Energy storage inverter inductance temperature identification method based on artificial intelligence, computer equipment and storage medium

Abstract

The invention relates to the technical field of energy storage inverters, and discloses an energy storage inverter inductance temperature identification method based on artificial intelligence, computer equipment and a storage medium. An LSTM hybrid neural network model is constructed, and then the trained GRUAmp is constructed; when the LSTM hybrid neural network model is applied to the real-time operation condition, the inductance temperature of the energy storage inverter can be recognized under the complex and changeable working conditions, the accuracy of inductance temperature recognition is effectively improved, performance optimization and / or fault prediction of the energy storage inverter are / is facilitated, the safety and stability of the energy storage inverter are improved, and the energy storage inverter control method is suitable for popularization and application. High popularization and application values are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage inverters, and in particular to an energy storage inverter inductor temperature recognition method based on artificial intelligence, a computer device and a storage medium. Background Art

[0002] With the continuous progress and innovation of energy storage inverter technology, the inductor, as an indispensable key component in the inverter structure, faces extreme environmental challenges. The abnormal increase in its temperature will not only seriously affect the expected service life of the inductor, but may even directly cause damage to the inductor. Therefore, it is particularly critical and important to implement real-time and accurate monitoring measures for the inductor temperature.

[0003] Accurate identification of inductor temperature plays a vital role in optimizing the overall performance of the inverter and predicting potential failures. However, in traditional inverter design, directly sampling the inductor temperature faces many practical difficulties. Specifically, this approach not only leads to a significant increase in hardware costs, but also the placement of temperature sensors such as NTC (negative temperature coefficient thermistor) inside the inductor may further aggravate eddy current losses, thereby accelerating the rise in inductor temperature. Therefore, traditional inverters usually do not directly sample the inductor temperature.

[0004] During the normal operation of the energy storage inverter, the inductor temperature will change dynamically according to different operating conditions. These operating condition data cover the inverter's operating parameters, such as input voltage, output voltage, current intensity, etc., as well as the physical property information of the inductor itself.

[0005] In summary, how to effectively identify and monitor the inductor temperature of the inverter, and thus improve the safety and stability performance of the inverter, has become one of the important technical problems that technicians in this field urgently need to overcome.

[0006] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0007] The present invention provides an energy storage inverter inductor temperature recognition method based on artificial intelligence, a computer device and a storage medium to solve the problems existing in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides an energy storage inverter inductor temperature identification method based on artificial intelligence, the method comprising:

[0010] S1. Build and train a GRU & LSTM hybrid neural network model;

[0011] S2. Apply the GRU&LSTM hybrid neural network model to the real-time operating condition, collect the inductor current, inductor value, and inductor initial value data in the real-time operating condition, and input them into the GRU&LSTM hybrid neural network model to obtain the energy storage inverter inductor temperature output by the GRU&LSTM hybrid neural network model.

[0012] Furthermore, in the energy storage inverter inductor temperature identification method based on artificial intelligence, after S2, the method further includes:

[0013] S3. Optimize the performance of the energy storage inverter and / or predict faults according to the output inductor temperature of the energy storage inverter.

[0014] Furthermore, in the energy storage inverter inductor temperature identification method based on artificial intelligence, S1 includes:

[0015] S11. Construct a GRU & LSTM hybrid neural network model based on the MATLAB neural network toolbox, wherein the GRU & LSTM hybrid neural network model includes an input layer, a Dropout layer, an LSTM layer, a GRU layer, a fully connected layer, a weight layer, and an output layer;

[0016] S12. Train the GRU&LSTM hybrid neural network model.

