Cascaded level inverter control method for smart power grid

Abstract

The invention relates to the technical field of cascaded level inverter control, in particular to a cascaded level inverter control method for an intelligent power grid. The method comprises the following steps: acquiring direct-current side input voltage and current of each module of the cascaded level inverter, inverter bridge arm output voltage and current, inverter temperature and heat dissipation power of a heat dissipation system, and then acquiring a power supply unreliability index of each module; respectively acquiring weighted input power and weighted output power based on the input power and the output power at each moment in the preset time period, and further obtaining a power supply bearing coefficient; and obtaining a future contribution coefficient according to the power supply bearing coefficient, the inverter temperature and the heat dissipation power of the heat dissipation system, further obtaining the future output power of each module in combination with the total output power value of the modules required by future power supply of the power grid, and controlling the cascaded level inverter. According to the invention, each module for controlling the cascaded level inverter can be accurately controlled.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cascaded level inverter control, and particularly relates to a cascaded level inverter control method for a smart grid. BACKGROUND

[0002] With the development of smart grids, cascaded level inverters (CHB) have been widely used in photovoltaic power generation, wind power generation and distributed energy storage systems and other new energy access scenarios due to their modular structure, good output power quality and easy expansion. This type of inverter is connected by multiple power units in series, each unit is connected to an independent DC power supply, can realize multi-level output, improve system efficiency and reduce output harmonics, and is an important part of power conversion and control in smart grids. In order to adapt to the trend of energy diversification and load dynamicization in smart grids, the control method of cascaded inverters is developing towards high precision, high reliability and intelligence.

[0003] In actual operation, cascaded level inverters often face the problem of uneven power distribution between modules. Due to the output capacity difference of the DC power supply connected to each module (for example, affected by factors such as light intensity, temperature change, battery aging, etc.), some modules are in an overload or underload state for a long time, causing heat stress concentration, voltage waveform distortion, increased power loss and reduced system life. Traditional control methods mostly use fixed reference values or centralized regulation, which is difficult to balance module independence and system global balance, and lacks dynamic adaptive control ability. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide a cascaded level inverter control method for a smart grid, and the technical solution adopted is as follows:

[0005] One embodiment of the present application provides a cascaded level inverter control method for a smart grid, which comprises:

[0006] Obtaining the DC side input voltage, current, inverter bridge arm output voltage, current, and inverter temperature, heat dissipation power of the heat dissipation system of each module of the cascaded level inverter;

[0007] According to the DC side input voltage and current of a module in a preset period, voltage unreliability index and current unreliability index are obtained respectively; the sum of the voltage unreliability index and the current unreliability index is the power supply unreliability index;

[0008] Obtaining input power and output power of each time in a preset period of a module; obtaining weighted input power and weighted output power based on input power and output power of each time in the preset period; obtaining power supply carrying coefficient based on power supply unreliability index, weighted input power and weighted output power;

[0009] Obtaining future contribution coefficient of the module based on power supply carrying coefficient of the module, inverter temperature and heat dissipation power of the heat dissipation system; obtaining future output power of each module based on future contribution coefficient of each module and total output power value of the module required by future power supply of the power grid; controlling the cascaded level inverter according to future output power of each module.

[0010] Preferably, obtaining voltage unreliability index and current unreliability index based on DC side input voltage and current of the module in the preset period, comprising:

[0011] Decomposing DC side input voltage and current of the module in the preset period using STL algorithm to obtain trend item and residual item corresponding to DC side input voltage and trend item and residual item corresponding to DC side input current; obtaining voltage unreliability index and current unreliability index based on trend item and residual item corresponding to DC side input voltage and trend item and residual item corresponding to DC side input current.

[0012] Preferably, obtaining voltage unreliability index and current unreliability index based on trend item and residual item corresponding to DC side input voltage and trend item and residual item corresponding to DC side input current, comprising:

[0013] Adding sampling interval distance between each data point in residual item corresponding to DC side input voltage and data point of current time to first preset value and taking inverse to obtain weight of each data point; wherein sampling interval distance is interval number between two data points; obtaining weighted average value of absolute value of amplitude of each data point in residual item by using weight of each data point in residual item and normalizing to obtain residual feature item; obtaining slope between each two adjacent data points in trend item corresponding to DC side input voltage and constructing slope sequence according to time sequence, wherein slope between two adjacent data points is slope corresponding to latter data point in two adjacent data points; similarly, obtaining weight corresponding to each slope in slope sequence based on sampling interval distance between each slope in slope sequence and slope of current time; obtaining unreliability factor corresponding to one slope by using exponential function with natural constant as base to negatively correlate the slope; obtaining weighted average value of unreliability factor corresponding to each slope by using weight corresponding to each slope and normalizing to obtain trend feature item; obtaining voltage unreliability index by weighted sum of residual feature item and trend feature item; similarly, obtaining current unreliability index based on trend item and residual item corresponding to DC side input current.

