A wind turbine group service quality control method and system for high-quality power generation

Abstract

The present invention discloses a method and system for controlling the service quality of a wind turbine group for high-quality power generation. The method includes establishing temperature models of the generator and converter of the wind turbine group in combination with thermodynamic mechanisms and energy losses, wherein the temperature models of the generator and converter are function models of the active power and reactive power of the wind turbine group; solving a multi-objective optimization problem in combination with the temperature models of the generator and converter to obtain the optimal power generation strategy of the wind turbine group, wherein the multi-objective optimization consists of three problems: temperature rise of the wind turbine generator, temperature rise of the converter, and voltage increment; sending the optimal power generation strategy to the wind turbine group for execution, calculating the service quality control index of the wind turbine group based on the wind farm status information after execution, and executing the wind turbine protection mechanism if it exceeds a preset threshold. The present invention aims to optimize the power generation strategy and improve the reliability and economy of wind farm operation by dynamically coordinating the health status of the unit with system-level performance indicators.

Description

Technical Field

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

[0002] As wind power capacity surpasses 800 GW, modern wind power systems, characterized by clustered large wind turbines, face three technical bottlenecks: the inherent random fluctuations in wind power output and the rigid power requirements of the power grid; reliability degradation caused by accelerated degradation of key components under extreme operating conditions; and economic constraints, with lifecycle operation and maintenance costs exceeding 25%. Of particular note, the current service quality management system suffers from significant structural flaws. The traditional discrete management paradigm for individual units emphasizes monitoring the health of individual equipment while ignoring system-level dynamic interactions such as inter-unit aerodynamic interference and grid coupling effects. This results in a "1+1<2" synergistic loss in overall wind farm performance. This single-dimensional management strategy is unable to meet the dual requirements of high-reliability output and low operation and maintenance costs imposed by the new power system. A dynamic service quality control system based on multi-dimensional collaborative optimization is urgently needed to achieve a two-way, real-time balance between equipment-level health and site-level operational efficiency. Summary of the Invention

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

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for controlling the service quality of a wind turbine group for high-quality power generation includes the following steps:

[0006] S1, respectively establishing temperature models of the generator and converter of the wind turbine generator set by combining thermodynamic mechanism and energy loss, wherein the temperature models of the generator and converter are function models of active power and reactive power of the wind turbine generator set;

[0007] S2, combined with the temperature models of the generator and converter, solves the multi-objective optimization problem shown in the following equation to obtain the optimal power generation strategy of the wind turbine:

[0008] ,

[0009] ,

[0010] ,

[0011] ,

[0012] in, is the objective function of the multi-objective optimization problem, is the objective function of wind turbine temperature rise, is the objective function of converter temperature rise, is the objective function of voltage increment, is the prediction step length, is the number of wind turbines, is the weight coefficient of wind turbine temperature rise, For the The temperature of the generator in each wind turbine, is the ambient temperature, is the weight coefficient of the converter temperature rise, For the The temperature of the converter in each wind turbine, is the weight coefficient of voltage increment, and Respectively Wind turbines at the current time The terminal voltage and reference voltage;

[0013] S3, sending the optimal power generation strategy of the wind turbine group to the wind turbine group for execution, calculating the service quality control index of the wind turbine group based on the wind farm status information after executing the optimal power generation strategy, and executing the wind turbine protection mechanism if the service quality control index of the wind turbine group exceeds the preset threshold, which refers to executing the load reduction or emergency shutdown of the wind turbine.

[0014] Optionally, the generator of the wind turbine is a doubly-fed induction generator, and the temperature model of the generator uses the stator winding temperature of the generator to characterize the temperature of the generator. The function expression of the temperature model of the generator is:

[0015] ,

[0016] in, is the stator winding temperature of the wind turbine, is the initial temperature, Output active power to the generator The increment of is the stator reactive power The increment of is the stator winding temperature About the generator output active power Sensitivity, is the stator winding temperature About stator reactive power sensitivity, and has:

