A frequency adjustment method, device, equipment and storage medium

Abstract

The present invention discloses a frequency regulation method, device, equipment and storage medium. The method is applied to a small hydropower microgrid system, which includes: a small hydropower generator and a controller. The frequency regulation method is executed by the controller, and the frequency regulation method includes: obtaining the current frequency and reference frequency of the generator; inputting the current frequency and reference frequency into a regulation amount prediction model to obtain a target regulation amount; sending the target regulation amount to the generator so that the generator adjusts the power generation according to the target regulation amount, and obtains the current frequency of the adjusted generator. Through the technical solution of the present invention, the system frequency can be adaptively adjusted to improve the frequency stability of the microgrid system.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power grid technology, and in particular to a frequency regulation method, device, equipment and storage medium. Background Art

[0002] At present, small hydropower has a high penetration rate in mountain distribution networks, and most of them generate electricity in maximum power mode. Due to the support of large power grids, the voltage and frequency of mountain distribution networks are relatively stable. When the main supply line is disconnected due to a fault, small hydropower units are mostly operated in a disconnected state, wasting water resources and failing to guarantee the reliability of power supply to local residents. The application of small hydropower microgrid systems can help improve the reliability of power supply after local loads are disconnected from the main power grid while saving power grid construction.

[0003] When the local load in the small hydropower microgrid changes, the frequency of the system changes. At this time, it is necessary to adjust the power generation of the generator of the hydropower unit to maintain the frequency stability of the microgrid system. Common frequency control modes of small hydropower units in the prior art include: First, the manual mode. Most small hydropower stations are not equipped with relevant frequency regulation controllers. They only adopt a manual control strategy of high-frequency water reduction and low-frequency water addition. This mode adopts the method of directly decoupling small hydropower units, which wastes water resources and cannot guarantee the power supply reliability of local residents; second, the automatic adjustment mode. Automatic frequency regulation equipment is installed in the small hydropower microgrid system and the traditional PID adjustment mode is generally adopted. When the automatic frequency regulation equipment is used, the problem of frequent adjustment and large frequency fluctuation often occurs. When the traditional PID adjustment mode is adopted, the fixed PID parameters cannot meet the control requirements of different operating conditions of the unit, and the problem of not being able to adjust the system frequency steadily and quickly when the operating point changes often occurs. Summary of the invention

[0004] The embodiments of the present invention provide a frequency regulation method, device, equipment and storage medium, which can solve the problem that when adjusting the frequency in the prior art, the frequency fluctuation is large and the fixed PID parameters cannot meet the control requirements of different working conditions of small hydropower units.

[0005] According to one aspect of the present invention, a frequency regulation method is provided, which is applied to a small hydropower microgrid system. The small hydropower microgrid system includes: a generator of a small hydropower unit and a controller. The frequency regulation method is executed by the controller. The frequency regulation method includes:

[0006] Get the current frequency and reference frequency of the generator;

[0007] Inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount;

[0008] The target adjustment amount is sent to the generator, so that the generator adjusts the power generation according to the target adjustment amount, and obtains the current frequency of the generator after adjustment.

[0009] According to another aspect of the present invention, there is provided a frequency adjustment device, the frequency adjustment device comprising:

[0010] A frequency acquisition module is used to obtain the current frequency and reference frequency of the generator;

[0011] An adjustment amount obtaining module, used for inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount;

[0012] The frequency adjustment obtaining module is used to send the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the generator after adjustment.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the frequency adjustment method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the frequency adjustment method described in any embodiment of the present invention when executed.

[0018] The embodiment of the present invention obtains the current frequency and reference frequency of the generator; inputs the current frequency and reference frequency into an adjustment amount prediction model to obtain a target adjustment amount; sends the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount, and obtains the current frequency of the adjusted generator. This solves the problem in the prior art that when adjusting the frequency, the frequency fluctuation is large and the fixed PID parameters cannot meet the control requirements of different working conditions of small hydropower units. The current frequency can be adaptively adjusted according to the reference frequency, thereby improving the frequency stability of the microgrid system, improving the power quality of the local load and improving the utilization rate of water resources.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a frequency adjustment method in Embodiment 1 of the present invention;

