Doubly-fed variable-speed pumped storage unit control method and device based on model prediction

Abstract

The invention provides a doubly-fed variable-speed pumped storage unit control method and device based on model prediction, and relates to the technical field of data processing. The doubly-fed variable-speed pumped storage unit control method based on model prediction comprises the steps that a pump turbine model, a doubly-fed motor model and a water diversion system model corresponding to a target doubly-fed variable-speed pumped storage unit are constructed; establishing a pump turbine control strategy, and establishing a doubly-fed motor control strategy based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter; substituting the error equation into a target function to generate a quadratic cost function, and solving an optimal analysis solution of a control variable under a model prediction control framework to generate a dynamically optimized control parameter; and adjusting the target function and the control parameters in real time by using a pump turbine control strategy and a doubly-fed motor control strategy. According to the technical scheme, the control accuracy and adaptability of the doubly-fed variable-speed pumped storage unit can be improved.

Description

Background Art

[0002] In the context of global energy transformation, the large-scale access of wind power, solar power and other highly volatile renewable energy sources to the power grid has brought new challenges to the stability and regulation capabilities of the power system. To meet these challenges, double-fed variable-speed pumped storage units have gradually become an important technology for improving grid stability due to their ability to quickly respond to grid frequency and power fluctuations. However, existing technologies still face some problems in the practical application of double-fed variable-speed pumped storage units.

[0003] First, the accuracy of existing control strategies is insufficient. The dynamic characteristics of doubly-fed variable-speed pumped storage units are complex and are affected by multiple factors such as hydraulic, electromagnetic and mechanical characteristics. These factors are coupled with each other and are difficult to accurately model. Traditional physical models and empirical models are often unable to fully and accurately describe the dynamic behavior of the unit in practical applications, resulting in large deviations under complex working conditions, which affects the control effect.

[0004] Secondly, the existing control strategies are not adaptable enough. The operating environment of modern power grids is complex and changeable, including load fluctuations, frequency changes, and short-term weather changes, which puts higher demands on the real-time adjustment capabilities of the units. However, the existing control methods mostly rely on fixed parameters and lack adaptive adjustment capabilities. They are difficult to cope with dynamic changes in operating conditions and cannot achieve the best control effect in a changing environment.

[0005] Therefore, there is still room for improvement in the accuracy and adaptability of the existing double-fed variable-speed pumped storage unit control technology.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute relevant technology known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the embodiments disclosed herein is to provide a doubly-fed variable-speed pumped-storage unit control method based on model prediction, a doubly-fed variable-speed pumped-storage unit control device based on model prediction, an electronic device and a computer-readable storage medium, thereby improving the accuracy and adaptability of the doubly-fed variable-speed pumped-storage unit control technology at least to a certain extent.

[0008] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0009] According to the first aspect of an embodiment of the present disclosure, a control method for a doubly-fed variable-speed pumped storage unit based on model prediction is provided, the method comprising: constructing a pump-turbine model, a doubly-fed motor model and a water diversion system model corresponding to the target doubly-fed variable-speed pumped storage unit based on data drive; establishing a pump-turbine control strategy based on turbine closed-loop regulation and pump open-loop regulation, and establishing a doubly-fed motor control strategy based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter; generating a quadratic cost function by substituting an error equation into an objective function and combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, and using the quadratic cost function to solve the analytical optimal solution of the control variables under the model predictive control framework to generate dynamically optimized control parameters; and using the pump-turbine control strategy and the doubly-fed motor control strategy to adjust the objective function and the control parameters in real time.

[0010] According to a second aspect of an embodiment of the present disclosure, a control device for a doubly-fed variable-speed pumped storage unit based on model prediction is provided, the device comprising: a dynamic model building module, for building a pump-turbine model, a doubly-fed motor model and a water diversion system model corresponding to a target doubly-fed variable-speed pumped storage unit based on data drive; a control strategy building module, for establishing a pump-turbine control strategy based on turbine closed-loop regulation and pump open-loop regulation, and establishing a doubly-fed motor control strategy based on DC bus voltage control of a grid-side converter and rotor voltage control of a rotor-side converter; a dynamic optimization control module, for generating a quadratic cost function by substituting an error equation into an objective function, combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, and using the quadratic cost function to solve the analytical optimal solution of the control variables under a model predictive control framework to generate dynamically optimized control parameters; a control parameter adjustment module, for adjusting the objective function and the control parameters in real time using the pump-turbine control strategy and the doubly-fed motor control strategy.

[0011] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned model-prediction-based double-fed variable-speed pumped storage unit control method is implemented.

[0012] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the control method of a double-fed variable-speed pumped storage unit based on the above-mentioned model prediction is implemented.

[0013] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the exemplary embodiment of the present disclosure, the control method of the double-fed variable speed pumped storage unit based on model prediction, on the one hand, adopts a data-driven method to construct a pump turbine model, a double-fed motor model and a water diversion system model, which can more accurately reflect the dynamic characteristics of the unit in actual operation. This modeling method can comprehensively consider the hydraulic, electromagnetic and mechanical characteristics of the system, and reduce the errors caused by changes in physical parameters in the traditional model. On the other hand, by adopting a closed-loop control strategy under the turbine working condition and an open-loop control strategy under the pump working condition, the control requirements under different working conditions can be optimized respectively, thereby achieving accurate water flow regulation and power control. In addition, based on the control strategy of the grid-side converter and the rotor-side converter, the motor power flow and rotor voltage can be flexibly adjusted within a larger range, which improves the dynamic response capability and regulation performance of the unit and ensures the stability of the motor under different working conditions. On the other hand, the error equation is substituted into the objective function and combined with each system model to generate a quadratic cost function, which can optimize the system error and control increment, thereby minimizing the change of the control input while meeting the system control requirements, and improving the stability and control accuracy of the system. By solving the analytical optimal solution of the quadratic cost function through model predictive control, dynamic optimization of control parameters can be achieved, so that control parameters can be adjusted in real time according to the current and predicted system status, enhancing the system's adaptability. In addition, during actual operation, by real-time monitoring and adjustment of objective functions and control parameters, the system can adapt to complex environments such as load fluctuations and frequency changes, and achieve precise control of power and speed. This real-time adjustment mechanism enhances the system's adaptability, enabling it to maintain efficient operation in a complex power grid environment, improves power grid stability, and maximizes the unit's regulation capability.

[0014] 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 present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0016] Figure 1 A flow chart of a control method for a double-fed variable-speed pumped storage unit based on model prediction according to some embodiments of the present disclosure is schematically shown.

[0017] Figure 2 The overall flow chart of a control method for a double-fed variable-speed pumped storage unit based on model prediction according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 3 A schematic diagram of a full flow characteristic curve according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 4 A schematic diagram of a full torque characteristic curve according to some embodiments of the present disclosure is schematically shown.

[0020] Figure 5 A control strategy block diagram of a double-fed variable-speed pumped storage unit according to some embodiments of the present disclosure is schematically shown.

[0021] Figure 6 A schematic diagram of a control strategy for closed-loop regulation of a water turbine according to some embodiments of the present disclosure is schematically shown.