[0017] Furthermore, in the energy storage inverter inductor temperature identification method based on artificial intelligence, S12 includes:

[0018] S121. In a laboratory environment, obtaining data of an inductor; the data including inductor current, inductor value, initial inductor value and inductor temperature;

[0019] S122, using the inductor current, inductor value, and initial inductor value as data input for training and testing the GRU & LSTM hybrid neural network model, and using the inductor temperature as data output for training and testing the GRU & LSTM hybrid neural network model;

[0020] S123, randomly dividing the acquired data into training data and test data;

[0021] S124, importing the training data into the GRU&LSTM hybrid neural network model, and training the GRU&LSTM hybrid neural network model until the GRU&LSTM hybrid neural network model converges;

[0022] S125. Import the test data into the GRU&LSTM hybrid neural network model, verify the converged GRU&LSTM hybrid neural network model, and when the maximum error MAX of the GRU&LSTM hybrid neural network model is less than or equal to 5% and the average error MAE is less than or equal to 3%, confirm that the evaluation result of the GRU&LSTM hybrid neural network model reaches the expected goal.

[0023] Furthermore, in the energy storage inverter inductor temperature identification method based on artificial intelligence, S121 includes:

[0024] S1211. In a laboratory environment, keep the inductor temperature constant, change the current across the inductor, sample the current inductor current through DSP, and at the moment when the IGBT is turned on. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of the inductance L at any moment can be obtained according to the following calculation formula:

[0025] ;

[0026] S1212. In a laboratory environment, keep the inductor current unchanged, adjust the inductor temperature, and then sample the current inductor current through DSP as described in S1211. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of and calculate the inductance L;

[0027] S1213, replace the inductor with a different initial inductance and repeat S1211 to S1212 to obtain more inductance data; the data includes inductor current, inductor inductance, inductor initial inductance and inductor temperature.

[0028] Furthermore, in the energy storage inverter inductor temperature identification method based on artificial intelligence, after S123, the method further includes:

[0029] S123.1. Map the training data uniformly to the interval [0, 1] to obtain the normalized training data.

[0030] Further, in the energy storage inverter inductor temperature identification method based on artificial intelligence, S124 includes:

[0031] S124.1. Select the Adam stochastic gradient descent algorithm to update the network weights;

[0032] S124.2. Import the training data into the GRU&LSTM hybrid neural network model for a set number of rounds of training until the GRU&LSTM hybrid neural network model converges, and set the initial learning rate to 0.0001.

[0033] Further, in the energy storage inverter inductor temperature identification method based on artificial intelligence, in S125, the calculation formula of the maximum error MAX is:

[0034] ;

[0035] The calculation formula of the average error MAE is:

[0036] .

[0037] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the energy storage inverter inductor temperature identification method based on artificial intelligence as provided in the first aspect above.

[0038] In a third aspect, the present invention provides a computer-readable storage medium having computer executable instructions stored thereon, wherein the computer executable instructions are executed by a computer processor to implement the artificial intelligence-based energy storage inverter inductor temperature identification method provided in the first aspect above.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention provides an energy storage inverter inductor temperature recognition method based on artificial intelligence, a computer device and a storage medium. By constructing and training a GRU&LSTM hybrid neural network model, and then applying the trained GRU&LSTM hybrid neural network model to real-time operating conditions, the inductor temperature of the energy storage inverter can be recognized under complex and changeable working conditions, thereby effectively improving the accuracy of inductor temperature recognition, being beneficial to performance optimization and / or fault prediction of the energy storage inverter, so as to improve the safety and stability of the energy storage inverter, and having high promotion and application value.

[0041] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a flow chart of an artificial intelligence-based energy storage inverter inductor temperature identification method provided in Embodiment 1 of the present invention;

[0044] Figure 2 is a relationship diagram between the inductance and the inductor current mentioned in the first embodiment of the present invention;

[0045] Figure 3 is a relationship diagram between the inductance and the inductance temperature mentioned in the first embodiment of the present invention;

[0046] Figure 4 It is a structural diagram of the GRU&LSTM hybrid neural network model mentioned in the first embodiment of the present invention;

[0047] Figure 5-6 It is a schematic diagram of the structure of the energy storage inverter and a schematic diagram of the calculation principle of the energy storage inverter inductance mentioned in the first embodiment of the present invention;

[0048] Figure 7 It is a structural diagram of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0049] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0050] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0051] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0052] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0053] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0054] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0055] In this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0056] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0057] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0058] Embodiment 1

[0059] In view of the defects of the above-mentioned prior art, the applicant, based on many years of rich practical experience and professional knowledge in this field, and in conjunction with the application of theory, actively conducts research and innovation in the hope of creating a technology that can solve the defects of the prior art. After continuous research and design, and after repeated trial production and improvement, the present invention with real practical value was finally created.