[0022] Figure 2 is a schematic diagram of an application adjustment amount prediction model in the first embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of another frequency adjustment method in Embodiment 1 of the present invention;

[0024] Figure 4 is a schematic diagram of a small hydropower microgrid system in Embodiment 1 of the present invention;

[0025] Figure 5 is a structural schematic diagram of a frequency adjustment device in Embodiment 2 of the present invention;

[0026] Figure 6 It is a structural schematic diagram of an electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a frequency regulation method in the first embodiment of the present invention. This embodiment is applicable to frequency regulation in a small hydropower microgrid system. This method can be executed by a frequency regulation device in the embodiment of the present invention. The device can be implemented in software and / or hardware. Figure 1 As shown, the method is applied to a small hydropower microgrid system, which includes: a generator and a controller of a small hydropower unit. The frequency adjustment method is executed by the controller and specifically includes the following steps:

[0032] S110, obtaining the current frequency and reference frequency of the generator.

[0033] The reference frequency is the given frequency value, that is, the frequency that the small hydropower unit should meet. The current frequency is the frequency obtained by the generator at regular intervals, and is also the frequency of the small hydropower microgrid system.

[0034] Specifically, periodic voltage and current data collection is performed regularly, such as calculating the current frequency according to the collected voltage and current data every 0.02s, and the reference frequency can be set according to actual needs, such as setting the reference frequency to 50Hz.

[0035] Optionally, get the current frequency of the generator, including:

[0036] Collect the current voltage and current of the generator;

[0037] The current voltage and current are calculated using a Fourier algorithm to obtain the current frequency of the generator.

[0038] Specifically, the current voltage and current of the generator are periodically measured by the voltage and current measuring elements.

[0039] Specifically, the current voltage and current can be processed by a discrete Fourier transform algorithm to obtain the frequency spectrum information of the voltage and current, and the current frequency of the generator can be determined by analyzing the frequency spectrum information. It should be noted that the current frequency of the generator can also be obtained by calculating the current frequency through the current voltage, the current current, and the phase difference between the current voltage and the current current.

[0040] The current frequency of the generator is obtained by collecting the current voltage and current current of the generator and calculating the current voltage and current current using a Fourier algorithm, thereby improving the accuracy of obtaining the current frequency.

[0041] S120, inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount.

[0042] The adjustment amount prediction model may be a trained recurrent network model. The target adjustment amount is the output result of the adjustment amount prediction model. Optionally, the adjustment amount prediction model includes: an input layer, a hidden layer, and an output layer, wherein the hidden layer includes at least one neuron and a delayer feedback connection, and the delayer records the hidden layer state at the previous moment.

[0043] Specifically, the current frequency and the reference frequency are used as inputs of the adjustment amount prediction model, and the output result is determined as the target adjustment amount.

[0044] Optionally, the current frequency and the reference frequency are input into an adjustment amount prediction model to obtain a target adjustment amount, including:

[0045] Inputting the current frequency and the reference frequency into the input layer of the adjustment amount prediction model, and inputting the output result of the input layer into the hidden layer;

[0046] The output result of each neuron in the hidden layer is obtained according to the preset tilt parameter in the hidden layer, the output result of the input layer, the state-state weight matrix, the hidden layer state at the previous moment, the state-input weight matrix and the preset bias vector, and the output result of each neuron is input into the output layer;

[0047] The output result of each neuron in the output layer is weighted and summed through the connection weight from the hidden layer to the output layer to obtain the target adjustment amount.

[0048] For example, it could be, Figure 2 is a schematic diagram of an application adjustment amount prediction model in the first embodiment of the present invention, such as Figure 2 As shown, the hidden layer of the adjustment prediction model includes three neurons and a delay device, which converts the reference frequency f ref And the current frequency f1 is input into the input layer of the adjustment amount prediction model, t is the cycle time for obtaining the current frequency, that is:

[0049] x(t)=[x i ] T ,i=1,2;

[0050] The input layer result is input into the hidden layer, and the output state of the hidden layer is h(t), h(t) = [h j (t)] T, j = 1, 2, 3, the output result h of each neuron in the hidden layer can be obtained according to the preset tilt parameters in the hidden layer, the output result of the input layer, the state-state weight matrix, the hidden layer state at the previous moment, the state-input weight matrix and the preset bias vector j (t), that is:

[0051]

[0052] Among them, z t is the net input of the hidden layer, z t =[z j ] T , a is the preset tilt parameter of the sigmoid function, whose range is [0,1], U is the state-state weight matrix, U=[U ij ], h(t-1) is the hidden layer state at the previous moment, W is the state-input weight matrix, W = [W ji ], b is the preset bias vector, b=[b j ] T ;

[0053] The output result of each neuron is input into the output layer, and the output result of each neuron is weighted summed in the output layer. The calculation formula is:

[0054]

[0055] Among them, y(t) is the target adjustment amount, ω j is the connection weight from the hidden layer to the output layer.

[0056] The target regulation amount is obtained through the regulation amount prediction model, which can meet the control requirements of different operating conditions of small hydropower microgrid units and improve the accuracy of the predicted target regulation amount.

[0057] S130, sending the target adjustment amount to the generator, so that the generator adjusts the power generation according to the target adjustment amount, and obtains the current frequency of the generator after adjustment.

[0058] Specifically, the controller sends the target adjustment amount to the generator, and the generator can adjust the unit output according to the target adjustment amount to meet the requirements of adjusting the power generation, and then affect the current frequency of the generator through the power generation of the generator, that is, the current frequency of the generator after adjustment. Figure 2 As shown, after the target regulation amount is obtained, the target regulation amount is sent to the turbine and the water diversion system to control the water flow size. The engine is controlled to adjust the power generation based on the water flow size and the load disturbance in the microgrid system, and then the current frequency f(t) after the new round of generator regulation is calculated.

[0059] Optionally, after sending the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount and obtains the adjusted current frequency of the generator, the method further includes:

[0060] adjusting the parameters of the adjustment amount prediction model according to the current frequency of the adjusted generator and the reference frequency;

[0061] Return to the operation of inputting the current frequency and the reference frequency into the adjustment amount prediction model to obtain the target adjustment amount, and send the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the adjusted generator, until the difference between the current frequency and the reference frequency is less than the difference threshold.

[0062] It should be noted that after sending the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount and obtains the current frequency of the adjusted generator, the parameters of the adjustment amount prediction model can continue to be optimized (it should be noted that the parameters of the model also need to be optimized when training the adjustment amount prediction model) to ensure that each adjustment frequency is infinitely close to the reference frequency.

[0063] The difference threshold can be set according to actual needs, and the difference threshold is a minimum value.

[0064] Specifically, the parameters of the adjustment amount prediction model are adjusted according to the error between the current frequency of the adjusted generator and the reference frequency, and the current frequency of the adjusted generator and the reference frequency are input into the adjustment amount prediction model after parameter adjustment, such as Figure 2 As shown, after obtaining the current frequency f(t) of the regulated generator, f(t) is repeatedly fed back to the input layer in the regulation amount prediction model to continue to obtain the target regulation amount, and the target regulation amount is sent to the generator, so that the generator adjusts the power generation according to the target regulation amount to obtain the current frequency of the regulated generator, until the difference between the current frequency and the reference frequency is less than the difference threshold.

[0065] Optionally, adjusting the parameters of the adjustment amount prediction model according to the adjusted current frequency of the generator and the reference frequency includes:

[0066] determining a target error according to the current frequency of the regulated generator and the reference frequency;

[0067] The parameters of the adjustment amount prediction model are corrected according to the target error using a gradient correction method, wherein the target weights include: a state-state weight matrix, a state-input weight matrix, and a connection weight from a hidden layer to an output layer.

[0068] Specifically, the calculation method for determining the target error according to the adjusted current frequency and reference frequency of the generator can be:

[0069] J = [f(t)-f ref ] 2 ;

[0070] Where J is the target error, f(t) is the current frequency of the generator after adjustment, and f ref is the reference frequency.

[0071] Specifically, the calculation method of correcting the parameters of the adjustment amount prediction model according to the target error using the gradient correction method can be:

[0072]

[0073]

[0074]

[0075] Among them, U ij is the state-state weight matrix, W ji is the state-input weight matrix, ω j is the connection weight from the hidden layer to the output layer, J is the target error, and η and η1 are both learning rates.