[0017] ,

[0018] ,

[0019] , ,

[0020] , ,

[0021] ,

[0022] , ,

[0023] ,

[0024] in, is the stator winding temperature About stator copper loss Sensitivity, Stator copper loss About the generator output active power Sensitivity, is the stator winding temperature About rotor copper loss Sensitivity, is the rotor copper loss About the generator output active power Sensitivity, Stator copper loss About stator reactive power Sensitivity, is the rotor copper loss About stator reactive power Sensitivity, for The 10th row and 10th column of the inverse matrix of for The 10th row and 4th column of the inverse matrix of is a matrix composed of the inverse of the thermal resistance between the nodes of the generator, , is the node temperature matrix of the generator, is a nodal heat source matrix of the generator, where nodes in the nodal heat source matrix include a shaft, a stator yoke, stator teeth, a stator winding, a stator winding end, an end space, a rotor yoke, a rotor teeth, a rotor winding, an air gap, and a housing of the motor; Stator copper loss About q-axis current Sensitivity, is the q-axis current About the generator output active power Sensitivity, Stator copper loss About d-axis current Sensitivity, is the rotor d-axis current About stator reactive power Sensitivity, is the rotor resistance, the stator copper loss The calculation function expression is:

[0025] ,

[0026] in, is the stator resistance, is the stator inductance, For mutual induction, is the d-axis current, is the q-axis current, is the stator flux; is the rotor copper loss About q-axis current Sensitivity, is the rotor copper loss About d-axis current The sensitivity of the rotor copper loss The calculation function expression is:

[0027] ,

[0028] in, is the rotor resistance.

[0029] Optionally, the function expression of the temperature model of the converter is:

[0030] ,

[0031] in, is the temperature of the converter, Output reactive power to the converter the increment; is the temperature of the converter About output active power Sensitivity, is the temperature of the converter About the reactive power output of the converter sensitivity; is the temperature of the converter About stator reactive power sensitivity;

[0032] , ,

[0033] , ,

[0034] in, is the temperature of the converter About converter losses Sensitivity, is the converter loss About output active power Sensitivity, is the converter loss About the reactive power output of the converter Sensitivity, is the converter loss About 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:

[0035] ,

[0036] in, For the Thermal resistance from layer to case, is the thermal resistance from case to heat sink, is the thermal resistance from the heat sink to the external environment, It is the number of physical structure layers between the chip device junction and the casing of the converter.

[0037] Optionally, the converter loss The calculation function expression is:

[0038] ,

[0039] ,

[0040] ,

[0041] in, and is an intermediate variable, is the RMS current of the grid-side converter, is the RMS 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, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the IGBT's reverse diode, is the lead resistance of the IGBT.

[0042] Optionally, the multi-objective optimization problem in step S2 further includes the following constraints:

[0043] ,

[0044] in, For the The active power of a wind turbine, is the active power dispatched, For the The reference active power of each wind turbine is For the The maximum active power of a wind turbine, For the The reference reactive power of each wind turbine is and Respectively The minimum reactive power and maximum reactive power of each wind turbine.

[0045] Optionally, the calculation function expression of the wind turbine group service quality control index in step S3 is:

[0046] ,

[0047] ,

[0048] ,

[0049] in, It is the service quality control index of wind turbine group. 、 、 、 、 and is the weight coefficient, for The normalized result of for The normalized result of is the health indicator at the wind turbine level, is the system-level 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 increment.

[0050] Optionally, step S3 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 will be increased to strengthen the monitoring of the temperature rise or increment.

[0051] In addition, the present invention also provides a wind turbine group service quality control system for high-quality power generation, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the wind turbine group service quality control method for high-quality power generation.

[0052] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the wind turbine group service quality control method for high-quality power generation through a processor.

[0053] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the wind turbine group service quality control method for high-quality power generation through a processor.

