Flexible network construction heat management method for IGBT of AC-DC mixed flow system

Abstract

The invention relates to a flexible network construction thermal management method for an IGBT of an AC-DC mixed flow system, and the method comprises the steps: building a hybrid energy storage model which comprises the storage battery and super capacitor capacity, and smoothing the output power of a fan; a life prediction model is established, the junction temperature is determined based on the switching and conduction loss of the IGBT module, and the life of the IGBT module is predicted based on the junction temperature. Through an IGBT potential income model based on control output power and service life, potential income is estimated, and optimal parameters of the model are determined by adopting an improved genetic algorithm. And determining the optimal control output power based on the optimal model and the real-time wind speed. And outputting the optimal control output power to a doubly-fed fan IGBT module to realize thermal management control. Compared with the prior art, the method has the advantages that the service life is prolonged, the reliability is high, and earnings are increased.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of IGBT chips and modules, and in particular to a flexible networking thermal management method for IGBTs in an AC / DC mixed current system. Background Art

[0002] The wind power converter system is the hub and key to wind power grid connection. Insulated Gate Bipolar Transistor (IGBT) chips and modules are important components in the converter, and their reliability can directly affect the performance of the entire converter system. IGBT conversion will produce large heat losses. For example, in deep sea wind power generation missions, there are tens of megawatts of power conversion heat losses. Scientific and reasonable thermal management methods are the core technology to ensure the safe and stable operation of converter systems and even large wind turbine systems.

[0003] In the wind power converter system, the IGBT module is a key power conversion device responsible for realizing the mutual conversion between AC power and DC power in the wind power system. In the AC-DC mixed flow system, the IGBT module processes the AC power from the wind turbine and converts it into DC power for subsequent power transmission and storage. In addition, the system also processes DC power from energy storage devices such as batteries and supercapacitors, and can convert it back to AC power to support the power grid or meet load requirements. This AC-DC mixed flow characteristic requires that the IGBT module and its thermal management strategy must have a high degree of flexibility and adaptability. IGBT thermal management technology belongs to complex giant system control, which comprehensively considers external thermal management means such as fans, heat sinks and liquid cooling systems, as well as the balance and coordination of internal thermal management means such as IGBT chips, module switches, frequency and power. At the same time, IGBT thermal management technology must ensure both reliability and potential benefits of the wind power system, and belongs to engineering game-type system control. From the development trend, in the layout of relevant advanced technologies for deep-sea wind power generation, IGBT thermal management technology is a control technology that meets the requirements of high-quality development such as cost reduction and efficiency improvement of operation and maintenance, and network support. Not only that, IGBT thermal management technology is undergoing a comprehensive transformation towards intelligent development, and is becoming a specific implementation object that is observable, measurable, and controllable in the development strategy requirements of new energy systems such as wind power and photovoltaics. The flexible network thermal management method for IGBT chips and modules takes into account the temperature control of the IGBT module and the economic performance of the entire wind power system, and maximizes the energy conversion efficiency and minimizes the operation and maintenance cost on the basis of ensuring the safety and reliability of the equipment.

[0004] Existing power control technologies generally use the maximum power point tracking method MPPT, which adjusts the generator speed to maximize energy conversion efficiency. However, when the wind speed fluctuates greatly, the output power of large wind turbines will produce large oscillations, causing serious thermal fatigue damage and evolution failure of IGBT chips and modules. Therefore, how to achieve the extension of the flexible network life cycle of IGBT chips and modules in MPPT and ensure high energy conversion efficiency and stability in the process of exploring the potential benefits of power generation is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a flexible networking thermal management method for IGBT in an AC / DC mixed current system. The wind power is smoothed by a hybrid energy storage model, wind power fluctuations are effectively suppressed, the service life and potential benefits of the IGBT module are predicted, the service life is extended and the potential benefits are maximized, and the stability of the system is improved by using a thermal management strategy.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to one aspect of the present invention, a flexible networking thermal management method for IGBT of an AC / DC mixed current system is provided, comprising the following steps:

[0008] S1. Input the real-time wind speed of the wind turbine to be tested, and obtain the output power of the wind turbine to be tested through the MPPT power control model;

[0009] S2. Smoothing the output power through a hybrid energy storage model to obtain a controlled output power;

[0010] S3, input fan modulation index m, DC side voltage UDC and switching frequency fsw, and predict the junction temperature of the IGBT module under different working conditions through iteration of power loss model, rain flow counting model and thermal network model, wherein the power loss includes switching loss and conduction loss;

[0011] S4. Predicting the life of the IGBT module using a Bayerer model based on the junction temperature data;

[0012] S5, inputting the controlled output power and predicted life into an IGBT potential benefit model, and using an improved genetic algorithm to determine optimal parameters of an MPPT power control model based on the potential benefit;

[0013] S6, output optimal control output power to the doubly-fed wind turbine IGBT module to achieve thermal management control.