[0060] Please refer to Figure 1 , an embodiment of the present invention provides an energy storage inverter inductor temperature identification method based on artificial intelligence, the method comprising:

[0061] S1. Build and train a GRU&LSTM hybrid neural network model.

[0062] It should be noted that GRU (Gated Recurrent Unit) and LSTM (Long Short-Term Memory): Both are variants of recurrent neural networks (RNNs) that are specifically designed to process sequence data. They are able to capture long-term dependencies in data, which is very useful in fields such as time series analysis and natural language processing.

[0063] This embodiment combines the advantages of GRU and LSTM to build a hybrid neural network model. This can fully utilize the different advantages of the two in processing sequence data and improve the performance of the model.

[0064] In this step, first, according to the requirements of energy storage inverter inductor temperature recognition, the GRU&LSTM hybrid neural network model is constructed. Then, a large amount of collected data or simulation data is used to train and test the network so that the network can learn the mapping relationship between input parameters and inductor temperature.

[0065] Through training, the model can accurately identify the inductor temperature of the energy storage inverter under different working conditions.

[0066] S2. Apply the GRU&LSTM hybrid neural network model to the real-time operating condition, collect the inductor current, inductor value, and inductor initial value data in the real-time operating condition, and input them into the GRU&LSTM hybrid neural network model to obtain the energy storage inverter inductor temperature output by the GRU&LSTM hybrid neural network model.

[0067] It should be noted that during the real-time operation of the energy storage inverter, real-time data of the inductor current, inductor value and initial inductor value are collected through sensors. These data are the necessary inputs for the model to make predictions.

[0068] The collected real-time data is input into the trained GRU&LSTM hybrid neural network model, and the model will calculate the inductor temperature of the energy storage inverter under the current working conditions based on the input data. Since the model has learned the complex relationship between the input data and the inductor temperature, it can provide accurate prediction results.

[0069] In the laboratory, the inductance and inductor current are measured as follows Figure 2 The relationship between the inductance and the inductance temperature is as follows: Figure 3 From the relationship between the inductance, inductance temperature and inductance current, we can see that there is a nonlinear relationship between the inductance, inductance temperature and inductance current.

[0070] Through such a method, the embodiment of the present invention can accurately identify the inductor temperature of the energy storage inverter under complex and changeable working conditions, thereby providing strong support for the performance optimization and fault prediction of the energy storage inverter. This artificial intelligence-based identification method not only improves the accuracy of inductor temperature identification, but also improves the safety, stability and overall performance of the energy storage inverter.

[0071] In one implementation of this embodiment, after S2, the method further includes:

[0072] S3. Optimize the performance of the energy storage inverter and / or predict faults according to the output inductor temperature of the energy storage inverter.

[0073] It should be noted that S3 is based on S2 and uses the energy storage inverter inductor temperature information output by the model to conduct a deeper analysis and application of the energy storage inverter. This step mainly includes two aspects: performance optimization and fault prediction.

[0074] Performance optimization: Based on the real-time monitoring of the inductor temperature, the operating status of the energy storage inverter can be analyzed in detail. If the inductor temperature is too high, it may mean that the inverter has problems such as overload and poor heat dissipation. By adjusting the inverter's operating parameters (such as input voltage, output voltage, current limit, etc.) or improving its heat dissipation conditions, the inductor temperature can be effectively reduced, thereby optimizing the inverter's performance and improving its efficiency and stability.