[0054] Compared with the existing technology, the present invention can achieve the following beneficial effects: the present invention builds a multi-objective optimization model by monitoring the terminal voltage deviation of the wind turbine and the temperature of key components (such as converters and generators) in real time, and solves the optimal active power reference value. and reactive power reference , achieving precise control of wind turbines. In addition, based on the real-time data fed back by the wind farm (including the terminal voltage, the deviation between the output power and the TSO power demand, and the temperature of key components), the present invention dynamically calculates the service quality indicators of the wind turbine, evaluates the operating status of the unit in real time, and optimizes the objective function through an adaptive weight adjustment mechanism to ensure the best balance between unit health and system performance under different operating conditions. It can significantly reduce the operating temperature of key components of the wind turbine while ensuring the operating performance of the wind turbine (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, improving the operational reliability, economy and equipment life of the wind farm, and is particularly suitable for high-penetration wind power grid-connected scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0056] Figure 2 Schematic diagram of the system architecture of the method according to an embodiment of the present invention.

[0057] Figure 3 Schematic diagram of the control principles of the rotor-side converter RSC and the grid-side converter GSC in an embodiment of the present invention, wherein (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.

[0058] Figure 4 Schematic diagram of the generator temperature obtained by simulation in an embodiment of the present invention.

[0059] Figure 5 FIG. 4 is a schematic diagram of the converter temperature obtained by simulation in an embodiment of the present invention.

[0060] Figure 6 Schematic diagram of wind turbine terminal voltage obtained by simulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0062] like Figure 1 As shown, the wind turbine group service quality control method for high-quality power generation in this embodiment includes the following steps:

[0063] S1, respectively establishing temperature models of the generator and converter of the wind turbine generator set by combining thermodynamic mechanism and energy loss, wherein the temperature models of the generator and converter are function models of active power and reactive power of the wind turbine generator set;

[0064] S2, combined with the temperature models of the generator and converter, solves the multi-objective optimization problem shown in the following equation to obtain the optimal power generation strategy of the wind turbine:

[0065] ,

[0066] ,

[0067] ,

[0068] ,

[0069] in, is the objective function of the multi-objective optimization problem, is the objective function of wind turbine temperature rise, is the objective function of converter temperature rise, is the objective function of voltage increment, is the prediction step length, is the number of wind turbines, is the weight coefficient of wind turbine temperature rise, For the The temperature of the generator in each wind turbine, is the ambient temperature, is the weight coefficient of the converter temperature rise, For the The temperature of the converter in each wind turbine, is the weight coefficient of voltage increment, and Respectively Wind turbines at the current time The terminal voltage and reference voltage are:

[0070] ,

[0071] in, For the Wind turbines at the current time k The voltage value, For the The voltage of a wind turbine The initial time value of For the The active power output of each wind turbine at the current moment, For the The voltage of a wind turbine Active power Sensitivity, For the Wind turbines at the current time k The active power increase, For the The reactive power of a wind turbine, For the The voltage of a wind turbine Reactive power Sensitivity, For the Wind turbines at the current time k The reactive power increase, , is the number of wind turbines;

[0072] S3, sending the optimal power generation strategy of the wind turbine group to the wind turbine group for execution, calculating the service quality control index of the wind turbine group based on the wind farm status information after executing the optimal power generation strategy, and executing the wind turbine protection mechanism if the service quality control index of the wind turbine group exceeds the preset threshold, which refers to executing the load reduction or emergency shutdown of the wind turbine.

[0073] like Figure 2 As 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. 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, in step S3, the optimal power generation strategy of the wind turbine group is sent to the wind turbine group for execution. The service quality model is called to calculate the service quality control index of the wind turbine group according to the wind farm status information after the execution of the optimal power generation strategy. If the service quality control index of the wind turbine group exceeds the preset threshold, the wind turbine protection mechanism is executed. The wind turbine protection mechanism refers to the execution of load reduction or emergency shutdown of the wind turbine. In addition, in this embodiment, the service quality model is called to also provide various sensitivity data, including the sensitivity of voltage V to active power P and reactive power Q, the sensitivity of temperature T to active power P and reactive power Q, and active power tracking, wherein is the reference value of active power obtained by active power tracking, is the rotor angle.

