Intelligent excitation system control method based on local cloud computing, intelligent excitation system, device and equipment

Abstract

The invention relates to an intelligent excitation system control method based on local cloud computing, an intelligent excitation system, a device and equipment, which are applied to a local cloud. The method comprises the following steps: detecting the health state of each candidate control cabinet of the intelligent excitation system to obtain a health state detection result corresponding to each candidate control cabinet; the candidate control cabinets at least comprise an adjusting cabinet, a power cabinet and a field suppression cabinet; determining a target control cabinet in each candidate control cabinet according to the health state detection result corresponding to each candidate control cabinet; the target control cabinet is a candidate control cabinet meeting a triggering condition that the control right is taken over; acquiring key data of the target control cabinet; the key data comprises key operation parameters and / or instruction execution logic; the local cloud is updated according to the key data, so that the updated local cloud is used as a twin body of the target control cabinet; and the twin body of the target control cabinet is used for replacing the target control cabinet to control the operation of the intelligent excitation system. By adopting the method, the reliability of the intelligent excitation system can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a local cloud computing-based intelligent excitation system control method, an intelligent excitation system, an apparatus, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] The power supply and its auxiliary equipment that supply the excitation current to the synchronous generator are collectively called the excitation system. It is generally composed of two main parts: the excitation power unit and the excitation regulator. The excitation power unit provides the excitation current to the synchronous generator rotor; and the excitation regulator controls the output of the excitation power unit according to the input signal and the given regulation criteria. The automatic excitation regulator of the excitation system plays a considerable role in improving the stability of the parallel units in the power system.

[0003] However, in the related technology, when the control channel of the excitation regulation cabinet fails, it will affect the precise regulation of the excitation current of the synchronous generator, making it impossible to adaptively adjust the excitation current according to predetermined requirements, which will eventually lead to a reduction in the reliability of the intelligent excitation system and fail to provide strong guarantees for the safe, stable and efficient operation of the power system.

[0004] Therefore, there is a problem of low reliability of the intelligent excitation system in the related technology. Summary of the invention

[0005] Based on this, it is necessary to provide an intelligent excitation system control method, intelligent excitation system, device, computer equipment, computer-readable storage medium and computer program product based on local cloud computing, which can improve the reliability of the intelligent excitation system in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for controlling an intelligent excitation system based on local cloud computing, which is applied to a local cloud and includes:

[0007] Detect the health status of each candidate control cabinet of the intelligent excitation system to obtain the health status detection result corresponding to each candidate control cabinet; the candidate control cabinets at least include a regulating cabinet, a power cabinet and a demagnetization cabinet;

[0008] According to the health status detection results corresponding to each of the candidate control cabinets, a target control cabinet is determined from among the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over, and the candidate control cabinets include a regulating cabinet and a power cabinet;

[0009] Acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic;

[0010] The local cloud is updated according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0011] In one embodiment, there are multiple intelligent excitation systems, and the method further includes:

[0012] For any candidate control cabinet, obtain a first health score threshold and a second health score threshold corresponding to the any candidate control cabinet;

[0013] The first health score threshold is determined by horizontally comparing the health scores of the candidate control cabinets corresponding to any candidate control cabinet in each of the intelligent excitation systems; the second health score threshold is determined by vertically comparing the health score corresponding to any candidate control cabinet with the historical health score corresponding to any candidate control cabinet;

[0014] The health score threshold corresponding to any candidate control cabinet is determined according to the average value between the first health score threshold and the second health score threshold; the health score threshold is used to determine whether any candidate control cabinet meets the trigger condition for control to be taken over.

[0015] In one embodiment, obtaining the first health score threshold and the second health score threshold corresponding to any candidate control cabinet includes:

[0016] Obtaining other health scores; the other health scores include health scores corresponding to candidate control cabinets in other intelligent excitation systems corresponding to any candidate control cabinet; the other intelligent excitation systems include intelligent excitation systems in each of the intelligent excitation systems except the intelligent excitation system where any candidate control cabinet is located;

[0017] The first health score threshold is determined according to an average value between the other health scores and the health score corresponding to any candidate control cabinet.

[0018] In one embodiment, obtaining the first health score threshold and the second health score threshold corresponding to any candidate control cabinet includes:

[0019] Obtaining a historical health score; the historical health score includes each health score obtained by detecting any candidate control cabinet in a historical time period;

[0020] The second health score threshold is determined according to an average value between the historical health score and the health score corresponding to any candidate control cabinet.

[0021] In one embodiment, determining the health score threshold corresponding to any candidate control cabinet according to the average value between the first health score threshold and the second health score threshold includes:

[0022] Obtaining first weight information corresponding to the first health score threshold, and obtaining second weight information corresponding to the second health score threshold;

[0023] According to the first weight information and the second weight information, a weighted average value between the first health score threshold and the second health score threshold is determined as the health score threshold corresponding to any candidate control cabinet.

[0024] In one embodiment, the health status detection result includes a health score, and determining a target control cabinet from each of the candidate control cabinets according to the health status detection result corresponding to each of the candidate control cabinets includes:

[0025] For any candidate control cabinet, obtain a health score threshold corresponding to the any candidate control cabinet;

[0026] If the health score corresponding to any candidate control cabinet is lower than the health score threshold corresponding to any candidate control cabinet, it is determined that any candidate control cabinet meets the trigger condition for control right to be taken over, and any candidate control cabinet is used as the target control cabinet.

[0027] In one embodiment, the detecting the health status of each candidate control cabinet of the intelligent excitation system to obtain the health status detection result corresponding to each candidate control cabinet includes:

[0028] Obtaining an operation status log of the intelligent excitation system; the operation status log includes operation status data of any candidate control cabinet;

[0029] Obtaining data operation rules corresponding to each health indicator, and performing operation processing on the operation status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain health indicator data of any candidate control cabinet; the health indicator is an indicator used to evaluate the health status of any candidate control cabinet;

[0030] According to the health indicator data of any candidate control cabinet, a health status detection result corresponding to any candidate control cabinet is obtained.

[0031] In one embodiment, the method further comprises:

[0032] Receiving requests sent by each of the candidate control cabinets;

[0033] In the case of detecting a target request, determining that the candidate control cabinet sending the target request meets the trigger condition for control to be taken over; the target request is used to instruct the local cloud to take over control;

[0034] The candidate control cabinet that sends the target request is used as the target control cabinet.

[0035] In one of the embodiments, the local cloud uses at least one of a constant voltage operation mode and a constant current operation mode to control the operation of the intelligent excitation system.

