Wind turbine group service quality regulation and control method and system for high-quality power generation

Abstract

The invention discloses a wind turbine group service quality regulation and control method and system for high-quality power generation, and the method comprises the steps: building a temperature model of a generator and a converter of a wind turbine generator set through the combination of a thermodynamic mechanism and energy loss; the temperature models of the generator and the converter are function models about active power and reactive power of the wind turbine generator; solving a multi-objective optimization problem by combining the temperature models of the generator and the converter to obtain an optimal power generation strategy of the wind turbine generator, wherein the multi-objective optimization is composed of three problems of temperature rise of the wind turbine generator, temperature rise of the converter and voltage increment; and issuing the optimal power generation strategy to the wind turbine generator to execute, calculating a service quality regulation index of the wind turbine generator group according to the executed wind power plant state information, and executing a fan protection mechanism if the service quality regulation index exceeds a preset threshold value. The invention aims to optimize the power generation strategy and improve the operation reliability and economical efficiency of the wind power plant by dynamically coordinating the health state of the unit and the system-level performance index.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to a method and system for regulating the service quality of a wind turbine group for high-quality power generation. Background Art

[0002] In the industrial background where the installed capacity of wind power has exceeded 800GW, the modern wind power system characterized by the clustering of large wind turbines is facing three major technical bottlenecks: the contradiction between the inherent random fluctuation characteristics of wind power output and the rigid power demand of the power grid; the reliability attenuation problem caused by the accelerated degradation of key components under extreme working conditions; and the economic constraint that the operation and maintenance cost in the whole life cycle accounts for more than 25%. It should be particularly noted that there are significant structural defects in the current service quality management system - the traditional single-machine discrete management paradigm only emphasizes the health monitoring of individual equipment, but ignores the system-level dynamic correlations such as aerodynamic interference between units and grid coupling effects, resulting in a "1 + 1 < 2" collaborative loss phenomenon in the overall performance of the wind farm. This one-dimensional management strategy is difficult to meet the dual requirements of high-reliability power output and low operation and maintenance cost for the wind farm in the new power system, and there is an urgent need to construct a dynamic regulation system for service quality based on multi-dimensional collaborative optimization to achieve two-way real-time balance between the equipment-level health status and the station-level operation efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention: In view of the above problems of the prior art, a method and system for regulating the service quality of a wind turbine group for high-quality power generation are provided. The present invention aims to optimize the power generation strategy by dynamically coordinating the health status of the units and the system-level performance indicators, and improve the operation reliability and economy of the wind farm.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A method for regulating the service quality of a wind turbine group for high-quality power generation, comprising the following steps: S1, respectively establish temperature models of the generator and the converter of the wind turbine by combining the thermodynamic mechanism and the energy loss. The temperature models of the generator and the converter are function models about the active power and reactive power of the wind turbine; S2, solve the multi-objective optimization problem shown in the following formula by combining the temperature models of the generator and the converter to obtain the optimal power generation strategy of the wind turbine: , , , , wherein, is the objective function of the multi-objective optimization problem, is the objective function of the temperature rise of the wind turbine generator is the objective function of the temperature rise of the converter, is the objective function of the voltage increment, is the prediction step size, is the number of wind turbines, is the weight coefficient of the temperature rise of the wind turbine, is the temperature of the generator in the th wind turbine unit, is the ambient temperature, is the weight coefficient of the temperature rise of the converter, is the temperature of the converter in the and are respectively the th wind turbine unit's terminal voltage and reference voltage at the current moment ; S3. Send the optimal power generation strategy of the wind turbine unit to the wind turbine unit for execution, calculate the service quality control index of the wind turbine group according to the state information of the wind farm after executing the optimal power generation strategy, and if the service quality control index of the wind turbine group exceeds the preset threshold, execute the fan protection mechanism, where the fan protection mechanism refers to performing load reduction or emergency shutdown of the fan.