[0074] In this embodiment, the generator of the wind turbine is a doubly-fed induction generator (DFIG). The characteristic of a DFIG is that the stator is directly connected to the grid, and the rotor is connected through back-to-back PWM converters (rotor-side converter RSC and grid-side converter GSC). The rotor-side converter RSC uses stator flux-oriented vector control to achieve decoupling regulation of active / reactive power, while the grid-side converter GSC uses grid voltage-oriented control to achieve DC bus voltage stability and grid-side reactive power regulation. In the stator flux-oriented dq reference frame, the stator voltage and flux equations are:

[0075] ,

[0076] in, and They represent the dq axis voltage and dq axis stator flux of the stator respectively; and They represent the dq axis current of the stator and the dq axis current of the rotor respectively; Corresponding to stator resistance, stator inductance and mutual inductance respectively; is the synchronous angular velocity. By aligning the dq-axis stator flux to the d-axis and ignore the stator resistance , the equation is simplified to:

[0077] , ,

[0078] Further derive the stator current expression:

[0079] , .

[0080] This decoupled rotor current model provides a theoretical basis for the power control of the rotor-side converter (RSC).

[0081] Active power output of a doubly-fed induction generator for:

[0082] ,

[0083] The stator active power output is the first term in the above formula, and the grid-side converter GSC active power output is the second term. By adjusting the rotor d-axis current To control, its calculation formula can be derived and expressed as:

[0084] ,

[0085] in, is the stator flux, It is mutual induction, is the source angular velocity, is the stator inductance of the doubly-fed induction generator, is the q-axis rotor voltage, is the rotor q-axis current.

[0086] In the synchronous rotating reference frame oriented to the grid voltage, the reactive power It can be modeled as:

[0087] ,

[0088] in, is the grid phase voltage amplitude.

[0089] Figure 3 Schematic diagram of the control principle of the rotor-side converter RSC and the grid-side converter GSC in this embodiment.

[0090] The state space model of the active power increment of the rotor-side converter RSC is as follows:

[0091] ,

[0092] in, Indicates the increment, is the increment of the rotor q-axis current, To represent the complex frequency domain variables in Laplace transform, is the time constant of the current loop, and are the proportional gain and integral gain of the PI controller for 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 active power output of the doubly fed induction generator, is the time constant of the filter loop, for and The error integral of . The state space matrix form is:

[0093] ,

[0094] ,

[0095] ,

[0096] ,

[0097] ,

[0098] in, for The first derivative of is the state matrix, is the control matrix, and is the system matrix.

[0099] The reactive power increment state space model of the grid-side converter GSC is derived as follows:

[0100] ,

[0101] in, is the increment of the rotor d-axis current, and are the proportional gain and integral gain of the PI controller of the reactive power of the grid-side converter GSC, is the reference value of stator reactive power, is the increment of stator reactive power, for and The error integral of . The state space matrix form is:

[0102] ,

[0103] ,

[0104] ,

[0105] ,

[0106] ,

[0107] in, for The first derivative of is the state matrix of stator reactive power, is the control matrix of stator reactive power, and is the system matrix of stator reactive power.

[0108] The reactive power increment state space model of the grid-side converter GSC is derived as follows:

[0109] ,

[0110] in, for 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, Output reactive power to the converter The reference value, Output reactive power to the converter The increment of is the time constant, for and The error integral of ; the state space equation can be expressed as:

[0111] ,

[0112] ,

[0113] ,

[0114] ,

[0115] ,

[0116] in, for The first derivative of is the state matrix of the converter output reactive power, is the control matrix for the converter output reactive power, and The system matrix for the converter output reactive power.

[0117] According to the above formula, the sensitivity relationship between the wind turbine output power and the converter current can be expressed as:

[0118] ,

[0119] ,

[0120] .

[0121] As the main energy conversion device in wind turbines, the health of the generator can be evaluated by monitoring its operating temperature. For a doubly fed induction generator (DFIG), the loss mainly comes from the copper loss generated by the winding current. , specifically including stator copper loss and rotor copper loss :

[0122] ,

[0123] in, is the stator resistance, is the rotor resistance.

[0124] according to and , stator copper loss can be rewritten as:

[0125] ,

[0126] Then the sensitivity relationship between the generator copper loss and the current can be expressed as:

[0127] ,

[0128] ,

[0129] ,

[0130] ,

[0131] In addition, according to the above formula, the sensitivity relationship between generator copper loss and wind turbine output power can be expressed as:

[0132] ,

[0133] .