[0036] In a second aspect, the present application further provides an intelligent excitation system based on local cloud computing, comprising: a candidate control cabinet and a local cloud; the candidate control cabinet comprises at least a regulating cabinet, a power cabinet and a demagnetization cabinet;

[0037] The local cloud is used to detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection result corresponding to each candidate control cabinet;

[0038] The local cloud is used to determine a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over;

[0039] The local cloud is used to obtain key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic;

[0040] The local cloud is used to update the local cloud according to the key data, so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0041] In a third aspect, the present application also provides an intelligent excitation system control device based on local cloud computing, which is applied to the local cloud, including:

[0042] A detection module is used to detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection result corresponding to each candidate control cabinet; the candidate control cabinets at least include a regulating cabinet, a power cabinet and a demagnetization cabinet;

[0043] A determination module, used to determine a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over;

[0044] An acquisition module, used to acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic;

[0045] An update module is used to update the local cloud according to the key data so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0046] In a fourth aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0047] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0048] In a sixth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0049] The above-mentioned intelligent excitation system control method, intelligent excitation system, device, computer equipment, computer-readable storage medium and computer program product based on local cloud computing are applied to the local cloud, and the health status of each candidate control cabinet of the intelligent excitation system is detected to obtain the health status detection results corresponding to each candidate control cabinet; the candidate control cabinets include at least a regulating cabinet, a power cabinet and a demagnetization cabinet; according to the health status detection results corresponding to each candidate control cabinet, the target control cabinet is determined among the candidate control cabinets; the key data of the target control cabinet is obtained; the key data includes key operating parameters and / or instruction execution logic; the local cloud is updated according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0050] In this way, when it is determined that there is a target control cabinet among the candidate control cabinets of the intelligent excitation system that meets the trigger conditions for the control right to be taken over according to the health status detection results corresponding to each candidate control cabinet, such as a failure of the candidate control cabinet, the local cloud can obtain the key data of the target control cabinet, including key operating parameters and / or instruction execution logic, and can update itself based on the key data to use the updated local cloud as the twin of the target control cabinet to control the operation of the intelligent excitation system instead of the target control cabinet. When the candidate control cabinet meets the trigger conditions for the control right to be taken over, the control of the candidate control cabinet is taken over by the local cloud, and the various components in the intelligent excitation system are continued to be controlled, thereby avoiding the situation where the intelligent excitation system cannot continue to operate when the candidate control cabinet cannot control the operation of the intelligent excitation system, thereby effectively improving the reliability of the intelligent excitation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 An application environment diagram of an intelligent excitation system control method based on local cloud computing in one embodiment;

[0053] Figure 2 A schematic flow chart of a method for controlling an intelligent excitation system based on local cloud computing in one embodiment;

[0054] Figure 3 A schematic flow chart of a step of determining a target control cabinet from among candidate control cabinets according to health status detection results corresponding to each candidate control cabinet in an embodiment;

[0055] Figure 4 is an application environment diagram of yet another intelligent excitation system control method based on local cloud computing in one embodiment;

[0056] Figure 5 A schematic flow chart of a method for controlling an intelligent excitation system based on local cloud computing in another embodiment;

[0057] Figure 6 is a structural block diagram of an intelligent excitation system control device based on local cloud computing in one embodiment;

[0058] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0061] The intelligent excitation system control method based on local cloud computing provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The local cloud 102 communicates with the candidate control cabinet 104 through a network, for example, through an optical fiber point-to-point network or a wireless network. The data storage system can store data that the local cloud 102 needs to process. The data storage system can be integrated on the local cloud 102, or placed on the cloud or other network servers. The local cloud 102 can detect the health status of each candidate control cabinet 104 of the intelligent excitation system and obtain the health status detection results corresponding to each candidate control cabinet; the candidate control cabinet 104 includes at least a regulating cabinet, a power cabinet and a demagnetization cabinet; the local cloud 102 can determine the target control cabinet in each candidate control cabinet 104 according to the health status detection results corresponding to each candidate control cabinet 104; the target control cabinet is the candidate control cabinet 104 that meets the triggering conditions for the control right to be taken over; the local cloud 102 can obtain the key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; the local cloud 102 can update the local cloud according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system. Among them, the local cloud 102 can be a server, and the server can be implemented as an independent server or a server cluster composed of multiple servers.

[0062] In an exemplary embodiment, Figure 2 As shown in the figure, a method for controlling an intelligent excitation system based on local cloud computing is provided. Figure 1 Taking the local cloud 102 in the example as an example, the method includes the following steps S210 to S240. Among them:

[0063] Step S210, detecting the health status of each candidate control cabinet of the intelligent excitation system, and obtaining the health status detection result corresponding to each candidate control cabinet.

[0064] The candidate control cabinets include at least one regulating cabinet, at least one power cabinet and at least one demagnetization cabinet.

[0065] Among them, the regulating cabinet can be the core control device in the intelligent excitation system, which is used to adjust and control the excitation current of the generator. It collects the voltage, current and other parameters of the generator in real time, adjusts the excitation current through the control algorithm, and ensures the stability of the generator output voltage.

[0066] The power cabinet can be a device in the intelligent excitation system used to provide excitation current to the generator rotor winding, usually including a power converter and a current regulator. The power cabinet has an independent control module that can provide the required excitation current according to the instructions of the regulator cabinet, and also has health monitoring, fault diagnosis and redundancy switching functions.

[0067] Among them, the demagnetization cabinet can be a device used to quickly cut off the excitation current of the generator. Its main function is to quickly eliminate the rotor magnetic field in an emergency (such as when the generator fails or the overvoltage protection is activated) to prevent the generator from being damaged or the grid fault from expanding. The demagnetization cabinet can achieve rapid demagnetization through automatic control and has a self-diagnosis function.

[0068] In the specific implementation, the local cloud can perform health checks on the intelligent excitation system to detect the health status of each candidate control cabinet and obtain the health status test results corresponding to each candidate control cabinet.

[0069] Step S220: determining a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets.

[0070] The target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over.

[0071] In a specific implementation, the local cloud can determine the target control cabinet that meets the triggering conditions for taking over the control right among the candidate control cabinets of the intelligent excitation system based on the health status detection results corresponding to each candidate control cabinet.

[0072] In some embodiments, when the local cloud determines that a candidate control cabinet has failed based on the health status detection results corresponding to each candidate control cabinet, it can be determined that the candidate control cabinet meets the triggering conditions for the control right to be taken over.

[0073] In other embodiments, when the candidate control cabinet proactively initiates a request to the local cloud to request the local cloud to take over its own control, that is, when the local cloud receives the request sent by the candidate control cabinet to request the local cloud to take over its own control, for example, when the candidate control cabinet detects that it can no longer control the operation of the intelligent excitation system, it can request the local cloud to take over its own control. Therefore, after receiving the request, the local cloud can determine that the candidate control cabinet meets the triggering conditions for the control to be taken over.

[0074] Step S230, obtaining key data of the target control cabinet.

[0075] Among them, the key data includes key operating parameters and / or instruction execution logic.

[0076] Among them, the key operating parameters of the regulating cabinet may include excitation current related parameters, such as real-time excitation current value, excitation current set value and excitation current regulation rate, etc., and may also include voltage feedback parameters, such as actual value of generator terminal voltage, grid voltage synchronization value, etc. In some embodiments, the key operating parameters of the regulating cabinet may include at least one of the excitation current related parameters and voltage feedback parameters.

[0077] Among them, the key operating parameters of the power cabinet may include power output parameters, such as real-time output power, rated power, power factor, etc.