[0014] Preferably, the input power and the output power of each time point in a preset period of one module are obtained, comprising:

[0015] The input power of each time point of one module is obtained according to the DC side input voltage and current of each time point of the module collected in the preset period, and the output power of each time point of the module is obtained according to the inverter bridge arm output voltage and current of each time point.

[0016] Preferably, the weighted input power and the weighted output power are respectively obtained based on the input power and the output power of each time point in the preset period, comprising:

[0017] The weight corresponding to each time point is obtained based on the sampling interval distance between each time point and the current time point in the preset period; the weighted average value of the input power of each time point is calculated using the weight corresponding to each time point to obtain the weighted input power; the weighted average value of the output power of each time point is calculated using the weight corresponding to each time point to obtain the weighted output power.

[0018] Preferably, the power supply carrying coefficient is obtained based on the power supply unreliability index, the weighted input power and the weighted output power, comprising:

[0019] The power supply carrying coefficient of one module is obtained by comparing the weighted output power of the module with the weighted input power and multiplying the power supply unreliability index of the module.

[0020] Preferably, the future contribution coefficient of one module is obtained according to the power supply carrying coefficient of the module, the inverter temperature and the heat dissipation power of the heat dissipation system, comprising:

[0021] The inverse of the power supply carrying coefficient of one module is normalized to obtain a carrying pressure feature term; the inverter temperature of the module at the current time point is added to a hyperparameter to obtain a temperature factor term; the heat dissipation power of the heat dissipation system of the module at the current time point is added to a hyperparameter to obtain a heat dissipation power factor term; the inverse of the product of the temperature factor term and the heat dissipation power factor term is calculated to obtain a heat dissipation performance feature term; the future contribution coefficient of the module is obtained by weighted sum of the carrying pressure feature term and the heat dissipation performance feature term.

[0022] Preferably, the future output power of each module is obtained based on the future contribution coefficient of each module and the total output power value of the modules required by the power grid for future power supply, comprising:

[0023] The future contribution coefficient of one module is compared with the sum of the future contribution coefficients of all modules, and multiplied by the total output power of the modules required by the power grid for future power supply to obtain the future output power of the module.

[0024] The application has at least the following beneficial effects: the application obtains the DC side input voltage, current, inverter bridge arm output voltage, current and inverter temperature, heat dissipation power of the heat dissipation system of each module of the cascade type level inverter, then analyzes the DC side input voltage and current of a module in a preset period to obtain the voltage unreliability index and the current unreliability index, and then obtains the power supply unreliability index of the module; then the input power and output power of each time in a preset period of a module are obtained, the weighted input power and weighted output power are obtained, the power supply unreliability index of the module is combined to obtain the power supply bearing coefficient of the module; further, the heat dissipation performance of the module is analyzed in combination with the inverter temperature of the module and the heat dissipation power of the heat dissipation system, various factors are comprehensively considered to obtain the future contribution coefficient of each module, and then the future output power of each module, that is, the required output power, is obtained in combination with the total output power value of the module required by the power grid in the future, so as to adjust the output power of each module through the control system according to the conditions and factors of the module, so as to realize the reasonable distribution of the load according to the conditions and factors of the module, realize load balancing, and improve the overall operation efficiency and stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings required in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0026] Figure 1 A method flow chart of a cascade type level inverter control method for a smart grid provided by the embodiments of the present application. DETAILED DESCRIPTION

[0027] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific embodiments, structure, features and effects of a cascade type level inverter control method for a smart grid according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0029] The application provides a cascaded level inverter control method for a smart grid.

[0030] Embodiments

[0031] The main application scenario of the application is adjusting and controlling each module of the cascaded level inverter.

[0032] Please refer to Figure 1 which shows a method flowchart of the cascaded level inverter control method for the smart grid, and the method comprises the following steps:

[0033] In step S1, the DC side input voltage, current, inverter bridge arm output voltage, current, inverter temperature and heat dissipation power of the heat dissipation system of each module of the cascaded level inverter are acquired.