[0075] Fault prediction: The abnormal continuous rise in inductor temperature may be a precursor to an impending inverter failure. By comparing historical data and real-time monitoring data, a correlation model between inductor temperature and inverter fault type can be established. When the inductor temperature reaches a certain preset threshold, the system can issue an early warning signal to prompt the operator to repair or replace the inverter, thereby avoiding the occurrence of failures and ensuring the continuous and stable operation of the system.

[0076] In practical applications, performance optimization and fault prediction often complement each other. Through real-time monitoring and analysis of the inductor temperature, not only can problems be discovered and solved in a timely manner and the performance of the inverter be improved, but potential faults can also be predicted, maintenance costs can be reduced, and the service life of the equipment can be extended. Therefore, the addition of S3 makes the method of this embodiment more comprehensive and practical, and provides new ideas and means for the intelligent management and maintenance of energy storage inverters.

[0077] In one implementation of this embodiment, the S1 includes:

[0078] S11. Construct a GRU & LSTM hybrid neural network model based on the MATLAB neural network toolbox, wherein the GRU & LSTM hybrid neural network model includes an input layer, a Dropout layer, an LSTM layer, a GRU layer, a fully connected layer, a weight layer, and an output layer;

[0079] It should be noted that this embodiment makes full use of MATLAB, a powerful scientific computing and simulation platform, and its built-in neural network toolbox as the basic tool for building the GRU & LSTM hybrid neural network model. This toolbox provides a wealth of neural network construction and training functions, which provides us with great convenience.

[0080] like Figure 4 As shown in the figure, our carefully designed GRU&LSTM hybrid neural network model consists of multiple layers, including input layer, Dropout layer, LSTM layer, GRU layer, fully connected layer, weight layer and output layer. Each layer has a specific function and works together to achieve accurate prediction of inductor temperature.

[0081] Input layer: responsible for receiving and processing the real-time collected energy storage inverter operating parameters as the input data of the model.

[0082] Dropout layer: used to randomly discard some neurons during the training process to prevent the model from overfitting and improve the generalization ability of the model.

[0083] LSTM layer and GRU layer: As the core part of the model, they are responsible for capturing the long-term dependencies in the input data and achieving accurate prediction of the inductor temperature.

[0084] Fully connected layer: summarizes and integrates the outputs of the LSTM layer and the GRU layer to provide input for the subsequent weight layer.

[0085] Weight layer: Weights the output of the fully connected layer to generate the final prediction result.

[0086] Output layer: responsible for outputting the model's predicted value of the inductor temperature.

[0087] S12. Train the GRU&LSTM hybrid neural network model.

[0088] It should be noted that before training the model, we carefully prepared a large amount of collected data and / or simulation data, which covers the operating parameters of the energy storage inverter and the corresponding inductor temperature under various working conditions. These data will be used to train the model so that it can learn the complex mapping relationship between the input parameters and the inductor temperature.

[0089] We use the training algorithm provided by the MATLAB neural network toolbox to train the GRU&LSTM hybrid neural network model. During the training process, we continuously adjust the parameters and structure of the model to minimize the prediction error and improve the prediction accuracy of the model.

[0090] In one implementation of this embodiment, the S12 includes:

[0091] S121. In a laboratory environment, obtaining data of an inductor; the data including inductor current, inductor value, initial inductor value and inductor temperature;

[0092] S122, using the inductor current, inductor value, and initial inductor value as data input for training and testing the GRU & LSTM hybrid neural network model, and using the inductor temperature as data output for training and testing the GRU & LSTM hybrid neural network model;

[0093] S123, randomly dividing the acquired data into training data and test data;

[0094] S124, importing the training data into the GRU&LSTM hybrid neural network model, and training the GRU&LSTM hybrid neural network model until the GRU&LSTM hybrid neural network model converges;

[0095] S125. Import the test data into the GRU&LSTM hybrid neural network model, verify the converged GRU&LSTM hybrid neural network model, and when the maximum error MAX of the GRU&LSTM hybrid neural network model is less than or equal to 5% and the average error MAE is less than or equal to 3%, confirm that the evaluation result of the GRU&LSTM hybrid neural network model reaches the expected goal.