[0022] Figure 7 A schematic diagram of a control strategy for open-loop regulation of a water pump according to some embodiments of the present disclosure is schematically shown.

[0023] Figure 8 A GSC control block diagram in a doubly-fed motor control strategy according to some embodiments of the present disclosure is schematically shown.

[0024] Fig. 9 A RSC control block diagram in a doubly-fed motor control strategy according to some embodiments of the present disclosure is schematically shown.

[0025] Fig.10 A schematic diagram of power distribution of a doubly-fed variable-speed pumped storage unit according to some embodiments of the present disclosure is shown schematically.

[0026] Fig.11 A schematic diagram of the speed optimization results of a doubly-fed variable-speed pumped storage unit according to some embodiments of the present disclosure is schematically shown.

[0027] Fig.12 A block diagram of a data classification device according to some embodiments of the present disclosure is schematically shown.

[0028] Fig.13 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0029] Fig.14 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0030] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0031] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0032] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0033] Existing pumped storage units usually adopt a fixed speed operation mode, and their regulation performance is relatively limited, which cannot meet the requirements of modern power grids for flexibility and rapid response. For this reason, doubly fed variable speed pumped storage units came into being. By introducing doubly fed motors and inverters, doubly fed variable speed units allow the units to operate in a wider speed range, thereby providing higher dynamic regulation capabilities. However, doubly fed variable speed pumped storage units still face the following challenges in actual operation.

[0034] First, the limitation of dynamic performance. Existing control strategies, such as PID control, often show problems such as response lag and overshoot when dealing with complex nonlinear systems and rapidly changing operating conditions in double-fed variable-speed pumped storage units. It is difficult to achieve precise control in a short time and cannot give full play to the regulation ability of the unit.

[0035] Secondly, the model accuracy is insufficient. The dynamic characteristics of the doubly-fed variable-speed pumped storage unit are affected by multiple factors such as hydraulic characteristics, electromagnetic characteristics and mechanical characteristics. These factors are coupled with each other, making the model complex and difficult to describe accurately. Traditional physical models and empirical models cannot fully and accurately reflect the dynamic behavior of the unit, resulting in large control deviations under different operating conditions, which affects the control effect of the unit.

[0036] In addition, data is not fully utilized. With the advancement of sensor technology and data acquisition systems, a large amount of real-time data is generated during the operation of the unit. However, this data has not been fully utilized and has not been effectively transformed into a means to improve control performance. Existing control methods cannot fully utilize real-time data for adaptive adjustments, which limits the dynamic response capability of the unit.

[0037] Finally, poor adaptability to complex environments. The operating environment of modern power grids is complex and changeable, including load fluctuations, frequency changes, short-term weather changes, etc., which puts higher demands on the control system of the unit. Existing control methods have poor adaptability when dealing with changing environments, cannot achieve optimal control in complex working conditions, and are difficult to ensure grid stability and regulation efficiency.

[0038] In order to solve some or all of the technical problems in the above-mentioned related technologies, in an exemplary embodiment of the present disclosure, a control method for a double-fed variable speed pumped storage unit based on model prediction is firstly proposed. Figure 1 The flowchart of a control method of a double-fed variable speed pumped storage unit based on model prediction according to some embodiments of the present disclosure is schematically shown. Figure 1 As shown, the control method of the double-fed variable speed pumped storage unit based on model prediction may include the following steps: Step S110, constructing a pump-turbine model, a doubly-fed generator model and a water diversion system model corresponding to the target doubly-fed variable-speed pumped storage unit based on data drive; Step S120, establishing a pump-turbine control strategy based on turbine closed-loop regulation and pump open-loop regulation, and establishing a doubly-fed machine control strategy based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter; Step S130, by substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model, a quadratic cost function is generated, and the quadratic cost function is used to solve the analytical optimal solution of the control variable under the model predictive control framework to generate dynamically optimized control parameters; Step S140: Use the pump-turbine control strategy and the doubly-fed generator control strategy to adjust the objective function and control parameters in real time.

[0039] This method constructs the target model of the doubly-fed variable-speed pumped storage unit based on the data-driven method, which specifically includes the construction of a pump-turbine model, a doubly-fed motor model and a water diversion system model to fully capture the hydraulic, electromagnetic and mechanical characteristics of the system and improve the accuracy of the model's description of the actual working conditions. Then, the control strategy of the pump-turbine is established based on the closed-loop regulation characteristics of the turbine and the open-loop regulation characteristics of the pump. At the same time, the control strategy of the doubly-fed motor is established based on the DC bus voltage control of the grid-side converter and the rotor voltage control of the rotor-side converter to achieve accurate regulation of the pump-turbine flow and motor speed and power. On this basis, by substituting the control error equation of the system into the objective function, combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, a quadratic cost function is generated, and the quadratic cost function is optimized and solved under the model predictive control (MPC) framework to obtain the analytical optimal solution of the control variables, thereby generating dynamically optimized control parameters to adapt to changes in real-time working conditions. Finally, the pump-turbine control strategy and the doubly-fed motor control strategy are used to dynamically adjust the objective function based on the generated control parameters, and the control input is updated in real time, so that the system can achieve adaptive optimization control according to actual grid conditions such as load fluctuations and frequency changes, ensuring the stability and rapid response capability of the unit in a complex grid environment.

[0040] Next, the control method of the double-fed variable speed pumped storage unit based on model prediction in the above exemplary embodiment will be further described.

[0041] In step S110, a pump-turbine model, a doubly-fed motor model and a water diversion system model corresponding to the target doubly-fed variable-speed pumped-storage unit are constructed based on data drive.

[0042] Among them, data-driven can represent a technical method for building a system model based on historical data and real-time data. By using the operating data of the double-fed variable-speed pumped storage unit, applying machine learning, neural network and other algorithms, a mathematical model that can describe the dynamic behavior of the double-fed variable-speed pumped storage unit is generated to achieve accurate modeling and adapt to changes in different working conditions. The double-fed variable-speed pumped storage unit can represent a pumped storage device that uses double-fed motors and inverter technology, which can operate at a wide range of speeds, thereby achieving flexible power regulation and rapid response to grid frequency fluctuations, and is used to improve the stability and regulation capabilities of the power system.

[0043] The pump-turbine model can represent a mathematical model based on the dynamic characteristics and working state of the pump-turbine. The model is based on parameters such as unit speed, unit flow, unit torque and guide vane opening to describe the changing relationship between the flow, power and efficiency of the pump-turbine under different working conditions, and is used to achieve optimal control of water flow regulation. The doubly-fed motor model can represent a mathematical model used to describe the electromagnetic characteristics and power dynamic characteristics of the doubly-fed motor. Based on the dq coordinate system, by analyzing the coupling relationship between the stator and the rotor, the response characteristics of the doubly-fed motor under different speeds and load conditions can be described by the relationship between voltage, current, torque and power, and is used to achieve motor speed control and grid power regulation. The water diversion system model can represent a model built based on elastic water hammer characteristics, which is used to describe the dynamic behavior of water flow in the water diversion pipeline, taking into account the inertia, reflection time and head loss factors of the water flow to ensure the stability of the pumped storage unit under the pressure fluctuation conditions of the hydraulic system.

