Local cloud computing-based intelligent excitation system control method, intelligent excitation system, apparatus, and device

Abstract

The present application relates to a local cloud computing-based intelligent excitation system control method, an intelligent excitation system, an apparatus, and a device, which are applied to a local cloud. The method comprises: detecting a health status of each candidate control cabinet of an intelligent excitation system to obtain a health status detection result corresponding to each candidate control cabinet, wherein the candidate control cabinets at least include a regulating cabinet, a power cabinet, and a de-excitation cabinet; on the basis of the health status detection result corresponding to each candidate control cabinet, determining a target control cabinet from among the candidate control cabinets, wherein the target control cabinet is a candidate control cabinet that meets a trigger condition for takeover of control authority; acquiring key data of the target control cabinet, wherein the key data comprises a key operating parameter and / or instruction execution logic; and updating a local cloud on the basis of the key data, so as to use the updated local cloud as a twin of the target control cabinet, wherein the twin of the target control cabinet is used for replacing the target control cabinet to control the operation of the intelligent excitation system. The use of the method can improve the reliability of the intelligent excitation system.

Description

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

[0001] This application relates to the field of power technology, and in particular to a control method, intelligent excitation system, device, computer equipment, computer-readable storage medium, and computer program product for an intelligent excitation system based on local cloud computing. Background Technology

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

[0003] However, in related technologies, if the control channel of the excitation regulating cabinet malfunctions, it will affect the precise control of the excitation current of the synchronous generator, making it impossible for the excitation current to be adjusted according to the predetermined requirements. Ultimately, this will reduce the reliability of the intelligent excitation system and fail to provide a strong guarantee for the safe, stable and efficient operation of the power system.

[0004] Therefore, the intelligent excitation system suffers from low reliability in related technologies. Summary of the Invention

[0005] Therefore, it is necessary to provide a local cloud computing-based intelligent excitation system control method, intelligent excitation system, device, computer equipment, computer-readable storage medium, and computer program product that can improve the reliability of intelligent excitation systems and address the aforementioned technical problems.

[0006] Firstly, this application provides a control method for an intelligent excitation system based on local cloud computing, applied locally in the cloud, including:

[0007] The health status of each candidate control cabinet of the intelligent excitation system is detected, and the health status detection result of each candidate control cabinet is obtained; the candidate control cabinet includes at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet.

[0008] Based on the health status detection results of each candidate control cabinet, 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 control to be taken over, and the candidate control cabinet includes 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 based on the key data, so that the updated local cloud serves as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

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

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

[0013] 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 of the intelligent excitation systems horizontally; the second health score threshold is determined by comparing the health score of any candidate control cabinet with the historical health score of any candidate control cabinet vertically.

[0014] The health score threshold corresponding to any candidate control cabinet is determined based on 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.

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

[0016] Obtain other health scores; the other health scores include the health scores of candidate control cabinets in other intelligent excitation systems that correspond to any of the candidate control cabinets; the other intelligent excitation systems include intelligent excitation systems other than the intelligent excitation system where any of the candidate control cabinets is located;

[0017] 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.

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

[0019] Obtain historical health scores; the historical health scores include health scores obtained by detecting any candidate control cabinet within a historical time period.

[0020] The second health score threshold is determined based on the average of the historical health scores and the health scores corresponding to any candidate control cabinet.

[0021] In one embodiment, 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 includes:

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

[0023] Based on 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 the step of determining the target control cabinet among the candidate control cabinets based on the health status detection result corresponding to each candidate control cabinet includes:

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

[0026] If the health score of any candidate control cabinet is lower than the health score threshold of any candidate control cabinet, then the candidate control cabinet is determined to meet the triggering condition for control to be taken over, and the candidate control cabinet is selected as the target control cabinet.

[0027] In one embodiment, 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:

[0028] Obtain the operating status log of the intelligent excitation system; the operating status log includes the operating status data of any candidate control cabinet;

[0029] Obtain the data calculation rules corresponding to each health indicator, and process the operating status data of any candidate control cabinet according to the data calculation 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.

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

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

[0032] Receive requests from each of the candidate control cabinets;

[0033] Upon detecting a target request, it is determined that the candidate control cabinet that sent the target request meets the triggering 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 will be designated as the target control cabinet.

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

[0036] Secondly, this application also provides an intelligent excitation system based on local cloud computing, including: a candidate control cabinet and a local cloud; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetizing 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 of each candidate control cabinet.

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

[0039] The local cloud is used to acquire 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 itself based on the key data, so that the updated local cloud serves as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

[0041] Thirdly, this application also provides an intelligent excitation system control device based on local cloud computing, applied in a local cloud environment, comprising:

[0042] The 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 of each candidate control cabinet; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetizing cabinet.

[0043] The determination module is used to determine the target control cabinet among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet; the target control cabinet is a candidate control cabinet that meets the triggering condition for control to be taken over.

[0044] The acquisition module is 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 based on the key data, so as to use the updated local cloud as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

[0046] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method described above.