[0005] Optionally, the generator of the wind turbine unit is a doubly-fed induction generator, and the temperature model of the generator represents the temperature of the generator by the stator winding temperature of the generator. The functional expression of the temperature model of the generator is: , where is the stator winding temperature of the wind turbine, is the initial temperature, is the increment of the active power output of the generator , is the increment of the stator reactive power , is the sensitivity of the stator winding temperature with respect to the active power output of the generator , is the sensitivity of the stator winding temperature with respect to the stator reactive power , and there are: , , , , , , , , , , wherein, is the stator winding temperature is the sensitivity with respect to stator copper loss , is the stator copper loss is the sensitivity with respect to the active power output of the generator , is the stator winding temperature is the sensitivity with respect to rotor copper loss , is the rotor copper loss is the sensitivity with respect to the active power output of the generator , is the stator copper loss is the sensitivity with respect to stator reactive power , is the rotor copper loss is the sensitivity with respect to stator reactive power , is the 10th row and 10th column of the inverse matrix of is the 10th row and 4th column of the inverse matrix of is the matrix composed of the reciprocals of the thermal resistances between the nodes of the generator , is the node temperature matrix of the generator is the node heat source matrix of the generator. The nodes in the node heat source matrix include the shaft, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and housing of the motor; is the stator copper loss is the sensitivity with respect to the q-axis current , is the q-axis current is the sensitivity with respect to the active power output of the generator , is the stator copper loss is the sensitivity with respect to the d-axis current , is the rotor d-axis current is the sensitivity with respect to stator reactive power , is the rotor resistance. The calculation function expression of the stator copper loss is: , Among them, is the stator resistance, is the stator inductance, is the mutual inductance, is the d-axis current, is the q-axis current, is the stator flux linkage; the rotor copper loss The calculation function expression is: , Among them, is the rotor resistance.

[0006] Optionally, the function expression of the temperature model of the converter is: , Among them, is the temperature of the converter, is the increment of the reactive power output by the converter . is the sensitivity of the temperature of the converter with respect to the active power output , is the sensitivity of the temperature of the converter with respect to the reactive power output by the converter ; is the sensitivity of the temperature of the converter with respect to the stator reactive power ; , , , , Among them, is the sensitivity of the temperature of the converter with respect to the converter loss , is the converter loss with respect to the active power output , is the converter loss with respect to the reactive power output by the converter ; is the converter loss with respect to the stator reactive power ; is the total thermal resistance of the converter, and the calculation function expression of the total thermal resistance of the converter is: , Among them, is the thermal resistance from the th layer to the housing, is the thermal resistance from the housing to the radiator, is the thermal resistance from the radiator to the external environment, is the number of physical structure layers between the chip device junction of the converter and the housing.

[0007] Optionally, the converter loss The calculation function expression is: , , , where, and are intermediate variables, is the root mean square current of the grid-side converter, is the root mean square current of the rotor-side converter, is the collector-emitter voltage of the IGBT, and are the turn-on and turn-off losses of the IGBT respectively, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the reverse diode of the IGBT, is the lead resistance of the IGBT.

[0008] Optionally, the multi-objective optimization problem in step S2 further includes the following constraint conditions: , where, is the active power of the th wind turbine, is the active power dispatched, is the reference active power of the th wind turbine, is the maximum active power of the th wind turbine, is the reference reactive power of the th wind turbine, and are the minimum reactive power and the maximum reactive power of the th wind turbine respectively.

[0009] Optionally, the calculation function expression of the service quality control index of the wind turbine group in step S3 is: , , , where, is the service quality control index for a wind turbine cluster, , , , , and are weight coefficients, is 's normalization result, is 's normalization result, is the health index at the wind turbine level, is the operation performance index at the system level, is the temperature of the generator 's change amount, is the temperature of the converter 's change amount, is the incremental active power output by the generator 's increment, is the terminal voltage 's increment.

[0010] Optionally, in step S3, it also includes respectively detecting the temperature rise of the wind turbine generator , the temperature rise of the converter and the voltage increment . If any one of the above three items exceeds the preset threshold, increase the weight coefficient corresponding to this item in the multi-objective optimization problem to strengthen the monitoring of the temperature rise or increment of this item.

[0011] In addition, the present invention also provides a service quality control system for a wind turbine cluster for high-quality power generation, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the service quality control method for a wind turbine cluster for high-quality power generation.

[0012] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the service quality control method for a wind turbine cluster for high-quality power generation through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instruction, and the computer program or instruction is programmed or configured to execute the service quality control method for a wind turbine cluster for high-quality power generation through a processor.

[0014] Compared with the prior art, the present invention can mainly achieve the following beneficial effects: The present invention constructs a multi-objective optimization model by real-time monitoring of the terminal voltage deviation of the wind turbine and the temperatures of key components (such as converters and generators), and solves to obtain the optimal active power reference value and the reactive power reference value , realizing precise regulation of the wind turbine generator set. In addition, based on the real-time data fed back by the wind farm (including the deviation of the terminal voltage, output power from the TSO power demand, and the temperature of key components), the present invention dynamically calculates the service quality index of the wind turbine generator set, evaluates the operating state of the unit in real time, and optimizes the objective function through an adaptive weight adjustment mechanism to ensure the best balance between the health of the unit and the system performance under different operating conditions. It can significantly reduce the operating temperature of the key components of the wind turbine generator set on the premise of ensuring the operating performance of the wind turbine generator set (including maintaining the bus voltage within a preset range and meeting the power demand of the external power grid), thereby effectively improving the health performance and service life of the unit, and enhancing the operating reliability, economy and equipment life of the wind farm, especially suitable for the scenario of high-penetration wind power grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the basic process of the method according to an embodiment of the present invention.