[0044] In some embodiments, the construction process of the pump-turbine model includes: first, based on the nonlinear relationship between the unit speed and unit flow rate and the guide vane opening of the pump-turbine, a full flow characteristic curve is determined. Figure 3 A schematic diagram of a flow rate full characteristic curve is schematically shown, wherein the abscissa represents the unit rotation speed and the ordinate represents the unit flow rate.

[0045] Then, based on the nonlinear relationship between the unit speed and unit torque of the pump-turbine and the guide vane opening, the full torque characteristic curve is determined. Figure 4 A schematic diagram of a torque full characteristic curve is schematically shown, wherein the abscissa represents the unit speed and the ordinate represents the unit torque.

[0046] Next, based on the operating parameters of the pump-turbine, the conversion relationship equations between unit speed, unit torque and unit flow and their actual values ​​are established: ; in, Indicates the unit speed, Indicates the actual speed. Indicates the nominal diameter of the pump turbine. Indicates water head, represents unit torque, Indicates the actual torque, represents unit flow rate, and Q represents actual flow rate.

[0047] In some embodiments, the process of constructing the doubly-fed motor model includes: determining active and reactive dynamic equations corresponding to the doubly-fed motor based on the voltage and current relationship of the doubly-fed motor: ; in, Indicates the active power on the stator side,t Indicates time, represents the mechanical inertia constant, represents the stator voltage, represents the coupling inductance between the stator and the rotor, represents the magnetic flux component of the rotor on the d-axis in the dq reference frame, Indicates the reactive power on the stator side, It represents the magnetic flux component of the rotor on the q-axis in the dq reference frame.

[0048] Combining the active and reactive dynamic equations with the relationship equations between the voltage and current on the rotor side, the doubly fed machine model is obtained: ; in, represents the slip angular velocity, represents the voltage component of the rotor on the d-axis in the dq reference frame, represents the voltage component of the rotor on the q-axis in the dq reference frame, is the rotor inductance, Indicates the synchronous angular velocity.

[0049] Specifically, in the process of establishing the doubly-fed machine model, first, the synchronous dq reference system is used to represent the doubly-fed machine, and the stator voltage is selected as the reference direction so that the stator voltage is aligned with the d-axis. Through the projection relationship of the stator voltage on the dq axis, the expression of the stator voltage and the magnetic flux is obtained: The d-axis voltage on the stator side ,in Indicates the stator voltage amplitude; stator side q-axis voltage ; In the magnetic flux, the d-axis flux and q-axis flux ,in Indicates the synchronous angular velocity.

[0050] Then, based on the above voltage and magnetic flux relationship, the expression of stator side current is derived: d-axis current and q-axis current ,in, represents the leakage coefficient, represents the stator inductance, is the rotor inductance, Represents the coupled inductance between the stator and the rotor.

[0051] Next, assuming that the rotor side resistance is negligible, the change of the rotor flux is completely controlled by the applied rotor voltage. The change of the rotor flux directly affects the active power and reactive power on the stator side through the relationship between voltage and current, which is expressed as: d-axis rotor voltage and q-axis rotor voltage ,in and denote the rotor voltage on the d-axis and q-axis respectively, and They represent the rotor flux on the d-axis and q-axis respectively.

[0052] Secondly, by analyzing the relationship between the voltage and current of the doubly-fed generator, the expressions of active power and reactive power are obtained: Active power of doubly-fed generator , reactive power .Will Substituting the active power and reactive power expressions and simplifying them, we get the active power and reactive power .

[0053] Finally, the first-order derivative of the simplified power expression is taken to obtain the dynamic equations of active power and reactive power: Dynamic equation of active power Dynamic equations for reactive power ,in , which represents the proportionality coefficient, is related to the inductance parameters of the stator and rotor. The obtained dynamic equations of active and reactive power are combined with the voltage and current relationship on the rotor side to establish a doubly fed machine model, which is used to describe the dynamic behavior of the doubly fed machine in the dq reference frame.

[0054] In some embodiments, the process of constructing the water diversion system model includes: constructing the water diversion system model based on approximate elastic water hammer characteristics: ; in, represents the transfer function of the water diversion system, is the time constant representing the inertia of water, represents the Laplace operator, Indicates the reflection time of water hammer pressure, Represents the head loss coefficient.

[0055] In step S120, a pump-turbine control strategy is established based on turbine closed-loop regulation and pump open-loop regulation, and a doubly-fed machine control strategy is established based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter.

[0056] Among them, the closed-loop regulation of the turbine can represent a control method that monitors and adjusts the operating status of the turbine in real time based on the feedback control mechanism. By collecting parameters such as speed and output power in real time, the actual state is compared with the set target value, and the guide vane opening is dynamically adjusted according to the error to achieve precise control of the water flow. The open-loop regulation of the pump can represent a control method that does not rely on feedback signals. The water flow of the pump is adjusted by presetting the guide vane opening. The open-loop regulation of the pump usually does not require real-time monitoring and adjustment. The guide vane opening is set according to the operating requirements of the pump working condition to simplify the control process and achieve stable operation. The pump-turbine control strategy can represent the control scheme for the pump-turbine under the pump working condition and the turbine working condition. This control strategy combines the closed-loop regulation of the turbine and the open-loop regulation of the pump. By reasonably adjusting the guide vane opening, efficient management of the water flow is achieved, thereby meeting the regulation requirements under different working conditions.

[0057] The grid-side converter (GSC) can represent the converter equipment located on the grid side of the doubly-fed generator. It adjusts the DC bus voltage through vector control and provides reactive power support to the grid when necessary to maintain grid voltage stability and control of motor power flow. The DC bus voltage can represent the voltage on the DC bus in the power system. It is used as one of the key parameters for converter control and is usually adjusted through vector control of the grid-side converter to ensure power balance and grid voltage stability in the doubly-fed generator system. The rotor-side converter (RSC) can represent the converter equipment connected to the rotor of the doubly-fed generator. It adjusts the output power and speed of the motor by controlling the rotor voltage. It can use a combination of model predictive control and vector control to ensure the dynamic response capability of the motor under different load and frequency conditions. The doubly-fed motor control strategy can represent a control scheme for adjusting the power and speed of the doubly-fed motor. The control strategy is based on the DC bus voltage control of the grid-side converter and the rotor voltage control of the rotor-side converter. It achieves precise adjustment of the motor through methods such as vector control and feedback control to adapt to grid frequency fluctuations and load changes.

[0058] In some embodiments, the motor speed can be controlled by the double-fed motor control system, and the power control strategy can be controlled by the pump turbine control system to improve the power response speed of the unit. Figure 5 The control strategy of the double-fed variable speed pumped storage unit is shown in the figure. The specific technical process is as follows: First, the control system receives the active reference value and reactive reference value required by the power grid, which determine the output target of the unit. Then, the reactive reference value is input to the grid-side converter controller (GSC controller). The GSC controller adjusts the grid-side converter according to the reactive reference value to meet the grid's demand for reactive power. By controlling the grid-side converter, the doubly-fed generator provides reactive support to the grid.