[0034] In order to realize dynamic perception and optimal control of the running state of each power module of the cascaded level inverter, the system needs to collect and analyze the key running parameters of each module in real time.

[0035] Specifically, the DC side input voltage, current (for evaluating the power supply capacity of the DC source), inverter bridge arm output voltage, current (for judging the output power output state) and heat dissipation power of the heat dissipation system of each module of the cascaded level inverter are collected.

[0036] The data collection process relies on an embedded sensing and communication module, sampling signals are collected through a high-speed ADC, then preprocessed and filtered by a local controller (such as a DSP, FPGA or ARM chip), and then uploaded to a main control unit or a distributed control network for state recognition and power balancing strategy calculation. Through the continuous collection and feedback of the above real-time data, accurate basis can be provided for the intelligent judgment of the subsequent control strategy, and accurate coordination and optimal scheduling of the power among the modules can be realized. The collection frequency is determined by the actual situation.

[0037] In step S2, the voltage unreliability index and the current unreliability index are respectively acquired according to the DC side input voltage and current of one module in a preset time period; and the sum of the voltage unreliability index and the current unreliability index is the power supply unreliability index.

[0038] In the control process of cascaded H-bridge (CHB) inverter, the system is usually composed of multiple series-connected power units (also known as H-bridge modules), each module is connected to a group of independent DC power sources such as photovoltaic components, energy storage batteries or super capacitors, etc. These modules work together to convert multiple independent DC power into multi-level AC power for the grid or load. In the cascaded H-bridge inverter system, in order to achieve power distribution balance among modules, each module should ideally bear equal output power.

[0039] However, in actual application, due to the differences in the state of DC power sources connected to each module, such as light intensity fluctuation, battery aging or environmental temperature change, etc., the power supply capacity of each module is inconsistent. If the system still uses a unified power reference value, it is easy to cause some modules to overload, temperature rise, etc. due to the inability to follow, thereby causing system power fluctuation and power quality decline. Therefore, in order to improve the overall efficiency and stability of the system, the power output proportion of each module should be dynamically adjusted according to the real-time power supply capacity of the DC side, so that the modules with strong power supply capacity and stable output can bear more output tasks, and the modules with weak or unstable power supply capacity can be appropriately reduced, thereby achieving more reasonable power distribution and system balance control.

[0040] In order to achieve the above purpose, it is necessary to analyze various data in a period of time before the current time, and then control and adjust the cascaded H-bridge inverter at the current time. In the embodiment of the present application, data in a preset period is analyzed, preferably, the preset period refers to data within 10 minutes before the current time, that is, data within 10 minutes from the current time.

[0041] Further, taking the data of one module as an example, the STL (Seasonal-Trend decomposition using Loess) algorithm is used to decompose the DC side input voltage and current of the module in the preset period respectively, to obtain the trend item and residual item corresponding to the DC side input voltage and the trend item and residual item corresponding to the DC side input current. This algorithm is prior art and will not be described here.

[0042] Compared to traditional direct statistical analysis methods, the STL algorithm decomposition can break down the DC-side supply voltage or current of an inverter module within a preset time period into three parts: a trend term and a residual term. This provides a more comprehensive picture of changes in power supply characteristics. The trend term identifies the overall evolution direction of power supply capacity. For example, if the supply voltage or current of a module shows a continuous downward trend, it indicates that its future power supply capacity may be insufficient. In this case, it may be necessary to appropriately reduce the output power of that module to avoid performance degradation. The residual term reflects unpredictable random fluctuations. If the residual fluctuation of the supply voltage or current of a module is significantly greater than that of other modules, it indicates a high degree of uncertainty in its power supply, which may bring potential instability risks. In this case, it may also be necessary to appropriately reduce its output power to reduce its output pressure. This can reduce the occurrence of potential failures due to pressure, and at the same time, it can balance the load output and improve the robustness of each module's operation based on its own operating conditions and capabilities during the power conversion process, thereby achieving more accurate and intelligent control objectives.

[0043] Next, voltage unreliability indices and current unreliability indices are obtained based on the trend terms and residual terms corresponding to the DC-side input voltage and the DC-side input current, respectively. Taking the DC-side input voltage as an example...