[0016] Figure 2 is a schematic diagram of the system architecture of the method according to an embodiment of the present invention.

[0017] Figure 3 is a schematic diagram of the control principle of the rotor-side converter RSC and the grid-side converter GSC in an embodiment of the present invention, where (a) is a schematic diagram of the reactive power control principle of the grid-side converter GSC, (b) is a schematic diagram of the active power control principle of the grid-side converter GSC, and (c) is a schematic diagram of the active power control principle of the rotor-side converter RSC.

[0018] Figure 4 is a schematic diagram of the generator temperature obtained by simulation in an embodiment of the present invention.

[0019] Figure 5 is a schematic diagram of the converter temperature obtained by simulation in an embodiment of the present invention.

[0020] Figure 6 is a schematic diagram of the wind turbine generator set terminal voltage obtained by simulation in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below in conjunction with the drawings in the embodiments of the present invention.

[0022] As Figure 1 shown, the method for regulating the service quality of a wind turbine group for high-quality power generation in this embodiment includes the following steps: S1. Establish the temperature models of the generator and the converter of the wind turbine by combining the thermodynamic mechanism and energy loss respectively. The temperature models of the generator and the converter are function models of the active power and reactive power of the wind turbine. S2. Solve the multi-objective optimization problem shown in the following formula by combining the temperature models of the generator and the converter to obtain the optimal power generation strategy of the wind turbine: , , , , where, is the objective function of the multi-objective optimization problem, is the objective function of the temperature rise of the wind turbine generator, is the objective function of the temperature rise of the converter, is the objective function of the voltage increment, is the prediction step, is the number of wind turbine generators, is the weight coefficient of the temperature rise of the wind turbine generator, is the temperature of the generator in the th wind turbine, is the ambient temperature, is the weight coefficient of the temperature rise of the converter, is the temperature of the converter in the th wind turbine, is the weight coefficient of the voltage increment, and are the terminal voltage and the reference voltage of the th wind turbine at the current moment respectively, and there is: , where, is the voltage value of the th wind turbine at the current moment k , is the initial moment value of the voltage of the th wind turbine, is the active power output by the th wind turbine at the current moment, is the th wind turbine voltage sensitivity to the active power , is the active power increment of the th wind turbine at the current moment k , is the The reactive power of the th wind turbine generator set, The sensitivity of the reactive power ; The th wind turbine generator set at the current moment k reactive power increment, , is the number of wind turbine generator sets; S3. Send the optimal power generation strategy of the wind turbine generator set to the wind turbine generator set for execution, calculate the service quality control index of the wind turbine group according to the wind farm status information after executing the optimal power generation strategy. If the service quality control index of the wind turbine group exceeds the preset threshold, execute the fan protection mechanism, and the fan protection mechanism refers to executing the load reduction or emergency shutdown of the fan.

[0023] As Figure 2 shown, the method of this embodiment mainly includes a wind farm, an optimization solver, and a service quality model. The measurement data of the wind farm (including active power P, reactive power Q, voltage U, and current I) are input into the optimization solver, and the optimization solver executes steps S1 and S2, including voltage deviation power tracking, voltage control and power response, power loss calculation, temperature calculation, and temperature rise calculation of key components (motor and converter) at the single-machine level. Then, after sending the optimal power generation strategy of the wind turbine generator set to the wind turbine generator set for execution in step S3, call the service quality model to calculate the service quality control index of the wind turbine group according to the wind farm status information after executing the optimal power generation strategy. If the service quality control index of the wind turbine group exceeds the preset threshold, execute the fan protection mechanism, and the fan protection mechanism refers to executing the load reduction or emergency shutdown of the fan. In addition, the service quality model called in this embodiment also provides various sensitivity data, including the sensitivity of voltage V with respect to active power P and reactive power Q, the sensitivity of temperature T with respect to active power P and reactive power Q, and active power tracking, where is the reference value of the active power obtained by active power tracking, is the rotor angle.