[0059] Next, in the doubly-fed generator control system, the rotor-side converter controller (RSC controller) is a core component that includes a model predictive controller. The RSC controller uses the doubly-fed generator model and the water system turbine model, and combines the active reference value and the speed reference value to adjust the rotor voltage and current in real time to keep the motor speed within the preset range, thereby ensuring that the unit output power meets the needs of the power grid. The controller predicts the future system state through the doubly-fed generator model and uses the Model Predictive Control (MPC) algorithm to generate control inputs to achieve dynamic adjustment.

[0060] At the same time, the active reference value is processed by the optimized speed module to generate the speed reference value of the motor. The speed reference value is converted into the active reference value on the stator side by the reference conversion module, and then sent to the RSC controller. In addition, the speed reference value is compared with the actual speed to generate a deviation signal, which is fed back to the optimized guide vane opening module to adjust the guide vane opening of the hydraulic controller, thereby realizing the adjustment of the pump turbine power.

[0061] Subsequently, the hydraulic controller controls the guide vane opening of the pump-turbine by optimizing the guide vane opening module. The guide vane opening directly affects the power output of the pump-turbine, thereby achieving the required active power control.

[0062] Finally, the double-fed generator adjusted by the GSC controller and the RSC controller is connected to the grid to realize the control of the active and reactive power of the grid to ensure the stability of the grid. By coordinating the speed of the double-fed generator and the power output of the pump-turbine, the control strategy can meet the dynamic power demand of the grid and significantly improve the response speed and adaptability of the system.

[0063] In some embodiments, a pump-turbine control strategy is established based on closed-loop regulation of the turbine and open-loop regulation of the pump, including: under turbine operating conditions, using a closed-loop control strategy to adjust water flow through guide vanes, real-time monitoring of the turbine speed and output power, and obtaining actual state data; comparing the actual state data with a preset target value, generating a deviation signal, and dynamically adjusting the guide vane opening using the deviation signal; under pump operating conditions, using an open-loop control strategy to adjust water flow through a preset guide vane opening, and setting the guide vane opening according to the operating requirements of the pump; using a closed-loop control strategy and an open-loop control strategy to control the flow of the turbine and the pump, respectively, to construct a pump-turbine control strategy.

[0064] refer to Figure 6 As shown in the control strategy of closed-loop regulation of the turbine, first, the control system receives the active power reference value provided by the grid demand or load requirement. , which represents the target output power that the turbine needs to achieve. Then, the power reference value Input to the optimal guide vane opening generator module, in which the corresponding optimal guide vane opening is calculated according to the power demand The optimal guide vane opening is used as the initial reference opening to achieve rough control of the turbine flow. Then, the control system calculates the actual guide vane opening of the turbine. and the optimal guide vane opening Compare and generate the opening deviation signal (i.e. ), and inputs this deviation signal to the PID control module. The PID control module performs proportional-integral-differential operations based on the deviation signal and dynamically adjusts the guide vane opening to reduce the deviation. PID control can respond to deviation changes in real time, and generate more accurate guide vane opening control instructions by correcting the deviation signal. Then, the signal after PID control is transmitted to the servo control system, which is responsible for actually performing the guide vane opening adjustment operation to achieve precise control of the amount of water flowing into the turbine. This process enables the turbine speed and output power to be quickly adjusted to meet the reference value Finally, the control system monitors the speed and output power of the turbine in real time, and compares these actual status data with the preset target value to generate a new deviation signal. The deviation signal is input into the PID control module again to achieve closed-loop control, thereby ensuring that the guide vane opening can be dynamically adjusted according to the actual working conditions, so as to ensure that the turbine operates in a stable and efficient state and achieve high-quality power support for the power grid.

[0065] refer to Figure 7 As shown in the control strategy of the water pump open-loop regulation, first, the control system receives the active power reference value , which represents the target water absorption and pumping power requirement of the pump under the current working conditions. This reference value enters the "optimal guide vane opening generator" module, and the optimal guide vane opening is preset according to the operating requirements of the pump. This opening It is calculated based on the pump operating conditions and water intake requirements to ensure that the power output meets the current operating conditions. Then, the preset guide vane opening Directly input to the PID control module. Under the pump working condition, the PID control module does not need to perform complex real-time feedback adjustment, but only adjusts according to the preset opening Simple adjustments are made to ensure that the guide vane opening remains at the predetermined set value. This open-loop control method has small flow fluctuations under the working conditions of the pump and can meet the control requirements. Next, the PID control signal is transmitted to the servo control system, and the servo control system adjusts the guide vane opening according to the PID output signal to adjust the actual water flow of the pump. Since the operating state of the pump is insensitive to small-scale flow fluctuations, this open-loop control method can stably control the guide vane opening without real-time feedback. Finally, the water flow into the pump is set within the expected range by the guide vane opening adjusted by the servo control system, so that the water suction and pumping power of the pump turbine are met. This open-loop control strategy simplifies the control system and does not require complex real-time data processing, ensuring that the pump operates efficiently in a relatively stable state.

[0066] A comprehensive pump-turbine control strategy is constructed by adopting a closed-loop control strategy to accurately control the flow of the turbine and an open-loop control strategy to fixedly adjust the flow of the pump. This strategy can efficiently and stably meet the flow control requirements under different operating conditions, thereby improving the response speed and stability of the unit to the power demand of the grid.

[0067] In some embodiments, a doubly-fed motor control strategy is established based on the DC bus voltage control of the grid-side converter and the rotor voltage control of the rotor-side converter, including: using the grid-side converter to perform vector control, and controlling the d-axis and q-axis currents respectively by decoupling the active power and reactive power of the motor to achieve regulation of the DC bus voltage and generate a control reference value for the power flow between the motor and the grid; using the rotor-side converter to adjust the rotor voltage of the motor, and controlling the d-axis current and q-axis current on the rotor side based on the dq-axis model to generate control inputs for the active power and reactive power of the motor; based on the model predictive control method, predicting the changing trend of the rotor current, and optimizing the control input based on the prediction result to dynamically adjust the rotor voltage.

[0068] Among them, the model predictive control method can represent a control method based on a dynamic system model, which optimizes the control input to meet the desired control target by predicting the future behavior of the system at each control moment. This method uses the current state of the system and the prediction model to calculate the system output in the future, construct the objective function, and obtain the optimal control input by solving the objective function. The following is a detailed description of the doubly fed motor control strategy: refer to Figure 8 As shown in the figure, in the DC bus voltage control process based on the grid-side converter (GSC), the GSC control system receives the DC bus voltage reference value and reactive power reference As input signal. The DC bus voltage reference value is used to control the stability of the DC bus voltage, and the reactive power reference value is used to adjust the reactive power to meet the grid demand. The actual measured DC bus voltage With reference value The error signal is sent to the PID controller to generate the d-axis current reference value. , thereby stabilizing the DC bus voltage. Reactive power reference value The q-axis current reference value is generated by the transformation module , used to control reactive power.