[0078] Specifically, key operating parameters are direct indicators that reflect the actual working status of each component of the excitation system. By obtaining key operating parameters, the local cloud can accurately detect the operating conditions of the system, just like the candidate control cabinet being taken over, and provide an accurate basis for subsequent control decisions. By obtaining key operating parameters, the local cloud cabinet can obtain the latest system status more quickly, continue the control tasks with the same parameter starting point, maintain the smooth operation of the excitation system, avoid control errors caused by unknown parameters, and ensure the continuity of power supply.

[0079] Among them, obtaining the instruction execution logic allows the local cloud to simulate the decision-making process of the target control cabinet, so that after taking over control, it can quickly and accurately generate and execute control instructions that meet system requirements, whether facing daily power system operation fluctuations or emergency fault conditions, to ensure the reliable operation of the excitation system and even the entire power system.

[0080] In the specific implementation, the local cloud can obtain key data of the target control cabinet.

[0081] Step S240, updating the local cloud according to the key data, so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0082] In the specific implementation, the local cloud can update itself according to key data. The updated local cloud can serve as the twin of the target control cabinet, replacing the target control cabinet to control the operation of the intelligent excitation system.

[0083] Among them, the local cloud adopts at least one of the constant voltage operation mode and the constant current operation mode to control the operation of the intelligent excitation system.

[0084] In some embodiments, the local cloud can perform health checks on the intelligent excitation system to detect the health status of each candidate control cabinet. If a health status indicator of a candidate control cabinet is detected to be faulty, the candidate control cabinet can be determined to meet the triggering conditions for control to be taken over, and the target control cabinet is used as the local cloud to actively take over the control of the target control cabinet. In this way, this control takeover method can be named active takeover.

[0085] In the above-mentioned intelligent excitation system control method based on local cloud computing, it is applied to the local cloud, and the health status of each candidate control cabinet of the intelligent excitation system is detected to obtain the health status detection results corresponding to each candidate control cabinet; the candidate control cabinets include at least a regulating cabinet, a power cabinet and a demagnetization cabinet; according to the health status detection results corresponding to each candidate control cabinet, the target control cabinet is determined among the candidate control cabinets; the key data of the target control cabinet is obtained; the key data includes key operating parameters and / or instruction execution logic; the local cloud is updated according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0086] In this way, when it is determined that there is a target control cabinet among the candidate control cabinets of the intelligent excitation system that meets the trigger conditions for the control right to be taken over according to the health status detection results corresponding to each candidate control cabinet, such as a failure of the candidate control cabinet, the local cloud can obtain the key data of the target control cabinet, including key operating parameters and / or instruction execution logic, and can update itself based on the key data to use the updated local cloud as the twin of the target control cabinet to control the operation of the intelligent excitation system instead of the target control cabinet. When the candidate control cabinet meets the trigger conditions for the control right to be taken over, the control of the candidate control cabinet is taken over by the local cloud, and the various components in the intelligent excitation system are continued to be controlled, thereby avoiding the situation where the intelligent excitation system cannot continue to operate when the candidate control cabinet cannot control the operation of the intelligent excitation system, thereby effectively improving the reliability of the intelligent excitation system.

[0087] In one embodiment, the health status detection result includes a health score, such as Figure 3As shown, step S220, according to the health status detection results corresponding to each candidate control cabinet, determines the target control cabinet from each candidate control cabinet, including steps S2202 to S2204:

[0088] Step S2202: for any candidate control cabinet, obtain a health score threshold corresponding to any candidate control cabinet.

[0089] Step S2204: if the health score corresponding to any candidate control cabinet is lower than the health score threshold corresponding to any candidate control cabinet, it is determined that any candidate control cabinet meets the triggering condition for the control right to be taken over, and any candidate control cabinet is used as the target control cabinet.

[0090] Among them, the health score can be used to evaluate the current operating status, reliability and adaptability of the candidate control cabinet. The higher the health score, the better the status of the candidate control cabinet. The lower the health score, the worse the status of the candidate control cabinet and the more likely it is to fail.

[0091] In the specific implementation, the local cloud can detect the health status of each candidate control cabinet; according to the health status detection results corresponding to each candidate control cabinet, obtain the health score corresponding to each candidate control cabinet; for any candidate control cabinet, obtain the health score threshold corresponding to the any candidate control cabinet, if the health score corresponding to the any candidate control cabinet is lower than the health score threshold corresponding to the any candidate control cabinet, then the any candidate control cabinet may have a fault, and it is determined that the any candidate control cabinet meets the triggering conditions for the control to be taken over, and the any candidate control cabinet is used as the target control cabinet.

[0092] In the technical solution of this embodiment, the health status detection result includes a health score. For any candidate control cabinet, the health score threshold corresponding to any candidate control cabinet is obtained; if the health score corresponding to any candidate control cabinet is lower than the health score threshold corresponding to any candidate control cabinet, it is determined that any candidate control cabinet meets the trigger condition for the control right to be taken over, and any candidate control cabinet is used as the target control cabinet to take over the control right of the target control cabinet, thereby realizing the active takeover of the control right of the target control cabinet with a health score lower than the health score threshold by detecting the health status of the candidate control cabinet, ensuring that when the health status of the candidate control cabinet is not good, the operation of the intelligent excitation system can still be controlled through the local cloud, thereby improving the reliability and fault response capability of the intelligent excitation system.

[0093] In some embodiments, there are multiple intelligent excitation systems, and the local cloud can obtain the first health score threshold and the second health score threshold corresponding to any candidate control cabinet in the process of obtaining the health score threshold corresponding to any candidate control cabinet; wherein the first health score threshold is determined by horizontally comparing the health scores corresponding to the candidate control cabinets corresponding to the any candidate control cabinet in each intelligent excitation system; wherein the second health score threshold is determined by vertically comparing the health score corresponding to any candidate control cabinet with the historical health score corresponding to any candidate control cabinet; in this way, the local cloud can determine the health score threshold corresponding to any candidate control cabinet based on the first health score threshold and / or the second health score threshold, and the health score threshold is used to determine whether any candidate control cabinet meets the triggering conditions for the control to be taken over.

[0094] In some embodiments, the local cloud may use the first health score threshold as the health score threshold corresponding to any candidate control cabinet. In other embodiments, the local cloud may use the second health score threshold as the health score threshold corresponding to any candidate control cabinet.

[0095] In some further embodiments, the local cloud may determine the health score threshold corresponding to any candidate control cabinet based on an average value between the first health score threshold and the second health score threshold.

[0096] In the process of obtaining the first health score threshold and the second health score threshold corresponding to any candidate control cabinet, the local cloud can obtain other health scores; other health scores include health scores corresponding to candidate control cabinets corresponding to any candidate control cabinet in other intelligent excitation systems; other intelligent excitation systems include intelligent excitation systems other than the intelligent excitation system where any candidate control cabinet is located in each intelligent excitation system. In this way, the local cloud can determine the first health score threshold according to the average value between other health scores and the health score corresponding to any candidate control cabinet.