[0024] In this embodiment, the generator of the wind turbine generator set is a doubly-fed induction generator (DFIG). The characteristic of the doubly-fed induction generator is that the stator is directly connected to the power grid, and the rotor is connected through a back-to-back PWM converter (rotor-side converter RSC and grid-side converter GSC). The rotor-side converter RSC uses stator flux-oriented vector control to achieve decoupled regulation of active / reactive power, while the grid-side converter GSC uses grid voltage-oriented control to achieve the stability of the DC bus voltage and the regulation of grid-side reactive power. In the dq reference system oriented by the stator flux, the stator voltage and flux equations are: , Among them, and respectively represent the dq-axis voltage and dq-axis stator flux linkage of the stator; and respectively represent the dq-axis current of the stator and the dq-axis current of the rotor; correspond to the stator resistance, stator inductance, and mutual inductance respectively; is the synchronous angular velocity. By aligning the dq-axis stator flux linkage to the d-axis and ignoring the stator resistance , the equation is simplified to: , , Further derive the expression of the stator current: , .

[0025] This decoupled rotor current model provides a theoretical basis for the power control of the rotor-side converter RSC.

[0026] The active power output of the doubly-fed induction generator is: , The first term in the above formula is the active power output of the stator, and the second term is the active power output of the grid-side converter GSC. The reactive power of the stator is controlled by adjusting the rotor d-axis current , and its calculation formula can be derived and expressed as: , Among them, is the stator flux linkage, is the mutual inductance, is the power supply angular velocity, is the stator inductance of the doubly-fed induction generator, is the q-axis rotor voltage, is the rotor q-axis current.

[0027] In the synchronous rotating reference frame oriented by the grid voltage, the reactive power can be modeled as: , Among them, is the amplitude of the grid phase voltage.

[0028] Figure 3 This is the schematic diagram of the control principle of the rotor-side converter RSC and the grid-side converter GSC in this embodiment, The state space model of the active power increment of the rotor-side converter RSC is as follows: , Among them, represents the increment, is the increment of the rotor q-axis current, is the complex frequency domain variable in the Laplace transform, is the time constant of the current loop, and are respectively the proportional gain and integral gain of the PI controller for the active power control of the rotor-side converter RSC, is the reference value of the active power output of the doubly-fed induction generator, is the increment of the active power output of the doubly-fed induction generator, is the time constant of the filter loop, is and the error integral of. The state space matrix form is: , , , , , Among them, is the first derivative of, is the state matrix, is the control matrix, and are the system matrices.

[0029] The derivation of the state space model of the reactive power increment of the grid-side converter GSC is as follows: , Among them, is the increment of the rotor d-axis current, and are respectively the proportional gain and integral gain of the PI controller for the reactive power of the grid-side converter GSC, is the reference value of the stator reactive power, is the increment of the stator reactive power, is and the error integral of. The state space matrix form is: , , , , , Among them, is the first derivative of, is the state matrix of the stator reactive power, is the control matrix of the stator reactive power, and are the system matrices of the stator reactive power.

[0030] The derivation of the state space model of the reactive power increment of the grid-side converter GSC is as follows: , Among them, is the increment of, is the q-axis current of the grid-side converter GSC, is the time constant, and are the proportional gain and integral gain of the PI controller of the reactive power of the grid-side converter GSC respectively, is the reference value of the reactive power output by the converter , is the increment of the reactive power output by the converter , is the time constant, is and the error integral of; The state space equation can be expressed as: , , , , , Among them, is the first derivative of, is the state matrix of the reactive power output by the converter, is the control matrix of the reactive power output by the converter, and are the system matrices of the reactive power output by the converter.

[0031] According to the above formula, the sensitivity relationship between the output power of the wind turbine and the converter current can be expressed as: , , .

[0032] As the main energy conversion device in a wind turbine, the health status of the generator can be evaluated by monitoring its operating temperature. For a doubly-fed induction generator (DFIG), the losses mainly come from the copper losses generated by the winding current , specifically including stator copper loss and rotor copper loss : , Among them, is the stator resistance, is the rotor resistance.

[0033] According to and , the stator copper loss can be rewritten as: , Then the sensitivity relationship of the generator copper loss to the current can be expressed as: , , , , In addition, according to the above formula, the sensitivity relationship of the generator copper loss to the output power of the wind turbine can be expressed as: , .