[0014] Furthermore, the S1 obtains the rotation speed and wind speed of the fan in real time, calculates the AC / DC conversion efficiency, and monitors the operating status of the system under AC / DC mixed flow conditions. The MPPT power control model processes the wind speed into output power and outputs it to the hybrid energy storage model and the power loss model, and its expression is:

[0015] P = k opt w r 3 ,

[0016]

[0017] Where P is the controlled output power, k opt is a constant coefficient, w r is the speed of the wind turbine, λ is the tip speed ratio, R is the radius of the wind turbine, and v is the wind speed.

[0018] Furthermore, the hybrid energy storage model of S2 uses a first-order low-pass filter to decompose the wind power deviation into low-frequency and high-frequency components, which is used to integrate capacity-type and power-type energy storage, construct an energy buffering mechanism for AC / DC mixed flow systems, and smooth out power fluctuations caused by wind speed fluctuations, wherein the low-frequency component is compensated by battery energy storage, and the high-frequency component is compensated by supercapacitors.

[0019] The expression of battery energy storage compensation is:

[0020]

[0021]

[0022] P b =N b C b U b / 10 4 ,

[0023] Among them, E b is the rated capacity of the battery pack, E bmin is the minimum capacity, P b is the rated value of the battery output power, the battery is discharged at a rate of 10C, N b is the number of batteries, C b For each battery’s rated capacity, U b For each battery's rated voltage, D depth is the maximum discharge depth.

[0024] The expression of high frequency component supercapacitor compensation is:

[0025]

[0026]

[0027] P cmax =N c U max I cmax / 10 3 ,

[0028] Among them, E cmax is the maximum capacity of the supercapacitor, E cmin is the minimum capacity of the supercapacitor, I cmax is the maximum operating current of a single supercapacitor, P cmax is the maximum output power of the supercapacitor, Nc is the number of supercapacitors, C c is the rated capacity of the supercapacitor, U cmax is the maximum operating voltage, U cmin It is the minimum operating voltage, and the value of voltage Uc is between Ucmax and Ucmin.

[0029] Furthermore, the specific steps of predicting the junction temperature in S3 include:

[0030] S301, determining the switching and conduction losses of the IGBT module based on the modulation index m, the DC side voltage UDC and the switching frequency fsw data stream of the wind turbine;

[0031] S302, calculating the IGBT junction temperature TTj and the diode junction temperature TDj based on the Foster thermal network model and the switching and conduction loss data of the IGBT module, inputting the IGBT junction temperature TTj into the rain flow counting module, and obtaining the number of complete iterations identified in the given temperature change data;

[0032] S303, iterative processing to obtain junction temperature data.

[0033] The factors affecting switching and conduction losses include battery capacity and supercapacitor capacity, wherein the battery capacity affects the DC side voltage UDC, and the supercapacitor capacity affects the switching frequency fsw.

[0034] Furthermore, in S5, the potential economic benefits of the AC / DC mixed flow system under different power output and life conditions are evaluated, and the expression of the model is:

[0035] C=P opt *L*E,

[0036] Among them, C is the potential profit, P opt To control the output power, L is the life of the IGBT module; E is the electricity price.

[0037] When the improved genetic algorithm in S5 determines that the population has a tendency to mature prematurely, it performs a mutation operation on all individuals with a probability far greater than the usual mutation probability to jump out of the local optimum and determine the optimal parameters of the MPPT power control model based on potential benefits, including battery capacity and supercapacitor capacity, to achieve a balance between power output and life. The specific steps include:

[0038] S501, based on the cut-in and cut-out wind speeds of the unit, obtain the control output power that meets the stable operation conditions of the wind turbine, thereby determining the upper and lower limits of the battery capacity and the supercapacitor capacity;

[0039] S502, generating an initial population P0, and sorting them according to individual fitness, wherein the individuals include a set of battery capacity and supercapacitor capacity parameters, using a tournament method to select elite individuals as a parent set, and breeding to obtain a child set Qt, the breeding process includes crossover and mutation, the crossover operation replaces and reorganizes part of the structure of two parent individuals to generate new individuals, and the fitness of the elite individuals is based on their ability to maximize potential benefits while meeting the stable operation conditions of the wind turbine;

[0040] S503, using consistent crossover and simulated binary crossover algorithms randomly as crossover operators to perform mutation operations;

[0041] S504, retain the excellent genes in the previous generation, form a temporary set together with the offspring, select the next generation population according to the individual fitness function, and judge whether the termination condition is met. If it is met, end the iteration and obtain the optimal individual, otherwise enter the next round of iterative optimization.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] (1) Extending service life: The hybrid energy storage model is used to smooth the output power of the wind turbine and reduce the low-frequency junction temperature fluctuation of the IGBT module. Under the conditions of meeting the wind speed and turbine speed, the output power of the wind turbine generator set is smoothed, the switching loss is reduced, and the service life of the flexible network of IGBT chips and modules is extended.