[0097] In order to facilitate the understanding of those skilled in the art, Figure 4 Another application environment diagram of an intelligent excitation system control method based on local cloud computing is provided. Figure 4 As shown in the figure, the local cloud can communicate with three intelligent excitation systems (intelligent excitation system 1, intelligent excitation system 2 and intelligent excitation system 3. The number of intelligent excitation systems can be set according to actual needs and the number is not specifically limited here). The candidate control cabinets of each intelligent excitation system can include two regulating cabinets (regulating cabinet 1 and regulating cabinet 2), three power cabinets (power cabinet 1, power cabinet 2 and power cabinet 3) and one demagnetization cabinet (demagnetization cabinet 1).

[0098] In the process of obtaining the first health score threshold corresponding to any candidate control cabinet, taking the power cabinet 1 in the intelligent excitation system 1 as an example, the local cloud can obtain other health scores; other health scores include the health scores corresponding to the candidate control cabinets in other intelligent excitation systems corresponding to the any candidate control cabinet; other intelligent excitation systems include intelligent excitation systems other than the intelligent excitation system where the any candidate control cabinet is located in each intelligent excitation system. Among them, the candidate control cabinet corresponding to any candidate control cabinet can refer to the candidate control cabinet of the same model as the any candidate control cabinet in other intelligent excitation systems, or the candidate control cabinet with the same function as the any candidate control cabinet in other intelligent excitation systems. In this embodiment, if the any candidate control cabinet is the power cabinet 1 in the intelligent excitation system 1, then the other intelligent excitation systems refer to the intelligent excitation system 2 and the intelligent excitation system 3, and the candidate control cabinets in the intelligent excitation system 2 and the intelligent excitation system 3 corresponding to the power cabinet 1 in the intelligent excitation system 1 can refer to: the power cabinet 1 in the intelligent excitation system 2, and the power cabinet 1 in the intelligent excitation system 3.

[0099] In this way, in this embodiment, when the local cloud determines the first health score threshold based on the average value of other health scores and the health score corresponding to any candidate control cabinet, the local cloud can calculate the current health score corresponding to the power cabinet 1 in the intelligent excitation system 2, the current health score corresponding to the power cabinet 1 in the intelligent excitation system 3, and the average value of the current health score corresponding to the power cabinet 1 in the intelligent excitation system 1 as the first health score threshold corresponding to the power cabinet 1 in the intelligent excitation system 1.

[0100] In some embodiments, the local cloud obtains the first health score threshold and the second health score threshold corresponding to any candidate control cabinet, including: obtaining a historical health score; wherein the historical health score includes various health scores obtained by detecting any candidate control cabinet in a historical time period; the local cloud can determine the second health score threshold based on the average value between the historical health score and the health score corresponding to any candidate control cabinet.

[0101] For example, continuing with the previous example, in the process of calculating the second health score threshold corresponding to the power cabinet 1 in the intelligent excitation system 1, the local cloud can obtain the historical health score corresponding to the power cabinet 1 in the intelligent excitation system 1, such as obtaining the three historical health scores of the power cabinet 1 in the intelligent excitation system 1 detected in the past three years (historical time period). In this way, the local cloud can calculate the average value of the three historical health scores corresponding to the power cabinet 1 in the intelligent excitation system 1 and the current corresponding health score as the second health score threshold corresponding to the power cabinet 1 in the intelligent excitation system 1.

[0102] In some further embodiments, in the process of determining the health score threshold corresponding to any candidate control cabinet based on the average value between the first health score threshold and the second health score threshold, the local cloud can obtain first weight information corresponding to the first health score threshold and second weight information corresponding to the second health score threshold; in this way, the weighted average value between the first health score threshold and the second health score threshold can be determined based on the first weight information and the second weight information as the health score threshold corresponding to any candidate control cabinet.

[0103] The technical solution of this embodiment is to obtain the first health score threshold and the second health score threshold corresponding to any candidate control cabinet; the first health score threshold is determined by comparing the health scores of the candidate control cabinets corresponding to any candidate control cabinet in each intelligent excitation system horizontally; the second health score threshold is determined by comparing the health score corresponding to any candidate control cabinet with the historical health score corresponding to any candidate control cabinet vertically; the health score threshold corresponding to any candidate control cabinet is determined according to the average value between the first health score threshold and the second health score threshold. In this way, through horizontal comparison, the relative health status of any candidate control cabinet in multiple intelligent excitation systems can be more accurately determined; through vertical comparison, the historical health score of any candidate control cabinet itself can be considered, and the vertical and horizontal comparisons can be combined to take the average of the first health score threshold and the second health score threshold as the health score threshold, which can comprehensively consider the situation inside the system and its own time series, so as to more accurately judge whether the current health status of any candidate control cabinet is within a reasonable range. At the same time, due to the comprehensive comparison results of two different dimensions, the determination of the health score threshold is not based on a single standard. This multidimensional assessment method can reduce misjudgments caused by the limitations of a single comparison method and enhance the reliability of determining health score thresholds.

[0104] In one embodiment, the health status of each candidate control cabinet of the intelligent excitation system is detected to obtain the health status detection result corresponding to each candidate control cabinet, including: obtaining the operation status log of the intelligent excitation system; the operation status log includes the operation status data of any candidate control cabinet; obtaining the data operation rules corresponding to each health indicator, and performing operation processing on the operation status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet; the health indicator is an indicator for evaluating the health status of any candidate control cabinet; according to the health indicator data of any candidate control cabinet, the health status detection result corresponding to any candidate control cabinet is obtained.

[0105] The operation status log records various events and operation records generated during the operation of the intelligent excitation system, and may include the operation status data of the intelligent excitation system.

[0106] The operating status data may refer to data associated with the operating status of the intelligent excitation system. For example, the operating status data may include temperature data, voltage and current data, and the like.

[0107] The operation status data recorded in the operation log data can be obtained through sensor monitoring. For example, an industrial-grade sensor is used to monitor the operation status of the intelligent excitation system and the generator in which it is located to obtain the operation status data.

[0108] Among them, the health indicator may include at least one indicator among conventional failure rate, accelerated failure rate, conventional mean time between failures, accelerated mean time between failures, and other indicators.

[0109] Among them, other indicators may include at least one indicator such as temperature, load, and operating time.

[0110] Among them, health indicators can be selected according to actual needs.

[0111] The health indicator data may be indicator data obtained by monitoring the health status of the intelligent excitation system using health indicators.

[0112] In the specific implementation, when the local cloud detects the health status of each candidate control cabinet and obtains the health status detection result corresponding to each candidate control cabinet, the local cloud can obtain the operation status log of the intelligent excitation system. The operation status log includes the operation status data of any candidate control cabinet. The local cloud can obtain the data operation rules corresponding to each health indicator, and according to the data operation rules corresponding to each health indicator, the operation status data of any candidate control cabinet is operated and processed to obtain the health indicator data of any candidate control cabinet; according to the health indicator data of any candidate control cabinet, the health status detection result corresponding to any candidate control cabinet is obtained. In this way, based on the same method, the health status detection result corresponding to each candidate control cabinet can be obtained.