[0034] In this embodiment, is the node heat source matrix of the generator. The nodes include the shaft, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and housing of the motor. Based on the lumped parameter concept, the distributed heat sources and thermal parameters inside the motor are simplified into several concentrated heat sources and thermal resistances. These include 11 key components: shaft, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and housing. Nodes are established for each region. The heat source is analogized to a current source, the node temperature is analogized to the electric potential, and the thermal resistance between nodes is analogized to a resistance, thus constructing an equivalent thermal network model of the wind turbine. By integrating the losses, heat transfer process and thermal resistance between nodes in the wind turbine, an 11th-order thermal balance equation of the equivalent thermal network model can be derived, which is expressed in matrix form as: , is the node temperature matrix of the generator, is the node heat source matrix of the generator, among which, is a matrix, which is composed of the reciprocals of the thermal resistances between nodes (the diagonal elements represent the total heat dissipation capacity of node , and the off-diagonal elements represent the thermal conductivity between node and node , taking negative values); is a node temperature matrix; is a node heat source matrix. By solving the heat balance equation, the temperature of the key nodes can be obtained. The stator winding temperature is selected as the representative node to evaluate the health status of the generator because it is the highest temperature in the generator. According to the above formula, the sensitivity relationship between the stator winding temperature and the copper loss of the generator can be expressed as: , , Based on the above analysis, the sensitivity relationship between the stator winding temperature and the output power of the wind turbine can be established: , , Then, the stator winding temperature rise model of the generator can be established: , In this embodiment, the temperature model of the generator characterizes the temperature of the generator by the stator winding temperature, and the functional expression of the temperature model of the generator is: , where is the stator winding temperature of the wind turbine, is the initial temperature, is the increment of the active power output of the generator , is the increment of the stator reactive power , is the stator winding temperature with respect to the sensitivity of the active power output of the generator , is the stator winding temperature with respect to the sensitivity of the stator reactive power , and there are: , , , , , , , , , , Among them, is the stator winding temperature with respect to the stator copper loss sensitivity of, is the stator copper loss with respect to the active power output of the generator sensitivity of, is the stator winding temperature with respect to the rotor copper loss sensitivity of, is the rotor copper loss with respect to the active power output of the generator sensitivity of, is the stator copper loss with respect to the stator reactive power sensitivity of, is the rotor copper loss with respect to the stator reactive power sensitivity of, is the 10th row and 10th column of the inverse matrix of, is the 10th row and 4th column of the inverse matrix of, is the matrix composed of the reciprocals of the thermal resistances between the nodes of the generator, , is the node temperature matrix of the generator, is the node heat source matrix of the generator. The nodes in the node heat source matrix include the shaft of the motor, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and housing (which can be partial for simplification); is the stator copper loss with respect to the q-axis current sensitivity of, is the q-axis current with respect to the active power output of the generator sensitivity of, is the stator copper loss with respect to the d-axis current sensitivity of, is the rotor d-axis current with respect to the stator reactive power sensitivity of, is the rotor resistance, and the calculation function expression of the stator copper loss is: , wherein, is the stator resistance, is the stator inductance, is the mutual inductance, is the d-axis current, is the q-axis current, is the stator flux linkage; the rotor copper loss has a calculation function expression of: , wherein, is the rotor resistance.

[0035] To reduce the impact of overheating faults, an association model between the converter temperature and power output is established. The converter consists of transistors and reverse diodes, and its losses can be divided into switching losses and conduction losses. The mechanical losses, copper losses, iron losses, and various stray losses generated during the operation of the wind turbine can be ignored. The losses of the converter include the rotor-side converter (RSC) losses and the grid-side converter (GSC) losses , and its expression is: , , , , , Define: , , Then the above formula can be simplified to: , wherein, represents the converter losses, including the rotor-side converter (RSC) losses and the grid-side converter (GSC) losses; and respectively represent the root mean square currents of the GSC and RSC; and respectively represent the root mean square values of the d-axis and q-axis currents of the GSC; and respectively represent the root mean square values of the d-axis and q-axis currents of the RSC; is the collector-emitter voltage of the IGBT; and are respectively the turn-on and turn-off losses of the IGBT; is the rated collector current of the IGBT; is the switching frequency; is the turn-off loss of the freewheeling diode; is the lead resistance of the IGBT. According to the above formula, the sensitivity relationship between the converter loss and the current can be expressed as: , , , wherein, is the initial value of the grid-side q-axis current at the initial moment, is the initial value of the rotor q-axis current at the initial moment, is the initial value of the rotor d-axis current at the initial moment.