[0047] Fifthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0048] Sixthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0049] The aforementioned 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. By detecting the health status of each candidate control cabinet of the intelligent excitation system, the health status detection results corresponding to each candidate control cabinet are obtained. Each candidate control cabinet includes at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet. Based on the health status detection results corresponding to each candidate control cabinet, a target control cabinet is determined from among the candidate control cabinets. Key data of the target control cabinet is acquired. Key data includes key operating parameters and / or instruction execution logic. The local cloud is updated based on the key data, so that the updated local cloud serves as a twin of the target control cabinet. The twin of the target control cabinet is used to replace the target control cabinet in controlling the operation of the intelligent excitation system.

[0050] Thus, based on the health status detection results of each candidate control cabinet, if a target control cabinet in the intelligent excitation system is identified as meeting the trigger conditions for control takeover, and if a candidate control cabinet malfunctions, the local cloud can obtain key data from the target control cabinet, including key operating parameters and / or instruction execution logic. Based on this key data, the cloud can update itself, using the updated local cloud as a twin of the target control cabinet to control the operation of the intelligent excitation system. This allows the local cloud to take over control of the candidate control cabinet when it meets the trigger conditions for control takeover, continuing to control the components of the intelligent excitation system. This avoids situations where the intelligent excitation system cannot continue operating when the candidate control cabinet fails to control it, effectively improving the reliability of the intelligent excitation system. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

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

[0053] Figure 2 is a flowchart illustrating a control method for an intelligent excitation system based on local cloud computing in one embodiment.

[0054] Figure 3 is a flowchart illustrating the steps of determining the target control cabinet from among the candidate control cabinets based on the health status detection results of each candidate control cabinet in one embodiment.

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

[0056] Figure 5 is a flowchart illustrating a smart excitation system control method 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 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

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

[0061] The intelligent excitation system control method based on local cloud computing provided in this application embodiment can be applied to the application environment shown in Figure 1. The local cloud 102 communicates with the candidate control cabinet 104 via a network, for example, through a fiber optic point-to-point network or a wireless network. The data storage system can store the 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. Each candidate control cabinet 104 includes at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet. Based on the health status detection results of each candidate control cabinet 104, the local cloud 102 can determine a target control cabinet among the candidate control cabinets 104. The target control cabinet is a candidate control cabinet 104 that meets the triggering conditions for control takeover. The local cloud 102 can acquire 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 based on the key data to use the updated local cloud as a twin of the target control cabinet. The twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet. The local cloud 102 can be a server, which can be implemented using a standalone server or a server cluster composed of multiple servers.

[0062] In an exemplary embodiment, as shown in FIG2, a smart excitation system control method based on local cloud computing is provided. Taking the application of this method to the local cloud 102 in FIG1 as an example, the method includes the following steps S210 to S240. Wherein:

[0063] Step S210: Detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection results corresponding to each candidate control cabinet.

[0064] The candidate control cabinet includes at least one regulating cabinet, at least one power cabinet, and at least one demagnetizing cabinet.

[0065] The regulating cabinet is the core control device in the intelligent excitation system, used to regulate and control the generator's excitation current. It collects parameters such as generator voltage and current in real time, and adjusts the excitation current through control algorithms to ensure the stability of the generator's output voltage.

[0066] The power cabinet, in an intelligent excitation system, is a device used to provide excitation current to the generator rotor windings. It typically includes a power converter and a current regulator. The power cabinet has an independent control module capable of providing the required excitation current according to the instructions from the regulating cabinet, and also features health monitoring, fault diagnosis, and redundancy switching functions.

[0067] The demagnetizing cabinet is a device used to quickly cut off the excitation current of a generator. Its main function is to quickly eliminate the rotor magnetic field in emergency situations (such as generator failure or overvoltage protection activation) to prevent generator damage or the spread of power grid faults. The demagnetizing cabinet can achieve rapid demagnetization through automatic control and has self-diagnostic functions.

[0068] In practice, 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 corresponding health status detection results for each candidate control cabinet.

[0069] Step S220: Based on the health status detection results of each candidate control cabinet, determine the target control cabinet from among the candidate control cabinets.

[0070] Among them, the target control cabinet is the candidate control cabinet that meets the triggering conditions for the takeover of control.

[0071] In practice, the local cloud can determine the target control cabinet that meets the triggering conditions for the takeover of control from among the candidate control cabinets of the intelligent excitation system based on the health status detection results of each candidate control cabinet.

[0072] In some embodiments, if a candidate control cabinet is found to be faulty based on the health status detection results of each candidate control cabinet in the local cloud, it can be determined that the candidate control cabinet meets the triggering conditions for control to be taken over.

[0073] In other embodiments, when the candidate control cabinet actively initiates a request to the local cloud to request the local cloud to take over its control, that is, when the local cloud receives the request from the candidate control cabinet to request the local cloud to take over its 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 control. Thus, after receiving the request, the local cloud can determine that the candidate control cabinet meets the triggering conditions for the takeover of control.

[0074] Step S230: Obtain key data of the target control cabinet.

[0075] The key data includes key operating parameters and / or instruction execution logic.