[0076] In a specific example, Figure 3 is another flow chart of frequency regulation in the first embodiment of the present invention. Figure 3 As shown, after initializing and acquiring the current frequency and the reference frequency, the current frequency and the reference frequency are input into the adjustment amount prediction model, the target adjustment amount obtained by the input layer is sent to the generator, the current frequency is adjusted by adjusting the power generation of the generator, and the parameters of the model are updated according to the error between the adjusted current frequency and the reference frequency, wherein the parameters include the state-state weight matrix, the state-input weight matrix, and the connection weights from the hidden layer to the output layer, and the adjusted current frequency is fed back to the input layer of the adjustment amount prediction model based on the updated model parameters, and the adjusted current frequency is continuously outputted until the difference between the adjusted current frequency and the reference frequency is less than the difference threshold.

[0077] By determining the target error according to the current frequency and reference frequency of the regulated generator and adjusting the parameters in the regulation amount prediction model based on the target error, the accuracy of the predicted frequency regulation amount can be improved when there is a load disturbance in the small hydropower microgrid, the current frequency after output regulation can be stabilized, and the stability of the microgrid system can be improved.

[0078] Optionally, sending the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the adjusted current frequency of the generator includes:

[0079] The target adjustment amount is input into the control motor to drive the rotation stroke of the electric speed regulator. The rotation stroke is driven by the gear to drive the connecting rod to control the turbine guide vane. The water flow is controlled by the turbine guide vane to adjust the power generation of the generator, so that the generator determines the current frequency of the adjusted generator according to the adjusted power generation.

[0080] Specifically, the controller first inputs the target adjustment amount into the rotation stroke of the control motor driving the electric speed regulator. The rotation stroke is driven by the gear to directly control the position of the turbine guide vane, and the guide vane controls the water flow to adjust the power generation of the generator, so that the voltage and current measuring elements measure the voltage and current after the power generation of the generator is adjusted, and the current frequency of the adjusted generator is obtained through the adjusted voltage and current. For example, it can be, Figure 4 is a schematic diagram of a small hydropower microgrid system in Embodiment 1 of the present invention, such as Figure 4 As shown, the small hydropower microgrid system includes a generator and a controller of a small hydropower unit. The small hydropower microgrid system also includes voltage and current measurement elements, a display device, a control motor, an electric speed governor and a turbine. The controller can be an STM32 single-chip microcomputer, that is, a main control module. The controller also includes: an input and output module, a storage module and a calculation module. The calculation module includes a frequency calculation unit and a regulation amount prediction model. Specifically, the voltage and current measuring element can measure the voltage and current of the generator and input them into the controller. The controller calculates the current frequency according to the frequency calculation unit and inputs the adjustment amount prediction model. The predicted target adjustment amount of the model and the current frequency after each adjustment can be stored in the storage module for subsequent operator search and use. The controller can send the predicted target adjustment amount of the model to the control motor, and the control motor drives the rotation stroke of the electric speed governor. The rotation stroke is driven by the gear to drive the connecting rod to directly control the position of the turbine guide vane, and the water flow is controlled by the guide vane to adjust the power generation of the generator. Then the voltage and current measuring element continues to measure the current and voltage of the generator after the power generation is adjusted, so as to calculate the current frequency after adjustment, and feed the adjusted current frequency back to the adjustment amount prediction model. The display device in the small hydropower microgrid system can display the contents stored in the storage module for the convenience of the operator to view.

[0081] The technical solution of this embodiment obtains the current frequency and reference frequency of the generator; inputs the current frequency and reference frequency into the adjustment amount prediction model to obtain the target adjustment amount; sends the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount, and obtains the current frequency of the adjusted generator. This solves the problem in the prior art that when adjusting the frequency, the frequency fluctuates greatly and the fixed PID parameters cannot meet the control requirements of different operating conditions of small hydropower units. It can adaptively adjust the current frequency according to the reference frequency, improve the frequency stability of the microgrid system, improve the power quality of the local load, and improve the utilization rate of water resources.