[0134] In this embodiment, It is the node heat source matrix of the generator, and the nodes include the motor shaft, stator yoke, stator teeth, stator winding, stator winding end, end space, rotor yoke, rotor teeth, rotor winding, air gap and casing. Based on the concept of lumped parameters, 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 casing. Nodes are established for each area. The heat source is analogized to a current source, the node temperature is analogized to an electric potential, and the thermal resistance between nodes is analogized to a resistor, thereby constructing an equivalent thermal network model of a wind turbine. By integrating the losses, heat transfer processes and thermal resistances between nodes in the wind turbine, the 11th-order thermal balance equation of the equivalent thermal network model can be derived, which is expressed in matrix form as:

[0135] , is the node temperature matrix of the generator, is the node heat source matrix of the generator, where is a The matrix consists of the inverse of the thermal resistance between nodes (diagonal elements Representation node Total heat dissipation capacity, off-diagonal elements Representation node and nodes The thermal conductivity between , takes a negative value); is a The node temperature matrix; is a The node heat source matrix of the stator winding is obtained by solving the heat balance equation. As a representative node to evaluate the health status of the generator, it is the highest temperature in the generator. According to the above formula, the sensitivity relationship between the stator winding temperature and the generator copper loss can be expressed as:

[0136] , ,

[0137] Based on the above analysis, the sensitivity relationship between stator winding temperature and wind turbine output power can be established:

[0138] ,

[0139] ,

[0140] Then, the temperature rise model of the generator stator winding can be established:

[0141] ,

[0142] In this embodiment, the temperature model of the generator uses the stator winding temperature of the generator to represent the temperature of the generator. The function expression of the temperature model of the generator is:

[0143] ,

[0144] in, is the stator winding temperature of the wind turbine, is the initial temperature, Output active power to the generator The increment of is the stator reactive power The increment of is the stator winding temperature About the generator output active power Sensitivity, is the stator winding temperature About stator reactive power sensitivity, and:

[0145] ,

[0146] ,

[0147] , ,

[0148] , ,

[0149] ,

[0150] , ,

[0151] ,

[0152] in, is the stator winding temperature About stator copper loss Sensitivity, Stator copper loss About the generator output active power Sensitivity, is the stator winding temperature About rotor copper loss Sensitivity, is the rotor copper loss About the generator output active power Sensitivity, Stator copper loss About stator reactive power Sensitivity, is the rotor copper loss About stator reactive power Sensitivity, for The 10th row and 10th column of the inverse matrix of for The 10th row and 4th column of the inverse matrix of is a matrix composed of the inverse of the thermal resistance between the nodes of the generator, , is the node temperature matrix of the generator, is a nodal heat source matrix of the generator, where nodes in the nodal 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 (which may be partial for simplification); Stator copper loss About q-axis current Sensitivity, is the q-axis current About the generator output active power Sensitivity, Stator copper loss About d-axis current Sensitivity, is the rotor d-axis current About stator reactive power Sensitivity, is the rotor resistance, the stator copper loss The calculation function expression is:

[0153] ,

[0154] in, is the stator resistance, is the stator inductance, For mutual induction, is the d-axis current, is the q-axis current, is the stator flux; is the rotor copper loss About q-axis current Sensitivity, is the rotor copper loss About d-axis current The sensitivity of the rotor copper loss The calculation function expression is:

[0155] ,

[0156] in, is the rotor resistance.