[0044] (2) Increased benefits: Through the potential benefit model and improved genetic algorithm, the improved thermal management strategy is superior to the MPPT strategy, maximizing the potential economic benefits of the AC / DC mixed flow system under different operating conditions.

[0045] (3) Improve the reliability of wind power systems: Optimized power control and energy storage strategies enhance the adaptability and support capabilities of the AC / DC mixed current system to the power grid, and enhance the reliability of the system under complex time-varying conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1It is a flow chart of a flexible networking thermal management method for IGBT in an AC / DC mixed current system;

[0047] Figure 2 Schematic diagram of the relationship between battery capacity, supercapacitor capacity and potential revenue. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] like Figure 1 As shown, a flexible networking thermal management method for IGBT of an AC / DC mixed current system is provided, comprising the following steps:

[0050] S1. Input the real-time wind speed of the wind turbine to be tested, and obtain the output power of the wind turbine to be tested through the MPPT power control model;

[0051] S2. Smoothing the output power through a hybrid energy storage model to obtain a controlled output power;

[0052] S3, input fan modulation index m, DC side voltage UDC and switching frequency fsw, and predict the junction temperature of the IGBT module under different working conditions through iteration of power loss model, rain flow counting model and thermal network model, wherein the power loss includes switching loss and conduction loss;

[0053] S4. Predicting the life of the IGBT module using a Bayerer model based on the junction temperature data;

[0054] S5, inputting the controlled output power and predicted life into an IGBT potential benefit model, and using an improved genetic algorithm to determine optimal parameters of an MPPT power control model based on the potential benefit;

[0055] S6, output optimal control output power to the doubly-fed wind turbine IGBT module to achieve thermal management control.

[0056] S1 obtains the speed and wind speed of the fan in real time, calculates the AC / DC conversion efficiency, and monitors the operating status of the system under AC / DC mixed flow conditions. The MPPT power control model processes the wind speed into output power and outputs it to the hybrid energy storage model and power loss model. Its expression is:

[0057] P = k ppt w r 3 ,

[0058]

[0059] Where P is the controlled output power, k opt is a constant coefficient, w r is the speed of the wind turbine, λ is the tip speed ratio, R is the radius of the wind turbine, and v is the wind speed.

[0060] The hybrid energy storage model of S2 uses a first-order low-pass filter to decompose the wind power deviation into low-frequency and high-frequency components, which is used to integrate capacity-type and power-type energy storage, construct an energy buffering mechanism for AC / DC mixed flow systems, and smooth out power fluctuations caused by wind speed fluctuations. The low-frequency component is compensated by battery energy storage, and the high-frequency component is compensated by supercapacitors.

[0061] The expression of battery energy storage compensation is:

[0062]

[0063]

[0064] P b =N b C b U b / 10 4 ,

[0065] Among them, E b is the rated capacity of the battery pack, E bmin is the minimum capacity, P b is the rated value of the battery output power, the battery is discharged at a rate of 10C, N b is the number of batteries, C b For each battery’s rated capacity, U b For each battery's rated voltage, D depth is the maximum discharge depth.

[0066] The expression of high frequency component supercapacitor compensation is:

[0067]

[0068]

[0069] P cmax =N c U max I cmax / 10 3 ,

[0070] Among them, E cmax is the maximum capacity of the supercapacitor, E cmin is the minimum capacity of the supercapacitor, I cmaxis the maximum operating current of a single supercapacitor, P cmax is the maximum output power of the supercapacitor, Nc is the number of supercapacitors, C c is the rated capacity of the supercapacitor, U cmax is the maximum operating voltage, U cmin It is the minimum operating voltage, and the value of voltage Uc is between Ucmax and Ucmin.

[0071] The specific steps of predicting junction temperature in S3 include:

[0072] S301, determining the switching and conduction losses of the IGBT module based on the modulation index m, the DC side voltage UDC and the switching frequency fsw data stream of the wind turbine;

[0073] S302, calculating the IGBT junction temperature TTj and the diode junction temperature TDj based on the Foster thermal network model and the switching and conduction loss data of the IGBT module, inputting the IGBT junction temperature TTj into the rain flow counting module, and obtaining the number of complete iterations identified in the given temperature change data;

[0074] S303, iterative processing to obtain junction temperature data.