[0044] Specifically, the sampling interval distance between each data point in the residual term corresponding to the DC-side input voltage and the data point at the current time is added to a first preset value, and the weight of each data point is calculated by summing the two data points. The sampling interval distance is the number of intervals between two data points. The weight of each data point in the residual term is used to calculate the weighted average of the absolute values ​​of the amplitudes of each data point in the residual term, and then normalized to obtain the residual feature term. The slope between every two adjacent data points in the trend term corresponding to the DC-side input voltage is calculated, and a slope sequence is constructed according to the time sequence. The slope between two adjacent data points is the slope of the latter of the two adjacent data points. The slope corresponding to each data point; similarly, the weight corresponding to each slope in the slope sequence is obtained based on the sampling interval distance between each slope in the slope sequence and the slope at the current time; the unreliability factor corresponding to the slope is obtained by negatively mapping an exponential function with the natural constant as the base; the weighted average of the unreliability factor corresponding to each slope is calculated using the weight corresponding to each slope and normalized to obtain the trend feature term; the voltage unreliability index is obtained by weighted summation of the residual feature term and the trend feature term; similarly, the current unreliability index is obtained based on the trend term and residual term corresponding to the DC side input current.

[0045] The specific calculation model for the voltage unreliability index is as follows:

[0046] ,

[0047] in, represents the voltage unreliability index of the a-th module; β represents the weight adjustment factor, which is set to 0.4 here (in load balancing decisions, the trend of DC-side output capability is more critical because it reflects the long-term changes in the module's power supply capability and directly affects the subsequent continuous output capability trend; while the residual is just a random fluctuation, and its influence on the inverter power supply process is relatively weak compared to the power supply trend, so it is recommended that the weight value be less than or equal to 0.5, and can be adjusted according to the actual application scenario); norm represents the linear normalization function; This represents the magnitude of the i-th data point in the residual term after DC-side voltage decomposition during a preset time period of the a-th module; This represents the sum of the sampling interval distance between the i-th data point and the current data point in the residual term after DC-side voltage decomposition in the preset time period of the a-th module, and the first preset value. For example, if the sampling interval distance between the current data point and the current data point is 0, then... The value is 1, meaning the sampling interval between the previous data point and the current data point is 1. It is 2, and so on. The larger the value, the closer it is to the current moment, and the greater the reference weight of its value change and impact on the current and future control decision analysis. Therefore, its reciprocal is used as the weight corresponding to the data point, representing the influence weight of the i-th data point in the residual term after the DC-side voltage decomposition of the a-th module in the preset time period. m represents the number of data points, that is, the number of DC-side voltages in the preset time period. This is a residual characteristic term, representing the random disturbance of the residual term. The larger this value, the greater the random variation in the analyzed data, meaning the higher its unreliability. Indicates the request The weighted average;

[0048] Let represent the s-th slope in the slope sequence formed by the slopes of every two adjacent data points in the trend term of the DC-side voltage decomposition of the a-th module during a preset time period, where e represents the natural constant. This represents the unreliable factor obtained by negatively mapping the s-th slope using an exponential function with the natural constant as the base. The smaller the value, The larger the value, the greater the downward trend of the local slope, and therefore the higher its unreliability. This represents the weight corresponding to the s-th slope. This parameter represents the distance between the s-th slope in the slope sequence and the sampling interval at the current time, plus a first preset value. The method for obtaining this parameter is the same as described above. The method for obtaining them is the same; This represents a trend characteristic term, indicating the overall unreliability of that trend term. Similarly, the current unreliability index is obtained using the same method based on the trend term and residual term corresponding to the DC-side input current. From this, the voltage and current unreliability indices for each module can be obtained.

[0049] Finally, the voltage unreliability index and the current unreliability index corresponding to a module are fused to obtain the unreliability index corresponding to the module, which is used to represent the unreliability of the DC power supply side of the module. The sum of the voltage unreliability index and the current unreliability index corresponding to a module is the unreliability index corresponding to the module.

[0050] Step S3: Obtain the input power and output power of a module at each time within a preset time period; obtain the weighted input power and weighted output power based on the input power and output power at each time within the preset time period; obtain the power supply bearing capacity coefficient based on the power supply unreliability index, weighted input power, and weighted output power.

[0051] In a cascaded inverter system, each module should maintain a relative balance between input and output power during the conversion of DC input to AC output. If the input-output power ratio of a module is out of balance for a long period, especially if the output is significantly higher than its sustainable input capacity, the module will be under continuous high load, increasing thermal stress and operating pressure, thereby accelerating performance degradation and increasing the risk of failure.