[0157] To reduce the impact of overheating, a correlation model between converter temperature and power output was 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. Converter losses Including rotor-side converter (RSC) losses and grid-side converter (GSC) losses , whose expression is:

[0158] ,

[0159] ,

[0160] ,

[0161] ,

[0162] ,

[0163] definition:

[0164] , ,

[0165] The above formula can be simplified to:

[0166] ,

[0167] in, represents the converter loss, including rotor-side converter (RSC) loss and grid-side converter (GSC) loss; and denote the RMS currents of GSC and RSC, respectively; and denote the RMS values ​​of the d-axis and q-axis currents of the GSC, respectively; and represent the RMS values ​​of the d-axis and q-axis currents of the RSC, respectively; is the collector-emitter voltage of the IGBT; and They are the turn-on and turn-off losses of the IGBT respectively; is the rated collector current of the IGBT; is the switching frequency; is the turn-off loss of the reverse 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:

[0168] ,

[0169] ,

[0170] ,

[0171] in, is the grid-side q-axis current The initial time value of is the rotor q-axis current The initial time value of is the rotor d-axis current The initial time value of .

[0172] According to the above formula, the sensitivity relationship between converter loss and wind turbine output power can be expressed as:

[0173] ,

[0174] ,

[0175] ,

[0176] The losses of the converter are dissipated in the form of heat, which is transferred to the external environment through the device junctions of the converter's internal components, the casing, and the heat sink. The converter temperature model can be expressed as:

[0177] ,

[0178] in, is the converter temperature, is the ambient temperature. According to the above formula, the sensitivity relationship between the converter temperature and converter loss can be expressed as:

[0179] ,

[0180] Based on the above analysis, the sensitivity relationship between converter temperature and output power can be established as follows:

[0181] , ,

[0182] , ,

[0183] Then, the converter temperature rise model can be expressed as:

[0184] ,

[0185] Therefore, in this embodiment, the function expression of the temperature model of the converter is:

[0186] ,

[0187] in, is the temperature of the converter, Output reactive power to the converter increment; is the temperature of the converter About output active power Sensitivity, is the temperature of the converter About the reactive power output of the converter sensitivity; is the temperature of the converter About stator reactive power sensitivity;

[0188] , ,

[0189] , ,

[0190] in, is the temperature of the converter About converter losses Sensitivity, is the converter loss About output active power Sensitivity, is the converter loss About the reactive power output of the converter Sensitivity, is the converter loss About 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:

[0191] ,

[0192] in, For the Thermal resistance from layer to case, is the thermal resistance from case to heat sink, is the thermal resistance from the heat sink to the external environment, is the number of physical layers between the converter chip junction and the housing. For example, in this embodiment, the number of physical layers between the converter chip junction and the housing is four. The corresponding physical structure (Cauer model) from the chip to the housing within the module includes: when i = 1, the IGBT / diode chip layer (silicon); when i = 2, the solder layer / interface material (such as solder or sintered silver); when i = 3, the substrate layer (copper or aluminum); and when i = 4, the insulating layer (aluminum nitride or aluminum oxide).

[0193] In this embodiment, the converter loss The calculation function expression is:

[0194] ,

[0195] ,

[0196] ,

[0197] in, and is an intermediate variable, is the RMS current of the grid-side converter, is the RMS 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, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the IGBT's reverse diode, is the lead resistance of the IGBT.

[0198] The multi-objective optimization problem in this embodiment is used to optimize the health of wind turbines and obtain the best trade-off between the multiple objectives of operational performance and health performance. For a wind farm, the output active power should meet the power requirements 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 constraints:

[0199] ,

[0200] in, For the The active power of a wind turbine, is the active power dispatched, For the The reference active power of each wind turbine is For the The maximum active power of a wind turbine, For the The reference reactive power of each wind turbine is and Respectively The minimum reactive power and maximum reactive power of each wind turbine.

[0201] The calculation function expression of the wind turbine group service quality control index in step S3 of this embodiment is:

[0202] ,

[0203] ,

[0204] ,

[0205] in, It is the service quality control index of wind turbine group. 、 、 、 、 and is the weight coefficient, for The normalized result of for The normalized result of is the health indicator at the wind turbine level, is the system-level 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:

[0206] ,

[0207] in, for The normalized result of 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 indicates that when the operating status of a wind turbine deteriorates, load reduction or shutdown should be prioritized to ensure timely repair and maintenance. To address the problem that indicators exceeding the threshold may be masked by other health indicators, resulting in invalid assessment 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 wind turbine protection mechanism is triggered. By taking the weighted sum of the service quality indices of the wind turbines in a wind farm, the wind farm's service quality index can be obtained, allowing real-time assessment of its operating status.

