.

[0071] In some embodiments of the present application, step 104 may specifically include the following steps:

[0072] Determining a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table includes:

[0073] When the comprehensive coefficient is less than the first preset coefficient, the rated speed of the target fan is multiplied by the first proportion value to obtain the target fan speed;

[0074] When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, the target fan speed is determined according to a preset linear proportional relationship;

[0075] When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

[0076] For example, if K < Kmin, the target fan operates at a low speed (such as 30% of the rated speed); if Kmin ≤ K < Kmax, the target fan adjusts its speed according to a linear ratio; if K ≥ Kmax, the target fan operates at full speed. Here, Kmin is the first preset coefficient and Kmax is the second preset coefficient.

[0077] Step 105: Control the target fan to operate at the target fan speed, and return to execute the step of obtaining the combined control data of the target fan.

[0078] In one embodiment, a frequency converter can be used to drive the motor of the unit's excitation fan to operate at the target fan speed.

[0079] It can be understood that in order to continuously and dynamically adjust the speed of the fan, steps 101 to 105 need to be executed periodically during the operation of the fan to achieve continuous and dynamic adjustment of the fan speed.

[0080] In some embodiments of the present application, the dynamic weight prediction model is trained through the following steps:

[0081] Obtain the historical combined control data of the target fan and the actual energy consumption corresponding to the historical combined control data; the historical combined control data includes the historical excitation power cabinet temperature, historical unit load, historical reactive power, and historical ambient humidity;

[0082] Perform normalization processing on the historical combined control data to obtain the normalized historical combined control data;

[0083] Input the normalized historical combined control data into the neural network model to be trained, and obtain the predicted dynamic weights corresponding to each type of historical data in the historical combined control data output by the neural network model, and the predicted energy consumption corresponding to the predicted dynamic weights;

[0084] Based on the predicted energy consumption, actual energy consumption, historical excitation power cabinet temperature, and excitation power cabinet safety temperature, calculate the loss value according to the preset loss function;

[0085] In the case where the loss value is greater than or equal to the preset loss value, adjust the parameters of the neural network model, and return to execute the step of inputting the normalized historical combined control data into the neural network model to be trained until the loss value is less than the preset loss value, and obtain the dynamic weight prediction model.

[0086] In one embodiment, the following loss function can be used to calculate the above loss value:

[0087] L = MSE(Predicted energy consumption, Actual energy consumption) + μ · max(T - Tsafe, 0)

[0088] Where L is the loss value, T is the excitation power cabinet temperature, Tsafe is the excitation power cabinet safety temperature, and μ is the temperature over-limit penalty coefficient.

[0089] According to the energy-saving control method of the fan of the unit excitation system based on multi-parameter dynamic adjustment proposed in the embodiment of the present disclosure, the joint control data of the target fan is obtained; the joint control data includes the excitation power cabinet temperature, unit load, reactive power and ambient humidity; the joint control data is input into the trained dynamic weight prediction model, the dynamic weight prediction model outputs the dynamic weight corresponding to each data based on the correlation between each data in the joint control data and the energy-saving effect of the fan of the excitation system of the target fan; based on the dynamic weight corresponding to each data in the joint control data, a comprehensive adjustment coefficient is determined; based on a preset first mapping relationship table, a target fan speed matching the comprehensive adjustment coefficient is determined; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed; the target fan is controlled to operate according to the target fan speed, and the step of obtaining the joint control data of the target fan is returned. The present disclosure dynamically adjusts the comprehensive control coefficient of the target fan by comprehensively adjusting the excitation power cabinet temperature, unit load, reactive power and ambient humidity, and dynamically adjusts the fan speed based on the comprehensive control coefficient, thereby improving the flexibility of the fan, effectively reducing the fan energy consumption while meeting the heat dissipation requirements.

[0090] Figure 2 This is a block diagram of a fan energy-saving control device for a unit excitation system based on multi-parameter dynamic adjustment according to an exemplary embodiment. Figure 2 The device includes an acquisition unit 201, a prediction unit 202, a first determination unit 203, a second determination unit 204 and a control unit 205.

[0091] The acquisition unit 201 is used to acquire the joint control data of the target wind turbine; the joint control data includes the excitation power cabinet temperature, unit load, reactive power and ambient humidity;

[0092] The prediction unit 202 is configured to input the joint control data into the trained dynamic weight prediction model to obtain a dynamic weight corresponding to each data output by the dynamic weight prediction model based on the correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target wind turbine;

[0093] A first determining unit 203 is configured to determine a comprehensive adjustment coefficient based on a dynamic weight corresponding to each type of data in the joint control data;

[0094] The second determining unit 204 is configured to determine a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed;

[0095] The control unit 205 is configured to control the target fan to operate at a target fan speed, and return to the step of obtaining the joint control data of the target fan.