[0096] In one implementation of this embodiment, the S121 includes:

[0097] S1211. In a laboratory environment, keep the inductor temperature constant, change the current across the inductor, sample the current inductor current through DSP, and at the moment when the IGBT is turned on. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of the inductance L at any moment can be obtained according to the following calculation formula:

[0098] Specific process such as Figure 5-6 shown.

[0099] S1212. In a laboratory environment, keep the inductor current unchanged, adjust the inductor temperature, and then sample the current inductor current through DSP as described in S1211. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of and calculate the inductance L;

[0100] S1213, replace the inductor with a different initial inductance and repeat S1211 to S1212 to obtain more inductance data; the data includes inductor current, inductor inductance, inductor initial inductance and inductor temperature.

[0101] It should be noted that by executing S1211 to S1213, we can obtain a large amount of energy storage inverter operation data covering different working conditions. These data not only help us train a more accurate and reliable GRU&LSTM hybrid neural network model, but also provide strong support for subsequent model testing and verification.

[0102] In summary, the introduction of step S122.1 not only enriches our data set, but also provides more comprehensive and reliable data support for the training and testing of the GRU&LSTM hybrid neural network model. The implementation of this step has laid a solid foundation for our subsequent inductor temperature prediction and energy storage inverter performance optimization.

[0103] In an implementation manner of this embodiment, after S123, the method further includes:

[0104] S123.1. Map the training data uniformly to the interval [0, 1] to obtain the normalized training data.

[0105] It should be noted that in the field of machine learning, normalization is a commonly used data preprocessing technology. It maps the original data to a specific interval (such as [0, 1]) to eliminate the dimensional differences and numerical range differences between different features, thereby accelerating the model training process and improving the model's prediction accuracy and generalization ability.

[0106] In this embodiment, we choose a normalization method that uniformly maps the training data to the interval [0, 1]. This method can ensure that all feature values ​​are at the same order of magnitude, making it easier for the model to learn and predict.

[0107] By executing step S123.1, we can map all feature values ​​in the training data set to the interval [0, 1], thereby eliminating the dimension differences and numerical range differences between different features. This processing can not only accelerate the training process of the GRU&LSTM hybrid neural network model, but also improve the prediction accuracy and generalization ability of the model.

[0108] In one implementation of this embodiment, the S124 includes:

[0109] S124.1. Select the Adam stochastic gradient descent algorithm to update the network weights;

[0110] S124.2. Import the training data into the GRU&LSTM hybrid neural network model for a set number of rounds of training until the GRU&LSTM hybrid neural network model converges, and set the initial learning rate to 0.0001.

[0111] It should be noted that the set number of rounds can be set to 6000 rounds, for example.

[0112] In one implementation of this embodiment, in S125, the calculation formula of the maximum error MAX is:

[0113] ;

[0114] The calculation formula of the average error MAE is:

[0115] .

[0116] Although the terms such as neural network model and inductor are used more frequently in this application, the possibility of using other terms is not excluded. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

[0117] An embodiment of the present invention provides an energy storage inverter inductor temperature identification method based on artificial intelligence. By constructing and training a GRU&LSTM hybrid neural network model, and then applying the trained GRU&LSTM hybrid neural network model to real-time operating conditions, the inductor temperature of the energy storage inverter can be identified under complex and changeable working conditions, thereby effectively improving the accuracy of inductor temperature identification, facilitating performance optimization and / or fault prediction of the energy storage inverter, so as to improve the safety and stability of the energy storage inverter, and having a high value of promotion and application.

[0118] Embodiment 2

[0119] Figure 7 A schematic diagram of the structure of a computer device provided in Embodiment 2 of the present invention. Figure 7 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 7 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0120] like Figure 7 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0121] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0122] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0123] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7not shown, usually called a "hard drive"). Although Figure 7 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0124] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0125] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via a bus 18. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the energy storage inverter inductor temperature identification method based on artificial intelligence provided in the embodiment of the present invention.