[0208] 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 three items above exceeds the preset threshold, the corresponding weight coefficient in the multi-objective optimization problem is increased to strengthen monitoring of the temperature rise or increment. The larger the corresponding item value, the greater the unit's weight coefficient, 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 changing operating scenarios. The core goal of dynamic adjustment is: if a certain indicator has frequently abnormalities recently, its weight needs to be automatically increased to strengthen monitoring.

[0209] In order to verify the wind turbine group service quality control method for high-quality power generation in this embodiment, the wind turbine group service quality control method for high-quality power generation in this embodiment is simulated, and the simulation results are as follows: Figures 4 to 6 As shown, Figure 4 This is a schematic diagram of the generator temperature in this embodiment. Figure 5 is a schematic diagram of the converter temperature of the method of this embodiment, Figure 6 This is a schematic diagram of the wind turbine terminal voltage in this embodiment. Figures 4 to 6 The simulation results show that after adopting the wind turbine service quality control method for high-quality power generation in this embodiment, the method of this embodiment builds a multi-objective optimization model by real-time monitoring of the terminal voltage deviation of the wind turbine and the temperature of key components (such as converters and generators) to solve the optimal active power reference value. and reactive power reference , achieving precise control of wind turbines. In addition, the method of this embodiment is based on real-time data fed back by the wind farm (including terminal voltage, output power and deviation from the power dispatch instruction TSO, and temperature of key components). This embodiment dynamically calculates the service quality index of the wind turbine, evaluates the operating status of the unit in real time, and optimizes the objective function through an adaptive weight adjustment mechanism to ensure the optimal balance between unit health and system performance under different operating conditions. While ensuring the operating performance of the wind turbine (including maintaining the bus voltage within a preset range and meeting the power requirements of the external power grid), the method of this embodiment significantly reduces the operating temperature of key components of the wind turbine, thereby effectively improving the health performance and service life of the unit, and improving the operational reliability, economy and equipment life of the wind farm. It is particularly suitable for high-penetration wind power grid-connected scenarios.

[0210] In addition, this embodiment also provides a wind turbine group service quality control system for high-quality power generation, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the wind turbine group service quality control method for high-quality power generation.

[0211] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction. The computer program or instruction is programmed or configured to execute the wind turbine group service quality control method for high-quality power generation through a processor.

[0212] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the wind turbine group service quality control method for high-quality power generation through a processor.

[0213] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling the service quality of a wind turbine group for high-quality power generation, characterized in that: The steps include: S1, respectively establishing temperature models of the generator and converter of the wind turbine generator set by combining thermodynamic mechanism and energy loss, wherein the temperature models of the generator and converter are function models of active power and reactive power of the wind turbine generator set; S2, combined with the temperature models of the generator and converter, solves the multi-objective optimization problem shown in the following equation to obtain the optimal power generation strategy of the wind turbine: ， ， ， ， in, is the objective function of the multi-objective optimization problem, is the objective function of wind turbine temperature rise, is the objective function of converter temperature rise, is the objective function of voltage increment, is the prediction step length, is the number of wind turbines, is the weight coefficient of wind turbine temperature rise, For the The temperature of the generator in each wind turbine, is the ambient temperature, is the weight coefficient of the converter temperature rise, For the The temperature of the converter in each wind turbine, is the weight coefficient of voltage increment, and Respectively Wind turbines at the current time The terminal voltage and reference voltage; S3, sending the optimal power generation strategy of the wind turbine group to the wind turbine group for execution, calculating the service quality control index of the wind turbine group based on the wind farm status information after executing the optimal power generation strategy, and executing the wind turbine protection mechanism if the service quality control index of the wind turbine group exceeds the preset threshold, which refers to executing the load reduction or emergency shutdown of the wind turbine.