[0052] Given that the collected data is time-series data, recent data is generally more valuable for evaluating the current input-output balance of the module. This is because the balance between the module's input and output becomes more significant and important closer to the current time, as it may predict similar output characteristics in the near future. Therefore, a time-weighted mechanism can be introduced to weight the module's historical DC input power to obtain a weighted input power estimate for the current moment; similarly, the inverter output power is weighted in the same way to obtain a current weighted output power estimate. For ease of description, the DC input power and inverter output power are collectively referred to as the module's input power and output power.

[0053] Therefore, it is necessary to obtain the input power and output power of a module at each moment within a preset time period. The input power of the module at each moment can be obtained based on the DC side input voltage and current of the module collected at each moment within the preset time period, and the output power of the module at each moment can be obtained based on the inverter bridge arm output voltage and current at each moment. This is a well-known technology and will not be described in detail here.

[0054] Furthermore, for a module, the weight corresponding to each moment is obtained based on the sampling interval distance between each moment within a preset time period and the current moment; the weighted average of the input power at each moment is calculated using the weight corresponding to each moment to obtain the weighted input power; the weighted average of the output power at each moment is calculated using the weight corresponding to each moment to obtain the weighted output power.

[0055] The weights at each time step are obtained in the manner described above. The methods are the same, and the values ​​are also the same; both have the same weight.

[0056] The specific calculation model for weighted input power is as follows:

[0057] ,

[0058] in, This represents the weighted input power on the DC side of the a-th module; m represents the number of moments within the preset time period, and also the amount of input power. This represents the weight corresponding to the i-th data point within the preset time period of the a-th module, and also represents the weight corresponding to the i-th time point. Same as the meaning above; This represents the input power at the i-th moment within the preset time period of the a-th module. The calculation method for its weighted output power is the same.

[0059] Furthermore, based on the power supply unreliability index, weighted input power, and weighted output power, the power supply carrying capacity coefficient is obtained. The weighted output power corresponding to a module is compared with the weighted input power, and then multiplied by the power supply unreliability index of the module to obtain the power supply carrying capacity coefficient of the module.

[0060] The calculation model for the power supply carrying capacity factor is as follows:

[0061] ,

[0062] in, This represents the power supply carrying capacity of the a-th module. This represents the power supply unreliability index of module a. The larger the value, the higher the unreliability of the DC input terminal of the module, and the greater the module's ability to withstand random changes. , These represent the weighted input power and weighted output power of the a-th module, respectively. This represents the ratio of the weighted output power to the weighted input power of the a-th module, i.e., the DC output rate of that module. The larger this value, the larger the output ratio of that module, and the larger its load factor. Similarly, the power supply load factor of each module can be obtained.

[0063] Step S4: Obtain the future contribution coefficient of a module based on its power supply carrying capacity coefficient, inverter temperature, and heat dissipation power of the cooling system; obtain the future output power of each module based on its future contribution coefficient and the total output power required by the grid for future power supply; and control the cascaded level inverter according to the future output power of each module.

[0064] The above methods provide the power supply carrying capacity coefficients for different modules. However, to achieve balanced power distribution among modules, relying solely on electrical parameters such as voltage and current is often insufficient to fully reflect the actual operating status of the modules. Due to differences in the operating environment, load pressure, and heat dissipation conditions of each module, some modules may experience thermal stress accumulation due to insufficient heat dissipation, accelerating device aging and even inducing failures. Therefore, during power regulation, the thermal performance of a module should be considered a crucial factor influencing its ability to participate in output distribution. A comprehensive analysis of heat dissipation efficiency and electrical characteristics can more effectively identify modules at risk of operating at high temperatures, preventing them from operating at high temperatures for extended periods and thus reducing module performance and lifespan, thereby improving the stability and safety of the system.

[0065] The future contribution coefficient of a module is obtained based on its power supply load factor, inverter temperature, and cooling system heat dissipation power. Specifically, the reciprocal of the power supply load factor of a module is normalized to obtain the load pressure characteristic term; the current inverter temperature of the module is added to the hyperparameter to obtain the temperature factor term; the current cooling system heat dissipation power of the module is added to the hyperparameter to obtain the heat dissipation power factor term; the reciprocal of the product of the temperature factor term and the heat dissipation power factor term is calculated to obtain the heat dissipation performance characteristic term; and the weighted sum of the load pressure characteristic term and the heat dissipation performance characteristic term yields the future contribution coefficient of the module.