[0082] Embodiment 2

[0083] Figure 5 Schematic diagram of the structure of a frequency regulation device in the second embodiment of the present invention. This embodiment is applicable to the frequency regulation in a small hydropower microgrid system. The device can be implemented in software and / or hardware. The device can be integrated in any device that provides frequency regulation functions, such as Figure 5 As shown, the frequency adjustment device specifically includes: a frequency acquisition module 210, an adjustment amount acquisition module 220 and an adjustment frequency acquisition module 230.

[0084] The frequency acquisition module 210 is used to acquire the current frequency and reference frequency of the generator;

[0085] The adjustment amount obtaining module 220 is used to input the current frequency and the reference frequency into the adjustment amount prediction model to obtain the target adjustment amount;

[0086] The frequency adjustment obtaining module 230 is used to send the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the generator after adjustment.

[0087] Optionally, also include:

[0088] A parameter adjustment module, used for adjusting the parameters of the adjustment amount prediction model according to the current frequency of the adjusted generator and the reference frequency;

[0089] A feedback execution module is used to return to execute the operation of inputting the current frequency and the reference frequency into the adjustment amount prediction model to obtain the target adjustment amount, sending the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount, and obtaining the current frequency of the adjusted generator, until the difference between the current frequency and the reference frequency is less than the difference threshold.

[0090] Optionally, the frequency acquisition module is specifically used for:

[0091] Collect the current voltage and current of the generator;

[0092] The current voltage and current are calculated using a Fourier algorithm to obtain the current frequency of the generator.

[0093] Optionally, the adjustment amount prediction model includes: an input layer, a hidden layer and an output layer, wherein the hidden layer includes at least one neuron and a delayer feedback connection, and the delayer records the state of the hidden layer at the previous moment.

[0094] Optionally, the adjustment amount obtaining module is specifically used for:

[0095] Inputting the current frequency and the reference frequency into the input layer of the adjustment amount prediction model, and inputting the output result of the input layer into the hidden layer;

[0096] The output result of each neuron in the hidden layer is obtained according to the preset tilt parameter in the hidden layer, the output result of the input layer, the state-state weight matrix, the hidden layer state at the previous moment, the state-input weight matrix and the preset bias vector, and the output result of each neuron is input into the output layer;

[0097] The output result of each neuron in the output layer is weighted and summed through the connection weight from the hidden layer to the output layer to obtain the target adjustment amount.

[0098] Optionally, the parameter adjustment module is specifically used for:

[0099] determining a target error according to the current frequency of the regulated generator and the reference frequency;

[0100] The parameters of the adjustment amount prediction model are corrected according to the target error using a gradient correction method, wherein the target weights include: a state-state weight matrix, a state-input weight matrix, and a connection weight from a hidden layer to an output layer.

[0101] Optionally, the frequency adjustment module is specifically used for:

[0102] The target adjustment amount is input into the control motor to drive the rotation stroke of the electric speed regulator. The rotation stroke is driven by the gear to drive the connecting rod to control the turbine guide vane. The water flow is controlled by the turbine guide vane to adjust the power generation of the generator, so that the generator determines the current frequency of the adjusted generator according to the adjusted power generation.

[0103] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0104] Embodiment 3

[0105] Figure 6: is a structural diagram of an electronic device in Embodiment 3 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0106] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a frequency adjustment method.

[0109] In some embodiments, the frequency adjustment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the frequency adjustment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the frequency adjustment method in any other appropriate manner (e.g., by means of firmware).

[0110] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0112] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0114] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0115] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0116] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0117] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A frequency adjustment method, characterized in that: Applied to a small hydropower microgrid system, the small hydropower microgrid system includes: a generator and a controller of a small hydropower unit, the frequency adjustment method is executed by the controller, and the frequency adjustment method includes: Get the current frequency and reference frequency of the generator; Inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount; The target adjustment amount is sent to the generator, so that the generator adjusts the power generation according to the target adjustment amount, and obtains the current frequency of the generator after adjustment; The adjustment amount prediction model includes: an input layer, a hidden layer and an output layer, wherein the hidden layer includes three neurons and a delayer feedback connection, and the delayer records the hidden layer state at the previous moment; The step of inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount includes: The reference frequency and the current frequency Inputting the input layer of the adjustment amount prediction model, To obtain the cycle time of the current frequency, that is: ； The output result of the input layer is input into the hidden layer, and the output state of the hidden layer is , ; The output result of each neuron in the hidden layer is obtained according to the preset tilt parameter in the hidden layer, the output result of the input layer, the state-state weight matrix, the hidden layer state at the previous moment, the state-input weight matrix and the preset bias vector. ,Right now: ； in, is the net input of the hidden layer, , is the preset tilt parameter of the sigmoid function, and its value range is [0,1]. is the state-state weight matrix, , is the hidden layer state at the previous moment, is the state-input weight matrix, , is the preset bias vector, ; The output result of each neuron is input into the output layer, and the output result of each neuron in the output layer is weighted and summed through the connection weight from the hidden layer to the output layer to obtain the target adjustment amount, including: In the output layer, the output results of each neuron are weighted and summed, and the calculation formula is: ； in, is the target adjustment amount, is the connection weight from the hidden layer to the output layer.