[0036] According to the above formula, the sensitivity relationship between the converter loss and the output power of the wind turbine can be expressed as: , , , The loss of the converter is dissipated in the form of heat, and the heat is transferred to the external environment through the device junctions, housings and heat sinks of the internal components of the converter. The converter temperature model can be expressed as: , wherein, is the converter temperature, is the ambient temperature. According to the above formula, the sensitivity relationship between the converter temperature and the converter loss can be expressed as: , Based on the above analysis, the sensitivity relationship between the converter temperature and the output power can be established as follows: , , , , Then, the converter temperature rise model can be expressed as: , Therefore, in this embodiment, the functional expression of the temperature model of the converter is: , wherein, is the temperature of the converter, is the increment of the reactive power output by the converter ; For the temperature of the converter Regarding the output active power Sensitivity For the temperature of the converter Regarding the reactive power output of the converter Sensitivity; For the temperature of the converter Regarding the stator reactive power Sensitivity; , , , , Wherein, For the temperature of the converter Regarding the converter loss Sensitivity For the converter loss Regarding the output active power Sensitivity For the converter loss Regarding the reactive power output of the converter Sensitivity For the converter loss Regarding the stator reactive power Sensitivity Is the total thermal resistance of the converter, and the calculation function expression of the total thermal resistance of the converter is: , Wherein, Is the thermal resistance from the Layer to the housing, Is the thermal resistance from the housing to the radiator, Is the thermal resistance from the radiator to the external environment, Is the number of physical structure layers between the chip device junction and the housing of the converter. For example, in this embodiment, the number of physical structure layers between the chip device junction and the housing of the converter is 4. The physical structure (Cauer model) from the chip to the housing inside the corresponding module includes: when i = 1, it is the IGBT / diode chip layer (silicon). When i = 2, it is the welding layer / interface material (such as solder or sintered silver). When i = 3, it is the substrate layer (copper or aluminum). When i = 4, it is the insulation layer (aluminum nitride or alumina).

[0037] In this embodiment, the calculation function expression of the converter loss Is: , , , Wherein, and is an intermediate variable, is the root mean square current of the grid-side converter, is the root mean square current of the rotor-side converter, is the collector-emitter voltage of the IGBT, and are the turn-on and turn-off losses of the IGBT respectively, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the reverse diode of the IGBT, is the lead resistance of the IGBT.

[0038] The multi-objective optimization problem in this embodiment is used to optimize the health status of the wind turbine and obtain the best trade-off between the operating performance and the health performance. For a wind farm, the output active power should meet the power demand of the Transmission System Operator (TSO), and the given reference power of each wind turbine should not exceed its maximum available power. Therefore, the multi-objective optimization problem in step S2 also includes the following constraint conditions: , wherein, is the active power of the th wind turbine, is the active power dispatched, is the reference active power of the th wind turbine, is the th wind turbine's maximum active power, is the th wind turbine's reference reactive power, and are the minimum reactive power and the maximum reactive power of the th wind turbine respectively.

[0039] The calculation function expression of the service quality control index of the wind turbine group in step S3 of this embodiment is: , , , wherein, is the service quality control index of the wind turbine group, , , , , and are weight coefficients, is The normalized result is for The normalized result is is the health indicator at the wind turbine level, is the system-level operating performance indicator. is the temperature of the generator The amount of change, is the temperature of the converter The amount of change, Output active power to the generator The increment of Terminal voltage The calculation function expression of the normalized result is: , in, for The normalized result is for or , [ ] represents the normal operating range of the parameter. In addition, the wind turbine group service quality control index is an expandable system, and more indicators and control targets can be added in the future. In this embodiment, the wind turbine group service quality control index The interval is [0,1]; it should be noted that the larger the parameter value, the smaller its adjustable margin. This shows that when the operating status of the wind turbine deteriorates, priority should be given to reducing the load or shutting down to ensure timely repair and maintenance. In order to solve the problem that indicators exceeding the threshold may be masked by other health indicators, resulting in invalid evaluation results, a threshold judgment mechanism is adopted: when all indicators are within the normal range, the service quality index is calculated using appropriate weights. However, when any indicator exceeds the threshold, the protection mechanism of the wind turbine will be triggered. By weighted summing the service quality index of the wind turbines in the wind farm, the service quality index of the wind farm can be obtained, so as to evaluate its operating status in real time.

[0040] As an optional implementation, step S3 of this embodiment further includes detecting the temperature rise of the wind turbine generators of each wind turbine group. , Converter temperature rise and voltage increment If any of the above three items exceeds the preset threshold, the weight coefficient corresponding to the item in the multi-objective optimization problem is increased to strengthen the monitoring of the temperature rise or increment of the item. The larger the corresponding item value, the larger the weight coefficient of the unit, and when solving the optimization problem, more emphasis is placed on reducing the impact of the output power on this item. Static weights cannot adapt to complex and changeable operating scenarios. The core goal of dynamic adjustment is: if a certain indicator is frequently abnormal recently, its weight needs to be automatically increased to strengthen monitoring.