[0113] The technical solution of this embodiment is to obtain the operation status log of the intelligent excitation system; the operation status log includes the operation status data of any candidate control cabinet; obtain the data operation rules corresponding to each health indicator, and perform operation processing on the operation status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet; the health indicator is an indicator used to evaluate the health status of any candidate control cabinet; according to the health indicator data of any candidate control cabinet, obtain the health status detection result corresponding to any candidate control cabinet. In this way, by obtaining the operation status log of the intelligent excitation system, the operation status log includes the operation status data of any candidate control cabinet, and by using the data operation rules corresponding to each health indicator, the operation status data of any candidate control cabinet is operated and processed according to the data operation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet, a more accurate detection of the health status of the candidate control cabinet is achieved, and the accuracy and reliability of the health status detection of the intelligent excitation system are effectively improved.

[0114] Furthermore, the operation log data may include operation status data to be processed; the local cloud can eliminate the operation status data that does not meet the preset conditions from the operation status data to be processed to obtain the eliminated operation status data, and then perform calculations on the eliminated operation status data according to the data calculation rules corresponding to each health indicator to obtain health indicator data.

[0115] Among them, the operating status data that does not meet the preset conditions may refer to at least one of data with data collection errors, data with data format errors, and data that is not logically consistent. For example, data collection errors may refer to the fact that the collected data does not conform to the actual situation due to sensor failure, communication interruption, or data transmission errors; data format errors may refer to the format of the data not conforming to the predefined rules; and illogicality may refer to that from the logical perspective of system operation, some data may not conform to the logic of normal operation of the intelligent excitation system. For example, under normal circumstances, the excitation current increases appropriately with the increase of the generator load, but the operation log shows that the excitation current suddenly drops to zero when the generator load increases. This is different from the logic of the normal operation of the system and is likely caused by data recording errors or abnormal conditions in the system. Such illogical data will be identified as erroneous data.

[0116] In other embodiments, in the process of obtaining the health status detection result corresponding to any candidate control cabinet based on the health indicator data of the candidate control cabinet, the local cloud can input the health indicator data of the candidate control cabinet into a pre-trained health status assessment model, and output the health status detection result of the candidate control cabinet under the excitation system health assessment system; the health status detection result is determined by the pre-trained health status assessment model based on the weight information corresponding to each health indicator and the health indicator data corresponding to each health indicator.

[0117] Among them, the pre-trained health assessment model can be an artificial intelligence model.

[0118] The excitation system health assessment system may refer to an indicator system for evaluating the health status of the intelligent excitation system, and the indicator system may include various health indicators at different levels.

[0119] In the specific implementation, the local cloud can input the health indicator data of any candidate control cabinet into the pre-trained health status assessment model. The pre-trained health status assessment model can detect the health status of any candidate control cabinet according to the weight information corresponding to each health indicator and the health indicator data corresponding to each health indicator, and output the health status detection result of any candidate control cabinet under the excitation system health assessment system.

[0120] In one embodiment, the method also includes: obtaining health indicators set for the intelligent excitation system; combining the status of the intelligent excitation system from the whole to the part of the intelligent excitation system in the form of hierarchical components according to each health indicator, and constructing an excitation system health assessment system; the excitation system health assessment system includes health indicators with corresponding hierarchical structures.

[0121] In the specific implementation, for the construction of the excitation system health assessment system, the local cloud can obtain the health indicators set for the intelligent excitation system, and according to each health indicator, the status of the intelligent excitation system is combined from the overall to the local of the intelligent excitation system in the form of hierarchical components to construct the excitation system health assessment system, so that the excitation system health assessment system includes various health indicators with corresponding hierarchical structures.

[0122] In practical applications, in the process of building an excitation system health assessment system, the status of the excitation system is organically combined from the overall to the local intelligent excitation system in the form of hierarchical components, and the health status of the intelligent excitation system is evaluated using the Analytic Hierarchy Process (AHP) and scoring rules are specified.

[0123] Specifically include:

[0124] Step 1: Determine the evaluation objectives and standards; Evaluation objectives: Evaluate the health status of the intelligent excitation system. Evaluation standards: Determine the main health indicators that affect the health status. These health indicators may include: conventional failure rate, accelerated failure rate, conventional mean time between failures, accelerated mean time between failures, and at least one of other indicators (such as temperature, load, operating time, etc., depending on the specific excitation system).

[0125] Step 2: Construct a hierarchical structure; construct a hierarchical structure from the whole to the part of the excitation system in the form of graded components; determine the hierarchy of each health indicator.

[0126] In some embodiments, the excitation system may include an excitation transformer and an excitation cabinet; an excitation transformer, a dry-type transformer; and wires between the excitation transformer and the excitation cabinet.

[0127] In other embodiments, the excitation system may include an excitation transformer (which includes a high-voltage coil, a low-voltage coil, an iron core, a temperature measurement, a voltage transformer, and a current transformer of a dry-type transformer), an AC conductor (a cable or a cast busbar), an excitation disk cabinet (an AC incoming circuit breaker inside the AC incoming cabinet (including an operating mechanism, contacts, a closing coil, an opening coil, an energy storage motor, and a heater), three power cabinets (including a fan, a thyristor, a fast fuse, a capacitor, a resistor, a control board, a contactor, a temperature probe, a pulse circuit, a copper bus, a heater, and a human-machine interface), a demagnetization switch cabinet (including a DC demagnetization switch, a copper bus, a transmitter, a shunt, a Hall sensor, a heater, a human-machine interface, and a DC output cable), a demagnetization resistor cabinet (including a demagnetization resistor, a jumper control board, a heater, and a human-machine interface), and a regulating cabinet (a core control board, a relay, a human-machine interface, an air switch, and a contactor)).

[0128] Thus, in some embodiments, a hierarchical structure is constructed from the whole to the part of the excitation system in the form of graded components; the hierarchical structure in which each health indicator is located is determined, and the health indicator data obtained by monitoring the health indicators of the intelligent excitation system can be used to detect faulty components in the intelligent excitation system.

[0129] Step 3: Perform paired comparisons: Use paired comparison method to make pairwise judgments on the importance of each health indicator for health status monitoring and obtain an indicator comparison matrix.

[0130] Step 4: Calculate the weight vector: Determine the weight information corresponding to each health indicator in the indicator comparison matrix through the eigenvalue method or the arithmetic mean method.

[0131] In some embodiments, the health indicator data of any candidate control cabinet is input into a pre-trained health status assessment model, and the health status detection result of any candidate control cabinet under the excitation system health assessment system is output, including: according to the scoring rules corresponding to each health indicator, the corresponding health indicator data is converted into a scoring value; the product result between the weight information corresponding to each health indicator and the corresponding scoring value is obtained; the sum of each product result is obtained, and a health status score and / or health status level is generated according to the sum of each product result; and the health status detection result is output according to the health status score and / or health status level.

[0132] In the specific implementation, the pre-trained health status assessment model can convert the corresponding health indicator data into a scoring value according to the scoring rules corresponding to each health indicator; obtain the product result between the weight information corresponding to each health indicator and the corresponding scoring value, add up the multiplication results to obtain the sum of the multiplication results, and generate a health status score and / or health status level based on the sum of the multiplication results; output the health status detection result based on the health status score and / or health status level.