[0096] In some embodiments of the present application, the first determining unit 203 is specifically configured to:

[0097] Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain a comprehensive adjustment coefficient.

[0098] In some embodiments of the present application, the first mapping relationship table includes mapping relationships between multiple comprehensive adjustment coefficient intervals and different fan speed adjustment methods; the second determination unit 204 is specifically configured to:

[0099] When the comprehensive coefficient is less than the first preset coefficient, the rated speed of the target fan is multiplied by the first proportion value to obtain the target fan speed;

[0100] When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, the target fan speed is determined according to a preset linear proportional relationship;

[0101] When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

[0102] In some embodiments of the present application, the apparatus may further include a training unit, the training unit being configured to:

[0103] Obtain the historical joint control data of the target fan and the actual energy consumption corresponding to the historical joint control data; the historical joint control data includes historical excitation power cabinet temperature, historical unit load, historical reactive power and historical ambient humidity;

[0104] Normalizing the historical joint regulation data to obtain normalized historical joint regulation data;

[0105] Input the normalized historical joint control data into the neural network model to be trained, and obtain the predicted dynamic weight corresponding to each type of historical data in the historical joint control data output by the neural network model, as well as the predicted energy consumption corresponding to the predicted dynamic weight;

[0106] Based on the predicted energy consumption, actual energy consumption, historical excitation power cabinet temperature and excitation power cabinet safety temperature, the loss value is calculated according to the preset loss function;

[0107] When the loss value is greater than or equal to the preset loss value, the neural network model is adjusted, and the step of inputting the normalized historical joint control data into the neural network model to be trained is returned until the loss value is less than the preset loss value, thereby obtaining a dynamic weight prediction model.

[0108] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0109] According to the embodiment of the present disclosure, a fan energy-saving control device for a unit excitation system based on multi-parameter dynamic adjustment is proposed. By obtaining the joint control data of the target fan; the joint control data includes the excitation power cabinet temperature, unit load, reactive power and ambient humidity; the joint control data is input into a trained dynamic weight prediction model, the dynamic weight prediction model outputs the dynamic weight corresponding to each data based on the correlation between each data in the joint control data and the energy-saving effect of the fan in the excitation system of the target fan; based on the dynamic weight corresponding to each data in the joint control data, a comprehensive adjustment coefficient is determined; based on a preset first mapping relationship table, a target fan speed matching the comprehensive adjustment coefficient is determined; the first mapping relationship table includes a mapping relationship between the comprehensive adjustment coefficient and the fan speed; the target fan is controlled to operate according to the target fan speed, and the step of obtaining the joint control data of the target fan is returned. The present disclosure dynamically adjusts the comprehensive control coefficient of the target fan by comprehensively adjusting the excitation power cabinet temperature, unit load, reactive power and ambient humidity, and dynamically adjusts the fan speed based on the comprehensive control coefficient, thereby improving the flexibility of the fan, effectively reducing the fan energy consumption while meeting the heat dissipation requirements.

[0110] Figure 3 This is a block diagram illustrating an apparatus for a fan energy-saving control method for a unit excitation system based on multi-parameter dynamic adjustment, according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0111] Reference Figure 3 , apparatus 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0112] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 302 may include one or more modules to facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.

[0113] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0114] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 300.

[0115] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0116] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.

[0117] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0118] The sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor assembly 314 can also detect changes in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and temperature changes of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0119] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0120] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0121] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by the processor 320 of the apparatus 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0122] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the apparatus 300 .

[0123] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow from the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0124] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A fan energy-saving control method for a unit excitation system based on multi-parameter dynamic adjustment, characterized in that: include: Acquire joint control data of the target fan; the joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity; Inputting the joint control data into the trained dynamic weight prediction model to obtain the dynamic weight corresponding to each data output by the dynamic weight prediction model based on the correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target wind turbine; Determining a comprehensive adjustment coefficient based on the dynamic weight corresponding to each type of data in the joint regulation data; Determining a target fan speed that matches the comprehensive adjustment coefficient based on a preset first mapping relationship table; The first mapping relationship table includes a mapping relationship between a comprehensive adjustment coefficient and a fan speed; The target fan is controlled to operate according to the target fan speed, and the process returns to the step of obtaining the joint control data of the target fan.

2. The fan energy-saving control method for the unit excitation system based on multi-parameter dynamic adjustment according to claim 1 is characterized in that: The determining of the comprehensive adjustment coefficient based on the dynamic weight corresponding to each type of data in the joint control data includes: Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain the comprehensive adjustment coefficient.