2. The method according to claim 1, characterized in that After sending the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount and obtains the adjusted current frequency of the generator, the method further includes: adjusting the parameters of the adjustment amount prediction model according to the current frequency of the adjusted generator and the reference frequency; Return to the operation of inputting the current frequency and the reference frequency into the adjustment amount prediction model to obtain the target adjustment amount, and send the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the adjusted generator, until the difference between the current frequency and the reference frequency is less than the difference threshold.

3. The method according to claim 1, characterized in that Get the current frequency of the generator, including: Collect the current voltage and current of the generator; The current voltage and current are calculated using a Fourier algorithm to obtain the current frequency of the generator.

4. The method according to claim 2, characterized in that: Adjusting the parameters of the adjustment amount prediction model according to the adjusted current frequency of the generator and the reference frequency includes: determining a target error according to the current frequency of the regulated generator and the reference frequency; The parameters of the adjustment amount prediction model are corrected according to the target error using a gradient correction method, wherein the target weights include: a state-state weight matrix, a state-input weight matrix, and a connection weight from a hidden layer to an output layer.

5. The method according to claim 1, characterized in that The target adjustment amount is sent to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the generator after adjustment, including: The target adjustment amount is input into the control motor to drive the rotation stroke of the electric speed regulator. The rotation stroke is driven by the gear to drive the connecting rod to control the turbine guide vane. The water flow is controlled by the turbine guide vane to adjust the power generation of the generator, so that the generator determines the current frequency of the adjusted generator according to the adjusted power generation.

6. A frequency adjustment device, characterized in that: include: A frequency acquisition module is used to obtain the current frequency and reference frequency of the generator; An adjustment amount obtaining module, used for inputting the current frequency and the reference frequency into an adjustment amount prediction model to obtain a target adjustment amount; A frequency adjustment obtaining module is used to send the target adjustment amount to the generator so that the generator adjusts the power generation according to the target adjustment amount to obtain the current frequency of the generator after adjustment; The adjustment amount prediction model includes: an input layer, a hidden layer and an output layer, wherein the hidden layer includes three neurons and a delayer feedback connection, and the delayer records the hidden layer state at the previous moment; The adjustment amount obtaining module is specifically used to: and the current frequency Inputting the input layer of the adjustment amount prediction model, To obtain the cycle time of the current frequency, that is: ； The output result of the input layer is input into the hidden layer, and the output state of the hidden layer is , ; The output result of each neuron in the hidden layer is obtained according to the preset tilt parameter in the hidden layer, the output result of the input layer, the state-state weight matrix, the hidden layer state at the previous moment, the state-input weight matrix and the preset bias vector. ,Right now: ； in, is the net input of the hidden layer, , is the preset tilt parameter of the sigmoid function, and its value range is [0,1]. is the state-state weight matrix, , is the hidden layer state at the previous moment, is the state-input weight matrix, , is the preset bias vector, ; The output result of each neuron is input into the output layer, and the output result of each neuron in the output layer is weighted and summed through the connection weight from the hidden layer to the output layer to obtain the target adjustment amount, including: In the output layer, the output results of each neuron are weighted and summed, and the calculation formula is: ； in, is the target adjustment amount, is the connection weight from the hidden layer to the output layer.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the frequency adjustment method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the frequency adjustment method according to any one of claims 1 to 5 when executed.

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