[0133] In practical applications, a health status score (health score) can be generated according to the sum of the product results, and then the corresponding health status level can be determined according to the health status score.

[0134] The technical solution of this embodiment converts the corresponding health indicator data into a scoring value according to the scoring rules corresponding to each health indicator; obtains the product result between the weight information corresponding to each health indicator and the corresponding scoring value; obtains the sum of each product result, and generates a health status score and / or a health status level according to the sum of each product result; and outputs the health status detection result according to the health status score and / or the health status level.

[0135] In this way, by introducing the weight information corresponding to each health indicator, the relative importance of different indicators to the overall health status of the excitation system can be fully reflected. The health status score and grade can be generated by multiplying the score value obtained by converting the health indicator data corresponding to each health indicator with the corresponding weight. The complex system operating conditions are converted into intuitive and easy-to-understand numerical values ​​and categories, allowing operation and maintenance personnel to quickly and accurately grasp the overall health of the excitation system, effectively improving the intelligence of the health assessment of the excitation system.

[0136] In some embodiments, for the construction of a pre-trained health status assessment model, a data-driven health status assessment model for the intelligent excitation system can be established based on existing excitation fault diagnosis data and expert library data. The health status assessment model is an artificial intelligence model. In practical applications, the health status assessment model can be trained using training sample data, and the model parameters of the health status assessment model can be dynamically updated by comparing the health status detection results of the health status assessment model with the actual experimental data of the intelligent excitation system to optimize the health status assessment model and obtain a pre-trained health status assessment model.

[0137] In this way, relying on the existing excitation fault diagnosis data, these data carry a large number of fault cases, fault characteristics and corresponding solutions that have occurred in actual operation in the past, providing the model with highly authentic and targeted learning materials; and the introduction of expert database data further strengthens the professionalism of the model. The model is continuously trained using training sample data, so that the model can be flexibly adjusted with the dynamic changes of the excitation system operating environment, working conditions and the equipment's own status, and the health status detection results are compared with the actual experimental data, providing a direct basis for the dynamic update of the model. This real-time feedback and optimization mechanism ensures that the model will not deviate from the actual situation and continuously improves the reliability of the prediction.

[0138] In one embodiment, the method also includes: receiving requests sent by each candidate control cabinet; when a target request is detected, determining that the candidate control cabinet sending the target request meets the trigger condition for control to be taken over; the target request is used to instruct the local cloud to take over control; and using the candidate control cabinet sending the target request as the target control cabinet.

[0139] In the specific implementation, each candidate control cabinet can communicate with the local cloud, each candidate control cabinet can send a request to the local cloud, and the local cloud can receive the request sent by each candidate control cabinet. When it is detected that the received request is a target request for instructing the local cloud to take over its control, it is determined that the candidate control cabinet sending the target request meets the trigger condition for the control to be taken over, and the candidate control cabinet sending the target request is used as the target control cabinet.

[0140] In actual applications, in the following scenarios, the candidate control cabinet can send a target request to instruct the local cloud to take over control:

[0141] 1. Self-fault warning:

[0142] When the candidate control cabinet detects that its hardware has potential faults, such as the temperature of the key chip is too high, memory read and write errors occur frequently, or there are abnormalities at the software level, such as the program is stuck in an infinite loop, the number of key process crashes and restarts exceeds the threshold, etc., in order to ensure the continued normal operation of the excitation system, it will actively request the local cloud to take over control.

[0143] 2. Network communication abnormality:

[0144] If the network connection between the candidate control cabinet and external devices (such as the monitoring sensor of the generator, the host computer, etc.) is frequently interrupted or the packet loss rate is too high (for example, the packet loss rate exceeds 20% within 5 consecutive minutes), it will be impossible to obtain accurate operating status data in time and it will be difficult to accurately control the excitation system. To ensure the stability of the system, it will choose to let the local cloud take over control and maintain effective information exchange between the excitation system and the outside world.

[0145] 3. Upgrade and maintenance requirements:

[0146] When the candidate control cabinet needs to be upgraded, parameters updated or other maintenance operations, in order not to affect the normal operation of the excitation system, it can send a takeover request to the local cloud and complete its own upgrade and transformation during the local cloud takeover period.

[0147] In this way, the candidate control cabinet can send a target request (takeover request) to the local cloud, requesting the local cloud to take over the control right. Thus, after receiving the target request, the local cloud can take over the control right of the candidate control cabinet. This control right takeover method can be named passive takeover.

[0148] In one embodiment, a smart excitation system based on local cloud computing is provided, and the system includes: a candidate control cabinet and a local cloud; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetization cabinet; the local cloud is used to detect the health status of each candidate control cabinet of the smart excitation system and obtain the health status detection result corresponding to each candidate control cabinet; the local cloud is used to determine the target control cabinet among the candidate control cabinets according to the health status detection result corresponding to each candidate control cabinet; the target control cabinet is the candidate control cabinet that meets the trigger condition for the control right to be taken over; the local cloud is used to obtain key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; the local cloud is used to update the local cloud according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the smart excitation system.

[0149] like Figure 4As shown in the figure, an intelligent excitation system may include: candidate control cabinets and a local cloud; the candidate control cabinets may include 2 regulating cabinets, 3 power cabinets, and 1 demagnetization cabinet. Among them, the 2 regulating cabinets include a main regulating cabinet and a standby regulating cabinet. When the main regulating cabinet fails, the control of the main regulating cabinet is transferred to the standby regulating cabinet. When the health status of the standby regulating cabinet indicates that it has failed, or the standby regulating cabinet sends a target request to the local cloud, the local cloud takes over the control of the standby regulating cabinet.

[0150] Furthermore, the regulating cabinet, power cabinet, demagnetization cabinet and local cloud may not be in the same computer room. The regulating cabinet, power cabinet, demagnetization cabinet and local cloud (can also be named cloud service host, local cloud cabinet (twin); the local cloud communicates with the regulating cabinet, power cabinet and demagnetization cabinet through optical fiber, and uses FPGA (Field Programmable Gate Array) module to process optical signals.

[0151] The local cloud can take over control of the candidate control cabinet in two ways: active takeover and passive takeover.

[0152] Active takeover: The local cloud can detect the health status of candidate control cabinets in the intelligent excitation system, and actively take over the control of a candidate control cabinet when a candidate control cabinet sends a fault signal.

[0153] Passive takeover: The candidate control cabinet requests the local cloud to take over its control.