2. The method for controlling the service quality of a wind turbine group for high-quality power generation according to claim 1, characterized in that: The generator of the wind turbine is a doubly-fed induction generator. The temperature model of the generator uses the stator winding temperature of the generator to represent the temperature of the generator. The function expression of the temperature model of the generator is: ， in, is the stator winding temperature of the wind turbine, is the initial temperature, Output active power to the generator The increment of is the stator reactive power The increment of is the stator winding temperature About the generator output active power Sensitivity, is the stator winding temperature About stator reactive power sensitivity, and: ， ， ， ， ， ， ， ， ， ， in, is the stator winding temperature About stator copper loss Sensitivity, Stator copper loss About the generator output active power Sensitivity, is the stator winding temperature About rotor copper loss Sensitivity, is the rotor copper loss About the generator output active power Sensitivity, Stator copper loss About stator reactive power Sensitivity, is the rotor copper loss About stator reactive power Sensitivity, for The 10th row and 10th column of the inverse matrix of for The 10th row and 4th column of the inverse matrix of is a matrix composed of the inverse of the thermal resistance between the nodes of the generator, , is the node temperature matrix of the generator, is a nodal heat source matrix of the generator, where nodes in the nodal heat source matrix include a shaft, a stator yoke, stator teeth, a stator winding, a stator winding end, an end space, a rotor yoke, a rotor teeth, a rotor winding, an air gap, and a housing of the motor; Stator copper loss About q-axis current Sensitivity, is the q-axis current About the generator output active power Sensitivity, Stator copper loss About d-axis current Sensitivity, is the rotor d-axis current About stator reactive power Sensitivity, is the rotor resistance, the stator copper loss The calculation function expression is: ， in, is the stator resistance, is the stator inductance, For mutual induction, is the d-axis current, is the q-axis current, is the stator flux; is the rotor copper loss About q-axis current Sensitivity, is the rotor copper loss About d-axis current The sensitivity of the rotor copper loss The calculation function expression is: ， in, is the rotor resistance.

3. The method for controlling the service quality of a wind turbine group for high-quality power generation according to claim 1, characterized in that: The function expression of the temperature model of the converter is: ， in, is the temperature of the converter, Output reactive power to the converter the increment; is the temperature of the converter About output active power Sensitivity, is the temperature of the converter About the reactive power output of the converter sensitivity; is the temperature of the converter About stator reactive power sensitivity; ， ， ， ， in, is the temperature of the converter About converter losses Sensitivity, is the converter loss About output active power Sensitivity, is the converter loss About the reactive power output of the converter Sensitivity, is the converter loss About 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: ， in, For the Thermal resistance from layer to case, is the thermal resistance from case to heat sink, is the thermal resistance from the heat sink to the external environment, It is the number of physical structure layers between the chip device junction and the casing of the converter.

4. The method for controlling the service quality of a wind turbine group for high-quality power generation according to claim 3, characterized in that: The converter loss The calculation function expression is: ， ， ， in, and is an intermediate variable, is the RMS current of the grid-side converter, is the RMS 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, is the switching frequency, is the rated collector current of the IGBT, is the turn-off loss of the IGBT's reverse diode, is the lead resistance of the IGBT.

5. The method for controlling 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 also includes the following constraints: ， in, For the The active power of a wind turbine, is the active power dispatched, For the The reference active power of each wind turbine is For the The maximum active power of a wind turbine, For the The reference reactive power of each wind turbine is and Respectively The minimum reactive power and maximum reactive power of each wind turbine.

6. The method for controlling the service quality of a wind turbine group for high-quality power generation according to claim 1, characterized in that: The calculation function expression of the wind turbine group service quality control index in step S3 is: ， ， ， in, It is the service quality control index of wind turbine group. 、 、 、 、 and is the weight coefficient, for The normalized result of for The normalized result of is the health indicator at the wind turbine level, is the system-level 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 increment.

7. The method for controlling the service quality of a wind turbine group for high-quality power generation according to claim 1, characterized in that: Step S3 also 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 will be increased to strengthen the monitoring of the temperature rise or increment.

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

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute, through a processor, the method for controlling the service quality of a wind turbine group for high-quality power generation as recited in any one of claims 1 to 7.

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, through a processor, the method for controlling the service quality of a wind turbine group for high-quality power generation as recited in any one of claims 1 to 7.

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