[0041] To verify the method for regulating the service quality of a wind turbine cluster for high-quality power generation in this embodiment, the method for regulating the service quality of a wind turbine cluster for high-quality power generation is simulated in this embodiment. The obtained simulation results are respectively as follows Figures 4 to 6 shown, where Figure 4 is the schematic diagram of the generator temperature of the method in this embodiment, Figure 5 is the schematic diagram of the converter temperature of the method in this embodiment, Figure 6 is the schematic diagram of the terminal voltage of the wind turbine unit of the method in this embodiment. Referring to Figures 4 to 6 the simulation results of, it can be seen that after adopting the method for regulating the service quality of a wind turbine cluster for high-quality power generation in this embodiment, the method in this embodiment constructs a multi-objective optimization model by real-time monitoring the terminal voltage deviation of the wind turbine unit and the temperatures of key components (such as converters and generators), and solves to obtain the optimal active power reference value and the reactive power reference value , realizing precise regulation of the wind turbine unit. In addition, based on the real-time data (including the deviation between the terminal voltage, output power and the power dispatch instruction TSO, and the temperatures of key components) fed back by the wind farm, the method in this embodiment dynamically calculates the service quality index of the wind turbine unit, evaluates the operating state of the unit in real time, and optimizes the objective function through an adaptive weight adjustment mechanism to ensure the best balance between the health of the unit and the system performance under different operating conditions. On the premise of ensuring the operating performance of the wind turbine unit (including maintaining the bus voltage within the preset range and meeting the power demand of the external power grid), the method in this embodiment significantly reduces the operating temperatures of the key components of the wind turbine unit, thereby effectively improving the health performance and service life of the unit, and enhancing the operating reliability, economy and equipment life of the wind farm, especially suitable for the scenario of high-penetration wind power grid connection.

[0042] In addition, this embodiment also provides a system for regulating the service quality of a wind turbine cluster for high-quality power generation, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the method for regulating the service quality of a wind turbine cluster for high-quality power generation.

[0043] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the method for regulating the service quality of a wind turbine cluster for high-quality power generation through a processor.

[0044] In addition, this embodiment also provides a computer program product, including a computer program or instruction, and the computer program or instruction is programmed or configured to execute the method for regulating the service quality of a wind turbine cluster for high-quality power generation through a processor.

[0045] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A method for regulating the service quality of a wind turbine cluster for high-quality power generation, characterized in that, Including the following steps: S1. Establish temperature models of the generator and the converter of the wind turbine by combining the thermodynamic mechanism and energy loss respectively. The temperature models of the generator and the converter are function models regarding the active power and reactive power of the wind turbine. S2. Solve the multi-objective optimization problem shown in the following formula by combining the temperature models of the generator and the converter to obtain the optimal power generation strategy of the wind turbine. ， ， ， ， Among them, is the objective function of the multi-objective optimization problem, is the objective function of the temperature rise of the wind turbine, is the objective function of the temperature rise of the converter, is the objective function of the voltage increment, is the prediction step size, is the number of wind turbines, is the weight coefficient of the temperature rise of the wind turbine, is the temperature of the generator in the th wind turbine unit, is the ambient temperature, is the weight coefficient of the temperature rise of the converter, is the temperature of the converter in the th wind turbine unit, is the weight coefficient of the voltage increment, and are respectively the terminal voltage and the reference voltage of the th wind turbine unit at the current moment ; S3. Send the optimal power generation strategy of the wind turbine to the wind turbine for execution, calculate the service quality control index of the wind turbine group according to the state information of the wind farm after executing the optimal power generation strategy. If the service quality control index of the wind turbine group exceeds the preset threshold, execute the fan protection mechanism, and the fan protection mechanism refers to executing the load reduction or emergency shutdown of the fan.