[0069] Actual values ​​of d-axis and q-axis currents and Respectively with their reference values and The current error signals are input to the PID control module and the d-axis and q-axis voltages are obtained after adjustment. and The d-axis voltage and q-axis voltage are used to control the output of the grid-side converter to manage the active and reactive power of the motor.

[0070] d-axis voltage and q-axis voltage The expression is: ; ; in, represents the motor inductance, represents the motor resistance, represents the synchronous angular velocity of the motor, and Represent the reference voltage signals of the d-axis and q-axis respectively, and Represent the actual values ​​of the d-axis and q-axis currents, respectively. t Indicates time.

[0071] The dynamic equation of the DC bus voltage is: ; in, represents the DC capacitance, is the DC bus current, Indicates the motor stator voltage amplitude, Indicates the DC bus voltage.

[0072] The control system contains three PID control modules, which are used for adjusting the DC bus voltage, controlling the d-axis current and controlling the q-axis current. First, the DC bus voltage control module is used to maintain the DC bus voltage Stable. The actual measured DC bus voltage With its reference value The voltage error signal is generated by comparison. This error signal passes through the first PID controller and outputs the d-axis current reference value. , to adjust the stability of the DC bus voltage. Secondly, the second PID control module is used to control the d-axis current. The actual measured d-axis current and d-axis current reference value The error signal is processed by the PID controller to generate the d-axis voltage , used to control the grid-side converter so that the motor active power control meets the target value. The third PID control module is used for q-axis current control. Reactive power reference value The q-axis current reference value is obtained by conversion The actual measured q-axis current and q-axis current reference value After comparison, a current error signal is generated, which is processed by a PID controller to output the q-axis voltage , used to adjust the reactive power output of the grid-side converter. The d-axis and q-axis voltages and After calculation, it is passed to the coordinate conversion module (dq-abc) and converted into a three-phase voltage signal And three-phase current signal , the voltage component on the output d-axis The voltage component on the q axis , and the current component on the d-axis and the current component on the q axis , driving the grid-side converter to perform the actual current regulation.

[0073] SRF-PLL module is used to lock the grid angle and synchronous angular velocity , so that the dq coordinate system is synchronized with the power grid. By controlling the d-axis current Realize active power regulation and control q-axis current Realize reactive power regulation and achieve the control target of power flow between motor and grid.

[0074] refer to Fig. 9 As shown in the figure, in the rotor voltage control process of the rotor side converter (RSC), the rotor side converter is used to adjust the rotor voltage of the doubly fed generator to achieve the active power of the motor. and reactive power The whole process adopts the data-driven model predictive control method, which predicts the future current change trend, calculates and optimizes the control input in advance, and thus realizes the precise regulation of the rotor current.

[0075] First, the RSC control system receives the target active power and reactive power As reference value input, it is passed to the optimization control strategy module. The optimization control strategy module uses these reference values and , combined with the current system state, generates a control input increment , used to dynamically adjust the rotor voltage. This control input increment By adjusting the RSC, the actual power output of the motor is made close to the target power level.

[0076] In the framework of model predictive control, the system performs control based on the dq axis model of the doubly fed machine. The doubly fed machine model is composed of The parameter matrices are defined, which together describe the dynamic characteristics of the motor in the dq coordinate system. These parameter matrices are constructed through a data-driven approach, which can reflect the nonlinear behavior of the motor and support accurate prediction of future current changes. The actual measured voltage and current The coordinate transformation module SRF-PLL generates the synchronous angular velocity on the d-axis and q-axis. and phase angle Through this transformation process, the voltage and current signals are converted from the abc coordinate system to the dq coordinate system so that the active power and reactive power can be independently controlled on the d-axis and q-axis. In the dq coordinate system, the optimized control strategy independently controls the d-axis current and q-axis current , which correspond to the regulation of the active power and reactive power of the motor respectively. Through the model predictive control algorithm, the system predicts the future current change trend in real time and dynamically optimizes the control input according to the prediction results. The control input directly acts on the rotor-side converter to adjust the d-axis voltage and q-axis voltage to achieve dynamic adjustment of the motor rotor voltage, thereby improving the motor's response speed to power commands and the stability of the system. The entire process estimates the motor's state variables through the estimator module, including the rotor angular velocity and the operating status of the motor. Finally, the doubly fed machine model The described dynamic characteristics work together with the optimized control strategy to achieve accurate control of the rotor voltage, thus effectively meeting the grid's requirement for fast response of the doubly-fed machine.

[0077] In step S130, by substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, a quadratic cost function is generated, and the quadratic cost function is used to solve the analytical optimal solution of the control variables under the model predictive control framework to generate dynamically optimized control parameters.

[0078] Among them, the error equation can represent the difference between the actual output of the system and the desired target, and is used to calculate the degree to which the system deviates from the target value in the current state, thereby providing a basis for correction. The quadratic cost function can represent an optimization objective, in which the square terms of the error and the control increment are weighted combinations, so that the optimization process can balance the requirements of minimizing the error and controlling energy consumption. The model predictive control framework can represent a control method in which the future state is predicted based on the system model and the current control input is obtained through a rolling optimization strategy. The control parameters can represent the variables that affect the behavior of the system and are used to adjust the system according to the optimal solution to achieve dynamic optimization control of the pump-turbine and the doubly fed motor.

[0079] In some embodiments, by substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, a quadratic cost function is generated, including: based on the load optimization regulation method, according to the system demand and load state, determining the active power reference value of the system, and using the back propagation neural network to determine the active power reference value according to the current load and system state; based on the active power reference value, determining the optimal speed of the doubly-fed motor, establishing a state space model of the doubly-fed motor according to the optimal speed, and discretizing the state space model; determining the control error according to the difference between the actual system state and the target reference value, and generating a control error expression; based on the discretized state space model and the control error expression, constructing a system state prediction model, and constructing an objective function based on the system state prediction model, the objective function is: ; in, represents the objective function, represents the system state error vector, Represents the weight matrix, which is used to adjust the weight of the system state error in the objective function. represents the control increment vector, represents the control increment weight matrix; the error equation is substituted into the objective function, and the pump-turbine model, the doubly-fed machine model and the water diversion system model are combined to generate a quadratic cost function that depends on the control increment.

[0080] Specifically, refer to Figure 2 As shown, the specific process of generating the quadratic cost function is described in detail: The first step is to determine the optimal speed. According to the load optimization regulation theory, the system's active power reference value is calculated based on the system's needs and current load status. The optimization function formula is as follows: ; in, Indicates time The load power demand, Indicates time The active power reference value is output by the BP neural network. represents the control input, represents the regularization parameter, which is used to balance the load tracking error and control energy consumption, T represents the length of the optimization time period, Represents the objective function.

[0081] Back propagation (BP) neural network is used to calculate the active power reference value. The input vector Contains the current state of the system (such as voltage, current, speed, etc.), and the output vector is the active power reference value The expression of BP neural network is: ; in, represents the weight matrix, Represents the bias vector, activation function Use ReLU to ensure the nonlinear mapping of the network.

[0082] Get active power reference value Then, calculate the corresponding optimal speed : ; in, represents the power constant of the doubly-fed machine, Represents the electromagnetic torque of the motor.