[0075] The factors affecting switching and conduction losses include battery capacity and supercapacitor capacity, wherein the battery capacity affects the DC side voltage UDC, and the supercapacitor capacity affects the switching frequency fsw.

[0076] In S5, the potential economic benefits of the AC / DC mixed system under different power output and life conditions are evaluated. The expression of the model is:

[0077] C=P opt *L*E,

[0078] Among them, C is the potential profit, P opt To control the output power, L is the life of the IGBT module; E is the electricity price. Figure 2 The relationship between battery capacity, supercapacitor capacity and potential revenue.

[0079] When the improved genetic algorithm in S5 determines that the population has a tendency to mature prematurely, it performs a mutation operation on all individuals with a probability far greater than the usual mutation probability to jump out of the local optimum and determine the optimal parameters of the MPPT power control model based on potential benefits, including battery capacity and supercapacitor capacity, to achieve a balance between power output and life. The specific steps include:

[0080] S501, based on the cut-in and cut-out wind speeds of the unit, obtain the control output power that meets the stable operation conditions of the wind turbine, thereby determining the upper and lower limits of the battery capacity and the supercapacitor capacity;

[0081] S502, generating an initial population P0, and sorting them according to individual fitness, wherein the individuals include a set of battery capacity and supercapacitor capacity parameters, using a tournament method to select elite individuals as a parent set, and breeding to obtain a child set Qt, the breeding process includes crossover and mutation, the crossover operation replaces and reorganizes part of the structure of two parent individuals to generate new individuals, and the fitness of the elite individuals is based on their ability to maximize potential benefits while meeting the stable operation conditions of the wind turbine;

[0082] S503, using consistent crossover and simulated binary crossover algorithms randomly as crossover operators to perform mutation operations;

[0083] S504, retain the excellent genes in the previous generation, form a temporary set together with the offspring, select the next generation population according to the individual fitness function, and judge whether the termination condition is met. If it is met, end the iteration and obtain the optimal individual, otherwise enter the next round of iterative optimization.

[0084] The results of the thermal management method of this embodiment are compared with the existing methods, as shown in Table 1. Among them, MPPT-ES is a method for optimizing MPPT using hybrid energy storage, and MPPT-GA is a method for optimizing hybrid energy storage using an improved genetic algorithm. The IGBT module life of MPPT-ES is 7.1% longer than that of MPPT, and the potential benefit of MPPT-GA is 15.8% higher than that of MPPT. The above numerical results verify the effectiveness of the MPPT-ES method in extending the life of IGBTs, and the effectiveness of MPPT-GA in optimizing potential benefits and automatically balancing the P and L of IGBT modules in doubly fed wind turbines.

[0085] Table 1: Performance comparison of MPPT, MPPT-ES and MPPT-GA at v = 8m / s, D = 20%

[0086]

[0087]

[0088] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A flexible networking thermal management method for IGBT in an AC / DC mixed current system, characterized in that: The following steps are involved: S1. Input the real-time wind speed of the wind turbine to be tested, and obtain the output power of the wind turbine to be tested through the MPPT power control model; S2. Smoothing the output power through a hybrid energy storage model to obtain a controlled output power; S3, input fan modulation index m, DC side voltage UDC and switching frequency fsw, and predict the junction temperature of the IGBT module under different working conditions through iteration of power loss model, rain flow counting model and thermal network model, wherein the power loss includes switching loss and conduction loss; S4. Predicting the life of the IGBT module using a Bayerer model based on the junction temperature data; S5, inputting the controlled output power and predicted life into an IGBT potential benefit model, and using an improved genetic algorithm to determine optimal parameters of an MPPT power control model based on the potential benefit; S6, output optimal control output power to the doubly-fed wind turbine IGBT module to achieve thermal management control.

2. The flexible networking thermal management method for IGBT of an AC / DC mixed current system according to claim 1, characterized in that: The S1 obtains the rotation speed and wind speed of the fan in real time, calculates the AC / DC conversion efficiency, and monitors the operating status of the system under AC / DC mixed flow conditions.

3. The flexible networking thermal management method for IGBT of an AC / DC mixed current system according to claim 1, characterized in that: The MPPT power control model of S1 processes the wind speed into output power and outputs it to the hybrid energy storage model and the power loss model. Its expression is: P=k opt w r 3 , Where P is the controlled output power, k opt is a constant coefficient, w r is the speed of the wind turbine, λ is the tip speed ratio, R is the radius of the wind turbine, and v is the wind speed.