2. The method for regulating the service quality of a wind turbine group for high-quality power generation according to claim 1, wherein The generator of the wind turbine is a doubly-fed induction generator. The temperature model of the generator characterizes the temperature of the generator by the stator winding temperature of the generator. The function expression of the temperature model of the generator is: ， Wherein, is the stator winding temperature of the wind turbine generator, is the initial temperature, is the increment of the active power output of the generator , is the increment of the stator reactive power , is the stator winding temperature with respect to the active power output of the generator sensitivity, is the stator winding temperature with respect to the stator reactive power sensitivity, and there is: ， ， ， ， ， ， ， ， ， ， Among them, is the stator winding temperature Regarding the sensitivity of the stator copper loss to is the stator copper loss Regarding the sensitivity of the active power output of the generator to is the stator winding temperature Regarding the sensitivity of the rotor copper loss to is the rotor copper loss Regarding the sensitivity of the active power output of the generator to is the stator copper loss Regarding the sensitivity of the stator reactive power to is the rotor copper loss Regarding the sensitivity of the stator reactive power to is The 10th row and 10th column of the inverse matrix of is The 10th row and 4th column of the inverse matrix of is the matrix composed of the reciprocals of the thermal resistances between the nodes of the generator, , is the node temperature matrix of the generator, is the node heat source matrix of the generator. The nodes in the node heat source matrix include the shaft, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and housing of the motor; is the stator copper loss Regarding the sensitivity of the stator copper loss to the q-axis current is the q-axis current Regarding the sensitivity of the active power output of the generator to is the stator copper loss Regarding the sensitivity of the stator copper loss to the d-axis current is the rotor d-axis current Regarding the sensitivity of the rotor d-axis current to the stator reactive power is the rotor resistance. The calculation function expression of the stator copper loss is: ， Among them, is the stator resistance, is the stator inductance, is the mutual inductance, is the d-axis current, is the q-axis current, is the stator flux linkage; the rotor copper loss The calculation function expression of is: ， Among them, is the rotor resistance.

3. The method for regulating the service quality of a wind turbine group for high-quality power generation according to claim 1, wherein The function expression of the temperature model of the converter is: ， Among them, is the temperature of the converter, is the increment of the reactive power output by the converter ; is the temperature of the converter with respect to the sensitivity of the active power output ; is the temperature of the converter with respect to the sensitivity of the reactive power output by the converter ; is the temperature of the converter with respect to the sensitivity of the stator reactive power ; ， ， ， ， Among them, is the temperature of the converter Regarding the sensitivity of the converter loss is the sensitivity of the converter loss with respect to the output active power is the sensitivity of the converter loss with respect to the converter output reactive power is the sensitivity of the converter loss with respect to the stator reactive power is is the total thermal resistance of the converter, and the calculation function expression of the total thermal resistance of the converter is: ， Among them, is the thermal resistance from the layer to the housing, is the thermal resistance from the housing to the radiator, is the thermal resistance from the radiator to the external environment, is the number of physical structure layers between the chip device junction of the converter and the housing.

4. The method for regulating the service quality of a wind turbine group for high-quality power generation according to claim 3, wherein The converter losses The calculation function expression is as follows: ， ， ， Among them, and are intermediate variables, is the root mean square current of the grid-side converter, is the root mean square current of the rotor-side converter, is the collector-emitter voltage of the IGBT, and are the turn-on and turn-off losses of the IGBT respectively, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the reverse diode of the IGBT, is the lead resistance of the IGBT.

5. The method for regulating the service quality of a wind turbine group for high-quality power generation according to claim 1, characterized in that, The multi-objective optimization problem in step S2 further includes the following constraint conditions: ， Among them, is the active power of the th wind turbine, is the active power dispatched, is the reference active power of the th wind turbine, is the th wind turbine's maximum active power, is the reference reactive power of the th wind turbine, and are respectively the minimum reactive power and the maximum reactive power of the th wind turbine.

6. The method for regulating the service quality of a wind turbine group for high-quality power generation according to claim 1, wherein The calculation function expression of the service quality control index of the wind turbine group in step S3 is: ， ， ， Among them, is the service quality control index of the wind turbine group, , , , , and are the weight coefficients, is 's normalization result, is 's normalization result, is the health index at the wind turbine level, is the operation performance index at the system level, is the temperature of the generator 's change amount, is the temperature of the converter 's change amount, is the incremental active power output of the generator 's increment, is the terminal voltage 's increment.

7. The method for regulating the service quality of a wind turbine cluster for high-quality power generation according to claim 1, characterized in that, In step S3, it also includes detecting the temperature rise of the wind turbines of each wind turbine generator respectively , the temperature rise of the converter and the voltage increment . If any one of the above three items exceeds the preset threshold, the weight coefficient corresponding to this item in the multi-objective optimization problem is increased to strengthen the monitoring of the temperature rise or increment of this item.

8. A service quality control system for a wind turbine group for high-quality power generation, comprising a microprocessor and a memory connected to each other, characterized in that, The microprocessor is programmed or configured to execute the service quality control method for the wind turbine group for high-quality power generation according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instruction is programmed or configured to execute the service quality control method for the wind turbine group for high-quality power generation according to any one of claims 1 to 7 through a processor.

10. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instruction is programmed or configured to execute the service quality control method for the wind turbine group for high-quality power generation according to any one of claims 1 to 7 through a processor.

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