[0154] Among them, the controllers in the regulating cabinet, power cabinet and demagnetization cabinet are all equipped with a control interface for local cloud control, which can be an optical fiber communication module, a wireless network module (spare); the regulating cabinet, power cabinet and demagnetization cabinet can receive the control instructions sent by the local cloud through the local area network through the control interface. In the process of the local cloud replacing the candidate control cabinet to control the operation of the intelligent excitation system, the local cloud can control the various components in the intelligent excitation system. When the local cloud takes over the control of the excitation system, it can be operated in at least one of the constant voltage operation mode and the constant current operation mode; the excitation system can continue to receive "increase magnetization", "demagnetization" and "shutdown" commands; specifically, the "increase magnetization", "demagnetization" and "shutdown" commands are received through the local cloud, and then, in response to the above commands, the local cloud sends corresponding control instructions to each component in the excitation system respectively, so that the excitation system completes the "increase magnetization", "demagnetization" and "shutdown" commands. In actual applications, the wireless network module can adopt the 802.11be (wifi7) protocol. WiFi7 has high throughput, low latency, anti-interference, and wide coverage, and can reduce the latency to 1-10 milliseconds (ms).

[0155] In this way, when the candidate control cabinet meets the triggering conditions for control to be taken over, the control of the candidate control cabinet is taken over through the local cloud, and the components in the intelligent excitation system continue to be controlled, avoiding the situation where the intelligent excitation system cannot continue to operate when the candidate control cabinet cannot control the operation of the intelligent excitation system, thereby effectively improving the reliability of the intelligent excitation system.

[0156] In another embodiment, Figure 5 As shown, a method for controlling an intelligent excitation system based on local cloud computing is provided, and the method is described by taking the application of the method to the local cloud as an example, including steps S200 to S240:

[0157] Step S200: receiving a request sent by each candidate control cabinet.

[0158] Step S202, when a target request is detected, it is determined that the candidate control cabinet sending the target request meets the triggering condition for the control right to be taken over; the target request is used to instruct the local cloud to take over the control right.

[0159] Step S204: The candidate control cabinet that sends the target request is used as the target control cabinet.

[0160] Step S201: for any candidate control cabinet, obtain a first health score threshold and a second health score threshold corresponding to any candidate control cabinet.

[0161] Among them, step S201, for any candidate control cabinet, obtains the first health score threshold and the second health score threshold corresponding to any candidate control cabinet, including the following steps S2011 to S2013, and steps S2012 to S2014:

[0162] Step S2011, obtaining other health scores; other health scores include health scores corresponding to candidate control cabinets corresponding to any candidate control cabinet in other intelligent excitation systems; other intelligent excitation systems include intelligent excitation systems in each intelligent excitation system except the intelligent excitation system where any candidate control cabinet is located.

[0163] Step S2013: Determine a first health score threshold value according to an average value between other health scores and a health score corresponding to any candidate control cabinet.

[0164] Step S2012, obtaining a historical health score; the historical health score includes each health score obtained by detecting any candidate control cabinet in a historical time period.

[0165] Step S2014: Determine a second health score threshold value according to an average value between the historical health score and the health score corresponding to any candidate control cabinet.

[0166] Step S203, determining the health score threshold corresponding to any candidate control cabinet according to the average value between the first health score threshold and the second health score threshold; the health score threshold is used to determine whether any candidate control cabinet meets the triggering condition for control to be taken over.

[0167] Wherein, step S203, determining the health score threshold corresponding to any candidate control cabinet according to the average value between the first health score threshold and the second health score threshold, includes steps S2031 to S2033:

[0168] Step S2031: Obtain first weight information corresponding to the first health score threshold, and obtain second weight information corresponding to the second health score threshold.

[0169] Step S2033: Determine a weighted average value between the first health score threshold and the second health score threshold according to the first weight information and the second weight information, as the health score threshold corresponding to any candidate control cabinet.

[0170] Step S210, detecting the health status of each candidate control cabinet of the intelligent excitation system, and obtaining the health status detection result corresponding to each candidate control cabinet.

[0171] Among them, step S210, detecting the health status of each candidate control cabinet of the intelligent excitation system, and obtaining the health status detection result corresponding to each candidate control cabinet, includes steps S2102 to S2106:

[0172] Step S2102, obtaining the operation status log of the intelligent excitation system; the operation status log includes the operation status data of any candidate control cabinet.

[0173] Step S2104, obtaining the data operation rules corresponding to each health indicator, and performing operation processing on the operating status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet.

[0174] Step S2106: Obtain a health status detection result corresponding to any candidate control cabinet according to the health indicator data of any candidate control cabinet.

[0175] Step S220: determining a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets.

[0176] Step S220, determining a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets, includes steps S2202 to S2204:

[0177] Step S2202: for any candidate control cabinet, obtain a health score threshold corresponding to any candidate control cabinet.

[0178] Step S2204: if the health score corresponding to any candidate control cabinet is lower than the health score threshold corresponding to any candidate control cabinet, it is determined that any candidate control cabinet meets the triggering condition for the control right to be taken over, and any candidate control cabinet is used as the target control cabinet.

[0179] Step S230, acquiring key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic.

[0180] Step S240, updating the local cloud according to the key data, so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0181] It should be noted that the specific limitations of the above steps can refer to the specific limitations of an intelligent excitation system control method based on local cloud computing mentioned above.

[0182] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0183] Based on the same inventive concept, the embodiment of the present application also provides a local cloud computing-based intelligent excitation system control device for implementing the above-mentioned local cloud computing-based intelligent excitation system control method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the local cloud computing-based intelligent excitation system control device provided below can refer to the above-mentioned limitations on the local cloud computing-based intelligent excitation system control method, which will not be repeated here.

[0184] In an exemplary embodiment, Figure 6 As shown, a local cloud computing-based intelligent excitation system control device is provided, which is applied to the local cloud and includes: a detection module 610, a determination module 620, an acquisition module 630 and an update module 640, wherein:

[0185] The detection module 610 is used to detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection result corresponding to each candidate control cabinet; the candidate control cabinets at least include a regulating cabinet, a power cabinet and a demagnetization cabinet.

[0186] The determination module 620 is used to determine a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over.

[0187] The acquisition module 630 is used to acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic.

[0188] The update module 640 is used to update the local cloud according to the key data so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

[0189] In one of the embodiments, the health status detection result includes a health score, and the determination module 620 is specifically used to obtain, for any candidate control cabinet, a health score threshold corresponding to the any candidate control cabinet; if the health score corresponding to the any candidate control cabinet is lower than the health score threshold corresponding to the any candidate control cabinet, it is determined that the any candidate control cabinet meets the trigger condition for the control to be taken over, and the any candidate control cabinet is used as the target control cabinet.

[0190] In one of the embodiments, there are multiple intelligent excitation systems, and the determination module 620 is specifically used to obtain a first health score threshold and a second health score threshold corresponding to any candidate control cabinet; the first health score threshold is determined by horizontally comparing the health scores of the candidate control cabinets corresponding to the any candidate control cabinets in each of the intelligent excitation systems; the second health score threshold is determined by vertically comparing the health score corresponding to any candidate control cabinet with the historical health score corresponding to any candidate control cabinet; the health score threshold corresponding to any candidate control cabinet is determined according to the average value between the first health score threshold and the second health score threshold.