[0066] The specific calculation model for the future contribution coefficient is as follows:

[0067] ,

[0068] in, Let represent the future contribution coefficient of the 'a'-th module, and 'c' represent the hyperparameter used to prevent the denominator from being 0, with a value of 0.001. This represents the power supply load factor of the a-th module. A smaller value indicates that the module experiences less load pressure. The larger the value, the more load it can share in future power supply, thereby reducing the burden on other modules with larger load-bearing capacity and achieving balanced output of each module. α represents the weighting factor. Preferably, in load balancing control, the module's load-bearing capacity is more critical, as it directly determines whether the module can work stably. Temperature is an auxiliary factor, mainly reflecting long-term and potential risks. Therefore, the weighting factor here is set to 0.65, and it is recommended to be greater than or equal to 0.5, depending on the specific requirements. and These represent the inverter temperature and the cooling system power of the a-th module at the current moment, respectively. This represents the product of the two values. The larger the value, the more heat is accumulating in the module, indicating poor heat dissipation or severe overheating. To prevent damage to the module during subsequent power supply, its power output coefficient should be relatively smaller. The smaller the value, the smaller the output coefficient of the module when supplying power in the future. This allows us to obtain the future contribution coefficient of each module in a cascaded level inverter.

[0069] Furthermore, after obtaining the future contribution coefficient of each module, the future output power of each module can be determined based on its future contribution coefficient and the total output power required by the power grid for future power supply. Specifically, the future contribution coefficient of a module is compared with the sum of the future contribution coefficients of all modules, and then multiplied by the total output power required by the power grid for future power supply to obtain the future output power of that module.

[0070] The specific calculation model is as follows:

[0071] ,

[0072] in, This represents the future output power of the a-th module in a cascaded level inverter. This represents the total output power of the modules required for future power supply to the grid (which can be obtained through existing methods, such as statistical methods based on historical data, and will not be elaborated here), and g represents the number of modules. This represents the future contribution coefficient of the a-th module; This represents the future contribution weight of the a-th module, i.e., its load-sharing ratio. The target output power value of the future power grid is obtained by multiplying the total output power value of the modules required for future power supply by the load sharing ratio coefficient of the a-th module.

[0073] Finally, the cascaded level inverters are controlled based on the future output power of each module. After obtaining the future output power of each module, the control system uses this as the basis for adjustment, dynamically adjusting the inverter's PWM modulation strategy to achieve power control. Specifically, the system adjusts the reference voltage, current amplitude, duty cycle, or carrier frequency of each module's modulation waveform to precisely control its output voltage and current amplitude, thereby achieving the target output power value (future output power). This adjustment method can flexibly match the power load undertaken by each module according to its actual power supply capacity and operating status, effectively improving the system's output balance and overall operating efficiency. The system can be designed with a timed load balancing adjustment task, such as adjusting once per minute (the specific interval depends on actual needs; for example, for systems requiring high-precision load balancing, the adjustment interval can be appropriately shortened to respond more quickly to changes in module status, and vice versa), thereby ensuring the balance of the inverter's continuous power supply.

[0074] In summary, this application analyzes the DC-side input voltage and current of each module of a cascaded level inverter, the output voltage and current of the inverter bridge arm, as well as the inverter temperature and the heat dissipation power of the cooling system to obtain the future output power of each module. Based on the future output power of each module, the application achieves real-time balanced power distribution among the modules, thereby improving the operating efficiency and system stability of the cascaded level inverter in the smart grid.

[0075] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0076] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for cascaded level inverters in smart grids, characterized in that, The method includes: Obtain the DC-side input voltage and current, inverter bridge arm output voltage and current, inverter temperature, and heat dissipation power of the cooling system for each module of the cascaded level inverter. The voltage unreliability index and the current unreliability index are obtained based on the DC side input voltage and current within a preset time period of a module; the sum of the voltage unreliability index and the current unreliability index is the power supply unreliability index. Obtain the input power and output power of a module at each moment within a preset time period; obtain the weighted input power and weighted output power based on the input power and output power at each moment within the preset time period; obtain the power supply carrying capacity coefficient based on the power supply unreliability index, weighted input power, and weighted output power; The future contribution coefficient of a module is obtained based on its power supply carrying capacity coefficient, inverter temperature, and heat dissipation power of the cooling system; the future output power of each module is obtained based on its future contribution coefficient and the total output power required by the grid for future power supply; and the cascaded level inverter is controlled according to the future output power of each module.