[0127] Embodiment 3

[0128] Embodiment 3 of the present invention provides a computer-readable storage medium having computer-executable instructions stored thereon. When the instructions are executed by a processor, the method for identifying the inductor temperature of an energy storage inverter based on artificial intelligence as provided in all the embodiments of the invention of this application is implemented.

[0129] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0130] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0132] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0133] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. An artificial intelligence-based energy storage inverter inductor temperature identification method, characterized in that: The method comprises: S1. Build and train a GRU & LSTM hybrid neural network model; S2. Apply the GRU&LSTM hybrid neural network model to the real-time operating condition, collect the inductor current, inductor value, and inductor initial value data in the real-time operating condition, and input them into the GRU&LSTM hybrid neural network model to obtain the energy storage inverter inductor temperature output by the GRU&LSTM hybrid neural network model.

2. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 1 is characterized in that: After S2, the method further includes: S3. Optimize the performance of the energy storage inverter and / or predict faults according to the output inductor temperature of the energy storage inverter.

3. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 1 is characterized in that: The S1 includes: S11. Construct a GRU & LSTM hybrid neural network model based on the MATLAB neural network toolbox, wherein the GRU & LSTM hybrid neural network model includes an input layer, a Dropout layer, an LSTM layer, a GRU layer, a fully connected layer, a weight layer, and an output layer; S12. Train the GRU&LSTM hybrid neural network model.

4. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 3 is characterized in that: The S12 includes: S121. In a laboratory environment, obtaining data of an inductor; the data including inductor current, inductor value, initial inductor value and inductor temperature; S122, using the inductor current, inductor value, and initial inductor value as data input for training and testing the GRU & LSTM hybrid neural network model, and using the inductor temperature as data output for training and testing the GRU & LSTM hybrid neural network model; S123, randomly dividing the acquired data into training data and test data; S124, importing the training data into the GRU&LSTM hybrid neural network model, and training the GRU&LSTM hybrid neural network model until the GRU&LSTM hybrid neural network model converges; S125. Import the test data into the GRU&LSTM hybrid neural network model, verify the converged GRU&LSTM hybrid neural network model, and when the maximum error MAX of the GRU&LSTM hybrid neural network model is less than or equal to 5% and the average error MAE is less than or equal to 3%, confirm that the evaluation result of the GRU&LSTM hybrid neural network model reaches the expected goal.

5. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 4 is characterized in that: The S121 includes: S1211. In a laboratory environment, keep the inductor temperature constant, change the current across the inductor, sample the current inductor current through DSP, and at the moment when the IGBT is turned on. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of the inductance L at any moment can be obtained according to the following calculation formula: ； S1212. In a laboratory environment, keep the inductor current unchanged, adjust the inductor temperature, and then sample the current inductor current through DSP as described in S1211. , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value; when the IGBT driver is turned off , record the instantaneous value of the current passing through the inductor , the voltage drop across the inductor is the bus voltage and inverter capacitor voltage The instantaneous value of and calculate the inductance L; S1213, replace the inductor with a different initial inductance and repeat S1211 to S1212 to obtain more inductance data; the data includes inductor current, inductor inductance, inductor initial inductance and inductor temperature.

6. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 4 is characterized in that: After S123, the method further includes: S123.

1. Map the training data uniformly to the interval [0, 1] to obtain the normalized training data.

7. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 4 is characterized in that: The S124 includes: S124.

1. Select the Adam stochastic gradient descent algorithm to update the network weights; S124.

2. Import the training data into the GRU&LSTM hybrid neural network model for a set number of rounds of training until the GRU&LSTM hybrid neural network model converges, and set the initial learning rate to 0.0001.

8. The method for identifying inductor temperature of an energy storage inverter based on artificial intelligence according to claim 4 is characterized in that: In S125, the calculation formula of the maximum error MAX is: ； The calculation formula of the average error MAE is: 。 9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the energy storage inverter inductor temperature identification method based on artificial intelligence as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: The computer executable instructions are executed by a computer processor to implement the artificial intelligence-based energy storage inverter inductor temperature identification method as described in any one of claims 1 to 8.

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