4. The flexible networking thermal management method for IGBT of an AC / DC mixed current system according to claim 1, characterized in that: The hybrid energy storage model of S2 uses a first-order low-pass filter to decompose the wind power deviation into low-frequency and high-frequency components, integrates capacity-type and power-type energy storage, constructs an energy buffering mechanism for the AC / DC mixed flow system, and smoothes power fluctuations caused by wind speed fluctuations, wherein the low-frequency component is compensated by battery energy storage, and the high-frequency component is compensated by supercapacitor.

5. The flexible networking thermal management method for IGBT of AC / DC mixed current system according to claim 4, characterized in that: The expression of battery energy storage compensation is: P b =N b C b U b / 10 4 , Among them, E b is the rated capacity of the battery pack, E bmin is the minimum capacity, P b is the rated value of the battery output power, the battery is discharged at a rate of 10C, N b is the number of batteries, C b For each battery’s rated capacity, U b For each battery's rated voltage, D depth is the maximum depth of discharge.

6. The flexible networking thermal management method for IGBT of AC / DC mixed current system according to claim 4, characterized in that: The expression of the high frequency component supercapacitor compensation is: P cmax =N c U max I cmax / 10 3 , Among them, E cmax is the maximum capacity of the supercapacitor, E cmin is the minimum capacity of the supercapacitor, I cmax is the maximum operating current of a single supercapacitor, P cmax is the maximum output power of the supercapacitor, Nc is the number of supercapacitors, C c is the rated capacity of the supercapacitor, U cmax is the maximum operating voltage, U cmin It is the minimum operating voltage, and the value of voltage Uc is between Ucmax and Ucmin.

7. The flexible networking thermal management method for IGBT in an AC / DC mixed current system according to claim 1, characterized in that: The specific steps of predicting the junction temperature in S3 include: S301, determining the switching and conduction losses of the IGBT module based on the modulation index m, the DC side voltage UDC and the switching frequency fsw data stream of the wind turbine; S302, calculating the IGBT junction temperature TTj and the diode junction temperature TDj based on the Foster thermal network model and the switching and conduction loss data of the IGBT module, inputting the IGBT junction temperature TTj into the rain flow counting module, and obtaining the number of complete iterations identified in the given temperature change data; S303, iterative processing to obtain junction temperature data.

8. The flexible networking thermal management method for IGBT of AC / DC mixed current system according to claim 7, characterized in that: The factors affecting the switching and conduction losses include battery capacity and supercapacitor capacity, wherein the battery capacity affects the DC side voltage UDC, and the supercapacitor capacity affects the switching frequency fsw.

9. The flexible networking thermal management method for IGBT of AC / DC mixed current system according to claim 1, characterized in that: In S5, the potential economic benefits of the AC / DC mixed flow system under different power output and life conditions are evaluated, and the expression of the model is: C=P opt *L*E, Among them, C is the potential profit, P is opt To control the output power, L is the life of the IGBT module; E is the electricity price.

10. The flexible networking thermal management method for IGBT in an AC / DC mixed current system according to claim 1, characterized in that: When the improved genetic algorithm in S5 determines that the population has a tendency to mature prematurely, it performs a mutation operation on all individuals with a probability far greater than the normal mutation probability to jump out of the local optimum, and determines the optimal parameters of the MPPT power control model based on potential benefits, including the battery capacity and the supercapacitor capacity, to achieve a balance between power output and life. The specific steps include: S501, based on the cut-in and cut-out wind speeds of the unit, obtain the control output power that meets the stable operation conditions of the wind turbine, thereby determining the upper and lower limits of the battery capacity and the supercapacitor capacity; S502, generating an initial population P0, and sorting them according to individual fitness, wherein the individuals include a set of battery capacity and supercapacitor capacity parameters, using a tournament method to select elite individuals as a parent set, and breeding to obtain a child set Qt, the breeding process includes crossover and mutation, the crossover operation replaces and reorganizes part of the structure of two parent individuals to generate new individuals, and the fitness of the elite individuals is based on their ability to maximize potential benefits while meeting the stable operation conditions of the wind turbine; S503, using consistent crossover and simulated binary crossover algorithms randomly as crossover operators to perform mutation operations; S504, retain the excellent genes in the previous generation, form a temporary set together with the offspring, select the next generation population according to the individual fitness function, and judge whether the termination condition is met. If it is met, end the iteration and obtain the optimal individual, otherwise enter the next round of iterative optimization.

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