[0191] In one embodiment, the determination module 620 is specifically used to obtain other health scores; the other health scores include health scores corresponding to candidate control cabinets in other intelligent excitation systems corresponding to any candidate control cabinet; the other intelligent excitation systems include intelligent excitation systems in each of the intelligent excitation systems except the intelligent excitation system where any candidate control cabinet is located; the first health score threshold is determined based on the average value between the other health scores and the health scores corresponding to any candidate control cabinet.

[0192] In one embodiment, the determination module 620 is specifically used to obtain a historical health score; the historical health score includes each health score obtained by detecting any candidate control cabinet in a historical time period; and the second health score threshold is determined based on the average value between the historical health score and the health score corresponding to any candidate control cabinet.

[0193] In one embodiment, the determination module 620 is specifically used to obtain first weight information corresponding to the first health score threshold, and obtain second weight information corresponding to the second health score threshold; based on the first weight information and the second weight information, determine the weighted average between the first health score threshold and the second health score threshold as the health score threshold corresponding to any candidate control cabinet.

[0194] In one embodiment, the detection module 610 is specifically used to obtain the operation status log of the intelligent excitation system; the operation status log includes the operation status data of any candidate control cabinet; obtain the data operation rules corresponding to each health indicator, and perform operation processing on the operation status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet; the health indicator is an indicator for evaluating the health status of any candidate control cabinet; according to the health indicator data of any candidate control cabinet, obtain the health status detection result corresponding to any candidate control cabinet.

[0195] In one of the embodiments, the determination module 620 is also used to receive requests sent by each of the candidate control cabinets; when a target request is detected, it is determined that the candidate control cabinet sending the target request meets the trigger condition for control to be taken over; the target request is used to instruct the local cloud to take over control; and the candidate control cabinet sending the target request is used as the target control cabinet.

[0196] In one of the embodiments, the local cloud uses at least one of a constant voltage operation mode and a constant current operation mode to control the operation of the intelligent excitation system.

[0197] Each module in the above-mentioned local cloud computing-based intelligent excitation system control device can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0198] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store key data of the target control cabinet. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a control method of an intelligent excitation system based on local cloud computing is implemented.

[0199] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0200] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0201] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0202] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0203] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0204] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0205] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0206] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling an intelligent excitation system based on local cloud computing, characterized in that: Applied to a local cloud, the method includes: Detect the health status of each candidate control cabinet of the intelligent excitation system to obtain the health status detection result corresponding to each candidate control cabinet; the candidate control cabinets at least include a regulating cabinet, a power cabinet and a demagnetization cabinet; According to the health status detection results corresponding to each of the candidate control cabinets, a target control cabinet is determined from among the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over; Acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; The local cloud is updated according to the key data to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

2. The method according to claim 1, characterized in that There are multiple intelligent excitation systems, and the method further includes: For any candidate control cabinet, obtaining a first health score threshold and a second health score threshold corresponding to the any candidate control cabinet; The first health score threshold is determined by horizontally comparing the health scores of the candidate control cabinets corresponding to any candidate control cabinet in each of the intelligent excitation systems; the second health score threshold is determined by vertically comparing the health score corresponding to any candidate control cabinet with the historical health score corresponding to any candidate control cabinet; The health score threshold corresponding to any candidate control cabinet is determined according to the average value between the first health score threshold and the second health score threshold; the health score threshold is used to determine whether any candidate control cabinet meets the trigger condition for control to be taken over.

3. The method according to claim 2, characterized in that The step of obtaining the first health score threshold and the second health score threshold corresponding to any candidate control cabinet includes: Obtaining other health scores; the other health scores include health scores corresponding to candidate control cabinets in other intelligent excitation systems corresponding to any candidate control cabinet; the other intelligent excitation systems include intelligent excitation systems in each of the intelligent excitation systems except the intelligent excitation system where any candidate control cabinet is located; The first health score threshold is determined according to an average value between the other health scores and the health score corresponding to any one of the candidate control cabinets.

4. The method according to claim 2, characterized in that: The step of obtaining the first health score threshold and the second health score threshold corresponding to any candidate control cabinet includes: Obtaining a historical health score; the historical health score includes each health score obtained by detecting any candidate control cabinet in a historical time period; The second health score threshold is determined according to an average value between the historical health score and the health score corresponding to any candidate control cabinet.

5. The method according to claim 2, characterized in that: The determining, according to the average value between the first health score threshold and the second health score threshold, the health score threshold corresponding to any candidate control cabinet includes: Obtaining first weight information corresponding to the first health score threshold, and obtaining second weight information corresponding to the second health score threshold; According to the first weight information and the second weight information, a weighted average value between the first health score threshold and the second health score threshold is determined as the health score threshold corresponding to any candidate control cabinet.

6. The method according to claim 1, characterized in that The health status detection result includes a health score, and determining a target control cabinet from each of the candidate control cabinets according to the health status detection result corresponding to each of the candidate control cabinets includes: For any candidate control cabinet, obtain a health score threshold corresponding to the any candidate control cabinet; If the health score corresponding to any candidate control cabinet is lower than the health score threshold corresponding to any candidate control cabinet, it is determined that any candidate control cabinet meets the trigger condition for control right to be taken over, and any candidate control cabinet is used as the target control cabinet.

7. The method according to claim 1, characterized in that The detecting the health status of each candidate control cabinet of the intelligent excitation system to obtain the health status detection result corresponding to each candidate control cabinet includes: Obtaining an operation status log of the intelligent excitation system; the operation status log includes operation status data of any candidate control cabinet; Obtaining data operation rules corresponding to each health indicator, and performing operation processing on the operation status data of any candidate control cabinet according to the data operation rules corresponding to each health indicator to obtain health indicator data of any candidate control cabinet; the health indicator is an indicator used to evaluate the health status of any candidate control cabinet; According to the health indicator data of any candidate control cabinet, a health status detection result corresponding to any candidate control cabinet is obtained.

8. The method according to claim 1, characterized in that The method further comprises: Receiving requests sent by each of the candidate control cabinets; In the case of detecting a target request, determining that the candidate control cabinet sending the target request meets the trigger condition for control to be taken over; the target request is used to instruct the local cloud to take over control; The candidate control cabinet that sends the target request is used as the target control cabinet.

9. An intelligent excitation system based on local cloud computing, characterized in that: The system includes: a candidate control cabinet and a local cloud; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetization cabinet; The local cloud is used to detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection result corresponding to each candidate control cabinet; The local cloud is used to determine a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over; The local cloud is used to obtain key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; The local cloud is used to update the local cloud according to the key data, so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

10. An intelligent excitation system control device based on local cloud computing, characterized in that: Applied to the local cloud, the device includes: A detection module is used to detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection result corresponding to each candidate control cabinet; the candidate control cabinets at least include a regulating cabinet, a power cabinet and a demagnetization cabinet; A determination module, used to determine a target control cabinet from among the candidate control cabinets according to the health status detection results corresponding to the candidate control cabinets; the target control cabinet is a candidate control cabinet that meets the triggering condition for the control right to be taken over; An acquisition module, used to acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; An update module is used to update the local cloud according to the key data so as to use the updated local cloud as the twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet to control the operation of the intelligent excitation system.

11. 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 steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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