[0083] The second step is to construct and discretize the state space model, and describe the dynamic behavior of the doubly fed generator by establishing a discrete time model of the system. The continuous time form of the state space model of the doubly fed generator is as follows: ; in, represents the state vector of the system, Represents the system state matrix, describing the dynamic relationship of the state vector, represents the input matrix, describing the effect of the control input on the state, represents the control input vector, represents the interference matrix, describing the impact of external interference on the system state, represents the external interference vector, represents the system output vector, Represents the output matrix, describing the impact of the state vector on the output.

[0084] The state space model of the doubly-fed generator is discretized to obtain a discrete time model as follows: ; in, represents the discretized system state matrix, where is the sampling time, represents the discretized input matrix, represents the discretized interference matrix, Represents the discretized output matrix, which remains unchanged.

[0085] The power dynamic equation of the doubly-fed generator is discretized to obtain the system active power and reactive power Discretization model of: ; in, represents the active power of the system, represents the reactive power of the system, is the sampling time, represents the slip angular velocity, represents the mechanical inertia constant of the doubly-fed machine, Indicates the stator side voltage, represents the mutual inductance between the stator and the rotor, represents the d-axis voltage on the rotor side, represents the q-axis voltage on the rotor side, is the rotor inductance, The state space model of the discretized doubly-fed machine can be further applied in predictive control to adjust the control input of the system in real time through the dynamic changes of active and reactive power.

[0086] The third step is error calculation and differential processing. The control error at the current moment is defined as the difference between the actual state of the system and the target reference value. The specific expression is as follows: ; in, represents the discrete state matrix, represents the discrete control input matrix, represents the discrete interference matrix, represents the discrete output matrix, is the state increment for the next moment, is the output increment at the next moment, is the control input increment at the current moment, is the interference increment at the current moment.

[0087] Assuming control error The actual output of the system With control reference value The difference is: ; The difference form is expressed as: ; Substituting into the control error formula, we get: ; Further simplifying, we get: ; Introducing extended state variables As and A combination of: ; Therefore, the state space model of the doubly fed machine can be expressed as: ; in, represents the discrete system state matrix, represents the discrete control input matrix, represents the discrete interference matrix, represents the discrete output matrix.

[0088] The fourth step is to establish the prediction model. Select the control level as M , build time k For the future P The prediction model of the step is expressed as: ; in, represents the prediction vector of the system state, represents the prediction vector of the control increment, represents the prediction vector of the interference increment, A prediction vector representing the increments to the reference values.

[0089] matrix S , F , K , T They are used to describe the influence relationship between different variables in the prediction model. The transfer matrix representing the state variables is used to reflect the impact of the current state of the system on the predicted values ​​of each future step. represents the influence of control input on the prediction model, represents the system's response to external disturbances, represents the influence of the reference input on the predicted state of the system, A represents the system state matrix, B represents the control input matrix, G represents the interference matrix, and C represents the output matrix.

[0090] The fifth step is to construct the objective function and substitute the error equation into the objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model to generate a quadratic cost function that depends on the control increment.

[0091] In some embodiments, a quadratic cost function is used to solve the analytical optimal solution of the control variable under the model predictive control framework to generate dynamically optimized control parameters, including: introducing the quadratic cost function into the model predictive control framework to form an optimization problem of the control increment; based on the optimization problem of the control increment, determining the analytical optimal solution of the control variable; substituting the analytical optimal solution into the calculation expression of the control variable to generate dynamically optimized control parameters, and applying the control parameters to the state space model of the doubly fed motor.

[0092] Specifically, the quadratic cost function is defined as the weighted sum of the deviation and control increment of the system in the future prediction time domain. First, by minimizing the quadratic cost function in the optimization problem, the optimal solution of the control increment can be found, so as to minimize the deviation of the system state in the future time domain and avoid excessive changes in the control input. In this process, the optimization problem of the control increment can be expressed as: ; in, represents the optimization goal, represents the system state error vector, represents the weight matrix, represents the control increment vector, Represents the control increment weight matrix.

[0093] When constructing the control increment optimization problem, the analytical optimal solution of the control increment is obtained through matrix calculation and optimization solution. The solution formula of the analytical optimal solution is: ; in, Represents the control increment influence matrix, which is used to characterize the influence of control input on the state at each future moment. S represents the state transfer matrix, represents the system state vector at the current moment, represents the disturbance vector at the current moment, T represents the control reference value influence matrix, Represents the control reference value vector.

[0094] Substitute the analytical optimal solution into the calculation expression of the control variable, that is, express the control variable as a function of the current system state, reference value and interference, obtain the new control variable value, and form the dynamically optimized control parameter. Then, the state space model of the doubly fed motor is adjusted using the dynamically optimized control parameter. Specifically, by adjusting the control input in the state space model, the dq axis current of the motor is updated in real time so that it can meet the target active power and reactive power reference values, thereby achieving optimal control of the dynamic performance of the doubly fed motor. Finally, through real-time calculation and feedback, the control parameters generated by the analytical optimal solution are dynamically applied to the state space model of the doubly fed motor to ensure that the output of the system meets the set control target and achieve accurate and stable control.

[0095] In step S140, the objective function and control parameters are adjusted in real time using the pump turbine control strategy and the doubly-fed generator control strategy.

[0096] Among them, by using the pump-turbine control strategy and the doubly-fed motor control strategy to adjust the objective function and control parameters in real time, the accurate dynamic response of the doubly-fed variable-speed pumped storage unit can be achieved. This real-time adjustment can quickly respond to system requirements under different operating conditions, ensuring that the operating parameters of the pump-turbine and the doubly-fed motor are always close to the optimal state, thereby improving the power output stability and regulation capability of the system. At the same time, this real-time optimization based on the model predictive control framework enables the system to effectively cope with grid frequency and load fluctuations, improve the operating efficiency of the motor and the stability of the grid, and further meet the needs of modern power grids for flexibility and rapid response.

[0097] In some embodiments, the objective function and control parameters are adjusted in real time using a pump-turbine control strategy and a doubly-fed motor control strategy, including: collecting the operating parameters of the doubly-fed variable-speed pumped storage unit, and based on the operating parameters, using a data-driven algorithm to determine the current system state, and generating a real-time estimate of the system state; combining the real-time estimate with a model predictive control framework to predict the future system state, and calculating the optimal control input based on the prediction result, and adjusting the rotor voltage and guide vane opening using the optimal control input; using closed-loop feedback control to determine the control error based on the operating parameters, and adjusting the control input based on the control error; real-time evaluation of the control performance of the doubly-fed variable-speed pumped storage unit, and based on the evaluation results, dynamically adjusting the objective function and control input using a pump-turbine control strategy and a doubly-fed motor control strategy.

[0098] Specifically, refer to Figure 2 As shown in the figure, in the process of system optimization and dynamic adjustment of control parameters, first of all, real-time system monitoring and state estimation are carried out. Through a high-precision sensor network, the operating parameters of the double-fed variable-speed pumped storage unit are collected in real time, including key parameters such as the voltage, current, speed, guide vane opening, etc. of the motor. These data are transmitted to the central control unit through sensors, and the unit uses a data-driven algorithm to analyze and process the current system state and generate an estimate of the real-time system state. These estimates provide dynamic feedback for the actual working state of the current system, helping the system to respond quickly to changes in real-time state.