2. The cascaded level inverter control method for smart grids according to claim 1, characterized in that, The step of obtaining voltage unreliability indicators and current unreliability indicators based on the DC-side input voltage and current within a preset time period of a module includes: The STL algorithm is used to decompose the DC-side input voltage and current of a module within a preset time period, and obtain the trend and residual terms corresponding to the DC-side input voltage and the DC-side input current. Based on the trend and residual terms corresponding to the DC-side input voltage and the DC-side input current, the voltage unreliability index and the current unreliability index are obtained respectively.

3. The cascaded level inverter control method for smart grids according to claim 2, characterized in that, The process of obtaining voltage unreliability indices and current unreliability indices based on the trend and residual terms corresponding to the DC-side input voltage and the trend and residual terms corresponding to the DC-side input current, respectively, includes: The sampling interval distance between each data point in the residual term corresponding to the DC-side input voltage and the data point at the current time is added to a first preset value, and the weight of each data point is calculated by summing the two data points. The sampling interval distance is the number of intervals between two data points. The weighted average of the absolute values ​​of the amplitudes of each data point in the residual term is calculated using the weight of each data point in the residual term and then normalized to obtain the residual feature term. The slope between every two adjacent data points in the trend term corresponding to the DC-side input voltage is calculated, and a slope sequence is constructed according to the time sequence. The slope between two adjacent data points is the slope of the next data point between the two adjacent data points. The slope corresponding to the data point; similarly, the weight corresponding to each slope in the slope sequence is obtained based on the sampling interval distance between each slope in the slope sequence and the slope at the current time; the unreliability factor corresponding to the slope is obtained by negatively mapping an exponential function with the natural constant as the base; the weighted average of the unreliability factor corresponding to each slope is calculated using the weight corresponding to each slope and normalized to obtain the trend feature term; the voltage unreliability index is obtained by weighted summation of the residual feature term and the trend feature term; similarly, the current unreliability index is obtained based on the trend term and residual term corresponding to the DC side input current.

4. The cascaded level inverter control method for smart grids according to claim 1, characterized in that, The acquisition of the input power and output power of a module at various times within a preset time period includes: The input power of a module at each moment is obtained by collecting the DC side input voltage and current of the module at each moment within a preset time period, and the output power of the module at each moment is obtained by collecting the inverter bridge arm output voltage and current at each moment.

5. A cascaded level inverter control method for smart grids according to claim 1, characterized in that, The process of obtaining weighted input power and weighted output power based on input power and output power at various times within a preset time period includes: The weights corresponding to each time point are obtained based on the sampling interval distance between each time point within a preset time period and the current time point; the weighted average of the input power at each time point is calculated using the weights corresponding to each time point to obtain the weighted input power; the weighted average of the output power at each time point is calculated using the weights corresponding to each time point to obtain the weighted output power.

6. The cascaded level inverter control method for smart grids according to claim 1, characterized in that, The power supply carrying capacity coefficient, derived based on power supply unreliability indicators, weighted input power, and weighted output power, includes: The power supply carrying capacity coefficient of a module is obtained by comparing its weighted output power with its weighted input power and multiplying the weighted output power with the module's power supply unreliability index.

7. A cascaded level inverter control method for smart grids according to claim 1, characterized in that, The method of obtaining the future contribution coefficient of a module based on its power supply carrying capacity, inverter temperature, and heat dissipation power of the cooling system includes: The load-bearing pressure characteristic term is obtained by normalizing the reciprocal of the power supply load factor of a module; the temperature factor term is obtained by adding the inverter temperature of the module at the current moment to the hyperparameter; the heat dissipation power of the heat dissipation system of the module at the current moment is obtained by adding the hyperparameter; the heat dissipation performance characteristic term is obtained by taking the reciprocal of the product of the temperature factor term and the heat dissipation power factor term; and the future contribution coefficient of the module is obtained by weighted summation of the load-bearing pressure characteristic term and the heat dissipation performance characteristic term.

8. A cascaded level inverter control method for smart grids according to claim 1, characterized in that, The process of obtaining the future output power of each module based on the future contribution coefficient of each module and the total output power value of the modules required for future power supply by the power grid includes: The future output power of a module is obtained by comparing its future contribution coefficient with the sum of the future contribution coefficients of all modules and multiplying it by the total output power of the modules required by the power grid in the future.

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