3. The fan energy-saving control method of the unit excitation system based on multi-parameter dynamic adjustment according to claim 1 is characterized in that: The first mapping relationship table includes mapping relationships between multiple comprehensive adjustment coefficient intervals and different fan speed adjustment methods; The determining, based on a preset first mapping relationship table, a target fan speed that matches the comprehensive adjustment coefficient includes: When the comprehensive coefficient is less than the first preset coefficient, multiplying the rated speed of the target fan by the first proportion value to obtain the target fan speed; When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, determining the target fan speed according to a preset linear proportional relationship; When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

4. The fan energy-saving control method for the unit excitation system based on multi-parameter dynamic adjustment according to claim 1 is characterized in that: The dynamic weight prediction model is trained by the following steps: Obtaining historical joint control data of the target wind turbine and actual energy consumption corresponding to the historical joint control data; the historical joint control data includes historical excitation power cabinet temperature, historical unit load, historical reactive power and historical ambient humidity; Normalizing the historical joint regulation data to obtain normalized historical joint regulation data; Inputting the normalized historical joint control data into the neural network model to be trained, obtaining the predicted dynamic weight corresponding to each type of historical data in the historical joint control data output by the neural network model, and the predicted energy consumption corresponding to the predicted dynamic weight; Calculating a loss value according to a preset loss function based on the predicted energy consumption, the actual energy consumption, the historical excitation power cabinet temperature, and the excitation power cabinet safety temperature; When the loss value is greater than or equal to the preset loss value, the neural network model is adjusted, and the step of inputting the normalized historical joint control data into the neural network model to be trained is returned to execute until the loss value is less than the preset loss value, thereby obtaining the dynamic weight prediction model.

5. A fan energy-saving control device for a unit excitation system based on multi-parameter dynamic adjustment, characterized in that: include: An acquisition unit is used to acquire joint control data of a target wind turbine; the joint control data includes excitation power cabinet temperature, unit load, reactive power and ambient humidity; A prediction unit, configured to input the joint control data into a trained dynamic weight prediction model, and obtain a dynamic weight corresponding to each data output by the dynamic weight prediction model based on a correlation between each data in the joint control data and the energy-saving effect of the excitation system of the target wind turbine; A first determining unit, configured to determine a comprehensive adjustment coefficient based on a dynamic weight corresponding to each type of data in the joint control data; a second determining unit, configured to determine a target fan speed matching the comprehensive adjustment coefficient based on a preset first mapping relationship table; The first mapping relationship table includes a mapping relationship between a comprehensive adjustment coefficient and a fan speed; A control unit is used to control the target fan to operate according to the target fan speed, and return to execute the step of obtaining the joint control data of the target fan.

6. The fan energy-saving control device for the unit excitation system based on multi-parameter dynamic adjustment according to claim 5 is characterized in that: The first determining unit is specifically configured to: Based on the dynamic weights corresponding to the excitation power cabinet temperature, unit load, reactive power and ambient humidity, the excitation power cabinet temperature, unit load, reactive power and ambient humidity are weighted and summed to obtain the comprehensive adjustment coefficient.

7. The fan energy-saving control device for the unit excitation system based on multi-parameter dynamic adjustment according to claim 5 is characterized in that: The first mapping relationship table includes mapping relationships between multiple comprehensive adjustment coefficient intervals and different fan speed adjustment methods; the second determining unit is specifically configured to: When the comprehensive coefficient is less than the first preset coefficient, multiplying the rated speed of the target fan by the first proportion value to obtain the target fan speed; When the comprehensive coefficient is greater than or equal to the first preset coefficient and less than the second preset coefficient, determining the target fan speed according to a preset linear proportional relationship; When the comprehensive coefficient is greater than the first preset coefficient, the full speed of the target fan is determined as the target fan speed.

8. The fan energy-saving control device for the unit excitation system based on multi-parameter dynamic adjustment according to claim 5 is characterized in that: The apparatus may further comprise a training unit configured to: Obtaining historical joint control data of the target wind turbine and actual energy consumption corresponding to the historical joint control data; the historical joint control data includes historical excitation power cabinet temperature, historical unit load, historical reactive power and historical ambient humidity; Normalizing the historical joint regulation data to obtain normalized historical joint regulation data; Inputting the normalized historical joint control data into the neural network model to be trained, obtaining the predicted dynamic weight corresponding to each type of historical data in the historical joint control data output by the neural network model, and the predicted energy consumption corresponding to the predicted dynamic weight; Calculating a loss value according to a preset loss function based on the predicted energy consumption, the actual energy consumption, the historical excitation power cabinet temperature, and the excitation power cabinet safety temperature; When the loss value is greater than or equal to the preset loss value, the neural network model is adjusted, and the step of inputting the normalized historical joint control data into the neural network model to be trained is returned to execute until the loss value is less than the preset loss value, thereby obtaining the dynamic weight prediction model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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