[0099] Then, dynamic adjustment and adaptive control are performed. The generated real-time system state estimate is combined with the model predictive control framework, under which the future system state is predicted. Specifically, through the model predictive control framework, the system can foresee the system operation change trend in the future based on the current state estimate. Based on the prediction results, the system uses an optimization algorithm to calculate the optimal control input so that the system achieves the desired control target. This optimal control input includes adjustment values ​​for the motor rotor voltage and the guide vane opening of the pump turbine. By applying this optimal control input, the system can adapt to changes in operating conditions, thereby optimizing the motor speed and power output.

[0100] Next, closed-loop feedback control and error correction are performed. In the closed-loop feedback control mechanism, real-time collected data is used to determine the control error. The control error is the difference between the actual output of the system and the preset target reference value, which is monitored and corrected in real time by the closed-loop feedback system. Based on this error signal, the system adjusts the current control input to immediately correct the deviation and make the actual state of the system as close to the target state as possible. Through this closed-loop feedback control, precise regulation and stable control of the system state can be achieved.

[0101] Finally, real-time performance evaluation and control strategy optimization are performed. During operation, the system continuously evaluates the control performance of the doubly-fed variable-speed pumped storage unit in real time, including key indicators such as control accuracy, response speed, output power, and energy efficiency. If the evaluation results show that the control performance does not meet the expected requirements, the system dynamically optimizes the control strategy based on the evaluation results. Specifically, the system uses the pump-turbine control strategy and the doubly-fed motor control strategy to adjust the objective function and control input in real time to further optimize the system performance.

[0102] Furthermore, a simulation experiment was conducted on the control method of the double-fed variable-speed pumped storage unit based on model prediction in the above embodiment in MATLAB / Simulink, and the simulation step size was set to 5×10^-5s. The simulation background is a microgrid composed of photovoltaic, wind power, variable-speed pumped storage unit, diesel generator set and auxiliary pump. The power situation of each device is as follows: Fig.10 As shown. Among them, It represents the system load power, that is, the total power demand of the microgrid at each time point. Represents the auxiliary water pump power, which represents the power output of the auxiliary pump used for pumping water for energy storage. Indicates the power of diesel generator set. Represents photovoltaic power generation power. Indicates wind power. The horizontal axis represents time in hours, and the vertical axis represents power in megawatts (MW). The variable speed pumped storage unit controls the output power through the double-fed motor control system, and the pump turbine control system controls the speed by adjusting the guide vane opening. In steady-state operation, the results after speed optimization are as follows Fig.11 This result is used to guide the control strategy of the power converter to ensure the optimal power output of the pumped storage system. Indicates the speed, It represents the power of the variable speed pumped storage unit. The horizontal axis represents time, and the vertical axis includes the power and speed of the pumped storage unit.

[0103] The control method of the doubly-fed variable-speed pumped storage unit based on model prediction in the above-mentioned embodiment can more effectively cope with the complex nonlinear characteristics of the variable-speed pumped storage unit by establishing an accurate dynamic model based on actual operating data, so that the system can maintain stable operation under various complex working conditions, and significantly improve the control accuracy. The adaptability of traditional PID control to dynamic changes is limited, while the present disclosure adopts a data-driven MPC method, which can predict and dynamically adjust the control strategy in real time, flexibly respond to the instantaneous changes of the system and external environmental fluctuations, and significantly improve the response speed of the system. In the optimization process, the physical constraints and operational limitations of the system can be directly processed to ensure that the system achieves optimal control while satisfying the constraints, overcome the shortcomings of traditional PID control in handling complex constraints, and further improve the reliability and operation efficiency of the system. Compared with the cumbersome parameter adjustment and optimization process in traditional PID control, the present disclosure automatically generates the optimal control input through a data-driven method, reduces the need for manual intervention, and makes the control system more flexible and efficient.

[0104] Next, in the embodiment of the present disclosure, a control device for a double-fed variable speed pumped storage unit based on model prediction is also provided. Fig.12 As shown in , the doubly-fed variable-speed pumped storage unit control device 1200 based on model prediction can be composed of a dynamic model building module 1201, a control strategy establishment module 1202, a dynamic optimization control module 1203 and a control parameter adjustment module 1204. Among them: the dynamic model construction module can be used to construct the pump-turbine model, doubly-fed motor model and water diversion system model corresponding to the target doubly-fed variable-speed pumped storage unit based on data drive; the control strategy establishment module can be used to establish the pump-turbine control strategy according to the turbine closed-loop regulation and the pump open-loop regulation, and establish the doubly-fed motor control strategy based on the DC bus voltage control of the grid-side converter and the rotor voltage control of the rotor-side converter; the dynamic optimization control module can be used to generate a quadratic cost function by substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed motor model and the water diversion system model, and using the quadratic cost function to solve the analytical optimal solution of the control variables under the model predictive control framework to generate dynamically optimized control parameters; the control parameter adjustment module can be used to use the pump-turbine control strategy and the doubly-fed motor control strategy to adjust the objective function and control parameters in real time.

[0105] It should be noted that the specific details of each part of the above-mentioned doubly-fed variable-speed pumped storage unit control device based on model prediction have been described in detail in the implementation method of the doubly-fed variable-speed pumped storage unit control method based on model prediction. The undisclosed details can be found in the implementation method content of the method part, and will not be repeated here.

[0106] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned double-fed variable-speed pumped storage unit control method based on model prediction is also provided.

[0107] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0108] Refer to the following Fig.13 1300 according to this embodiment of the present disclosure is described. Fig.13 The electronic device 1300 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0109] like Fig.13 As shown, the electronic device 1300 is in the form of a general computing device. The components of the electronic device 1300 may include but are not limited to: the at least one processing unit 1310, the at least one storage unit 1320, a bus 1330 connecting different system components (including the storage unit 1320 and the processing unit 1310), and a display unit 1340.

[0110] The storage unit stores program codes, which can be executed by the processing unit 1310, so that the processing unit 1310 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0111] The storage unit 1320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 1321 and / or a cache memory unit 1322 , and may further include a read-only memory unit (ROM) 1323 .

[0112] The storage unit 1320 may also include a program / utility 1324 having a set (at least one) of program modules 1325, such program modules 1325 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0113] Bus 1330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0114] The electronic device 1300 may also communicate with one or more external devices 1370 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1300, and / or communicate with any device that enables the electronic device 1300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1350. In addition, the electronic device 1300 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 1360. As shown, the network adapter 1360 communicates with other modules of the electronic device 1300 through a bus 1330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0115] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0116] refer to Fig.14 As shown, a program product 1400 for implementing the above-mentioned model-predicted double-fed variable-speed pumped storage unit control method according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus, or a device.

[0117] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 thereof.

[0118] It should be understood that the present disclosure is not limited to the exact structures that have been 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 disclosure is limited only by the appended claims.

Claims

1. A control method for a double-fed variable speed pumped storage unit based on model prediction, characterized in that: include: Based on data-driven construction, the pump-turbine model, doubly-fed generator model and water diversion system model corresponding to the target doubly-fed variable-speed pumped storage unit are constructed; A pump-turbine control strategy is established based on turbine closed-loop regulation and pump open-loop regulation, and a doubly-fed generator control strategy is established based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter; Substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model, generating a quadratic cost function, and using the quadratic cost function to solve the analytical optimal solution of the control variable under the model predictive control framework to generate dynamically optimized control parameters; The objective function and the control parameters are adjusted in real time using the pump-turbine control strategy and the doubly-fed motor control strategy.

2. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The construction process of the pump-turbine model includes: Based on the nonlinear relationship between the unit speed and unit flow rate and the guide vane opening of the pump-turbine, the full flow characteristic curve is determined; Determining a full torque characteristic curve based on a nonlinear relationship between a unit speed and a unit torque and a guide vane opening of the pump-turbine; Based on the operating parameters of the pump-turbine, the conversion relationship equation between the unit speed, unit torque and unit flow and their actual values ​​is established: ； in, Indicates the unit speed, Indicates the actual speed. Indicates the nominal diameter of the pump turbine. Indicates water head, represents unit torque, Indicates the actual torque, represents unit flow rate, and Q represents actual flow rate.

3. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The construction process of the doubly-fed generator model includes: Based on the voltage and current relationship of the doubly-fed generator, the active and reactive dynamic equations corresponding to the doubly-fed generator are determined: ； in, Indicates the active power on the stator side, t Indicates time, represents the mechanical inertia constant, represents the stator voltage, represents the coupling inductance between the stator and the rotor, represents the magnetic flux component of the rotor on the d-axis in the dq reference frame, Indicates the reactive power on the stator side, It represents the magnetic flux component of the rotor on the q-axis in the dq reference frame; The active and reactive dynamic equations are combined with the relationship equation between the voltage and current on the rotor side to obtain the doubly-fed machine model: ； in, represents the slip angular velocity, represents the voltage component of the rotor on the d-axis in the dq reference frame, represents the voltage component of the rotor on the q-axis in the dq reference frame, is the rotor inductance, Indicates the synchronous angular velocity.

4. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The construction process of the water diversion system model includes: The water diversion system model is constructed based on the approximate elastic water hammer characteristics: ； in, represents the transfer function of the water diversion system, is the time constant representing the inertia of water, represents the Laplace operator, Indicates the reflection time of water hammer pressure, Represents the head loss coefficient.

5. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The pump-turbine control strategy is established based on the turbine closed-loop regulation and the pump open-loop regulation, including: Under turbine operating conditions, a closed-loop control strategy is used to adjust the water flow through the guide vanes, monitor the turbine speed and output power in real time, and obtain actual status data; Comparing the actual state data with a preset target value, generating a deviation signal, and dynamically adjusting the guide vane opening using the deviation signal; Under the pump working condition, the open-loop control strategy is used to adjust the water flow by presetting the guide vane opening, and the guide vane opening is set according to the operation requirements of the pump; The closed-loop control strategy and the open-loop control strategy are used to control the flow of the turbine and the pump respectively, so as to construct the pump-turbine control strategy.

6. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The control strategy of the doubly-fed machine is established based on the DC bus voltage control of the grid-side converter and the rotor voltage control of the rotor-side converter, including: Utilize the grid-side converter to perform vector control, decouple the active power and reactive power of the motor, control the d-axis and q-axis currents respectively, so as to adjust the DC bus voltage and generate a control reference value for the power flow between the motor and the grid; The rotor voltage of the motor is adjusted by using the rotor-side converter, and the d-axis current and the q-axis current of the rotor side are controlled based on the dq-axis model to generate control inputs of the active power and the reactive power of the motor; Based on the model predictive control method, the change trend of the rotor current is predicted, and the control input is optimized based on the prediction result to dynamically adjust the rotor voltage.

7. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: Substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model, a quadratic cost function is generated, including: Based on the load optimization regulation method, the active power reference value of the system is determined according to the system demand and load status, and the back propagation neural network is used to determine the active power reference value according to the current load and system status; Based on the active power reference value, determining the optimal speed of the doubly-fed motor, establishing a state space model of the doubly-fed motor according to the optimal speed, and discretizing the state space model; Determine the control error according to the difference between the actual system state and the target reference value, and generate a control error expression; Based on the discretized state space model and the control error expression, a system state prediction model is constructed, and based on the system state prediction model, an objective function is constructed, and the objective function is: ； in, represents the objective function, represents the system state error vector, Represents the weight matrix, which is used to adjust the weight of the system state error in the objective function. represents the control increment vector, represents the control increment weight matrix; Substituting the error equation into the objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model, a quadratic cost function dependent on the control increment is generated.

8. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The method of using the quadratic cost function to solve the analytical optimal solution of the control variable under the model predictive control framework to generate dynamically optimized control parameters includes: The quadratic cost function is introduced into the model predictive control framework to form an optimization problem of control increment; Determining an analytical optimal solution of a control variable based on the optimization problem of the control increment; The analytical optimal solution is substituted into the calculation expression of the control variable to generate dynamically optimized control parameters, and the control parameters are applied to the state space model of the doubly-fed motor.

9. The control method of a double-fed variable speed pumped storage unit according to claim 1, characterized in that: The real-time adjustment of the objective function and the control parameters by using the pump-turbine control strategy and the doubly-fed electric machine control strategy includes: Collecting the operating parameters of the double-fed variable speed pumped storage unit, determining the current system state based on the operating parameters using a data-driven algorithm, and generating a real-time estimated value of the system state; Combining the real-time estimated value with a model predictive control framework to predict the future system state, and calculating an optimal control input based on the prediction result, and using the optimal control input to adjust the rotor voltage and guide vane opening; Determining a control error according to the operating parameter using closed-loop feedback control, and adjusting the control input according to the control error; The control performance of the doubly-fed variable-speed pumped storage unit is evaluated in real time, and according to the evaluation result, the objective function and the control input are dynamically adjusted using the pump-turbine control strategy and the doubly-fed motor control strategy.

10. A control device for a double-fed variable speed pumped storage unit based on model prediction, characterized in that: include: Dynamic model building module, used to build the pump-turbine model, doubly-fed generator model and water diversion system model corresponding to the target doubly-fed variable-speed pumped storage unit based on data-driven; A control strategy establishment module, used to establish a pump-turbine control strategy based on turbine closed-loop regulation and pump open-loop regulation, and to establish a doubly-fed generator control strategy based on DC bus voltage control of the grid-side converter and rotor voltage control of the rotor-side converter; A dynamic optimization control module, for generating a quadratic cost function by substituting an error equation into an objective function, combining the pump-turbine model, the doubly-fed generator model and the water diversion system model, and using the quadratic cost function to solve an analytical optimal solution of a control variable under a model predictive control framework to generate dynamically optimized control parameters; A control parameter adjustment module is used to adjust the objective function and the control parameters in real time by using the pump-turbine control strategy and the doubly-fed motor control strategy.

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