[0076] The key operating parameters of the regulating cabinet may include excitation current-related parameters, such as real-time excitation current value, excitation current setpoint, and excitation current regulation rate, and may also include voltage feedback parameters, such as actual generator terminal voltage value and grid voltage synchronization value. 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] Key operating parameters of the power cabinet may include power output parameters, such as real-time output power, rated power, and power factor.

[0078] Specifically, key operating parameters are direct indicators reflecting the current actual working status of each component of the excitation system. By acquiring these parameters, the local cloud can accurately detect the system's operating condition, much like a candidate control cabinet being taken over, providing an accurate basis for subsequent control decisions. By obtaining these parameters, the local cloud cabinet can more quickly acquire the latest system status, continuing control tasks from the same parameter starting point, maintaining stable operation of the excitation system, avoiding control errors caused by unknown parameters, and ensuring the continuity of power supply.

[0079] Among them, 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 the system requirements, whether facing daily power system operation fluctuations or emergency fault situations, thus ensuring the reliable operation of the excitation system and even the entire power system.

[0080] In practice, the local cloud can obtain key data from the target control cabinet.

[0081] Step S240: Update the local cloud based on key data to use the updated local cloud as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

[0082] In practice, the local cloud can update itself based on key data. The updated local cloud can act as a twin of the target control cabinet, replacing the target control cabinet to control the operation of the intelligent excitation system.

[0083] The local cloud uses at least one of the constant voltage and constant current operating modes 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 fault is detected in the health status indicator of a candidate control cabinet, it can be determined that the candidate control cabinet meets the triggering conditions for control takeover, and it can be designated as the target control cabinet. The local cloud then actively takes over the control of the target control cabinet. This control takeover method can be named active takeover.

[0085] In the aforementioned intelligent excitation system control method based on local cloud computing, the method is applied to the local cloud and obtains the health status detection results of each candidate control cabinet by detecting the health status of each candidate control cabinet. The candidate control cabinets include at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet. Based on the health status detection results of each candidate control cabinet, the target control cabinet is determined among the candidate control cabinets. Key data of the target control cabinet is acquired. The key data includes key operating parameters and / or instruction execution logic. The local cloud is updated based on the key data, so that the updated local cloud serves as a twin of the target control cabinet. The twin of the target control cabinet is used to replace the target control cabinet in controlling the operation of the intelligent excitation system.

[0086] Thus, based on the health status detection results of each candidate control cabinet, if a target control cabinet in the intelligent excitation system is identified as meeting the trigger conditions for control takeover, and if a candidate control cabinet malfunctions, the local cloud can obtain key data from the target control cabinet, including key operating parameters and / or instruction execution logic. Based on this key data, the cloud can update itself, using the updated local cloud as a twin of the target control cabinet to control the operation of the intelligent excitation system. This allows the local cloud to take over control of the candidate control cabinet when it meets the trigger conditions for control takeover, continuing to control the components of the intelligent excitation system. This avoids situations where the intelligent excitation system cannot continue operating when the candidate control cabinet fails to control it, effectively improving the reliability of the intelligent excitation system.

[0087] In one embodiment, the health status detection result includes a health score, as shown in Figure 3. Step S220 involves determining the target control cabinet from among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet, including steps S2202 to S2204:

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

[0089] Step S2204: If the health score of any candidate control cabinet is lower than the health score threshold of any candidate control cabinet, then any candidate control cabinet is determined to meet the triggering condition for control to be taken over, and any candidate control cabinet is selected as the target control cabinet.

[0090] The health score is used to assess the current operating status, reliability, and adaptability of candidate control cabinets. A higher health score indicates a better condition for the candidate control cabinet, while a lower health score indicates a worse condition and potential failure.

[0091] In practice, the local cloud can detect the health status of each candidate control cabinet; based on the health status detection results of each candidate control cabinet, obtain the health score of each candidate control cabinet; for any candidate control cabinet, obtain the health score threshold corresponding to that candidate control cabinet; if the health score of any candidate control cabinet is lower than the health score threshold corresponding to that candidate control cabinet, then that candidate control cabinet may have a fault, and determine that the candidate control cabinet meets the triggering condition for control to be taken over, and then designate that candidate control cabinet as the target control cabinet.

[0092] The technical solution of this embodiment includes a health status detection result, which includes a health score. For any candidate control cabinet, a health score threshold corresponding to the candidate control cabinet is obtained. If the health score of any candidate control cabinet is lower than the health score threshold, it is determined that the candidate control cabinet meets the trigger condition for control takeover. The candidate control cabinet is then used as the target control cabinet to take over the control of the target control cabinet. This achieves the proactive takeover of the control 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. This ensures that even when the health status of the candidate control cabinet is poor, the operation of the intelligent excitation system can still be controlled through the local cloud, thus improving the reliability and fault response capability of the intelligent excitation system.

[0093] In some embodiments, there are multiple intelligent excitation systems. During the process of obtaining the health score threshold corresponding to any candidate control cabinet, the local cloud can obtain a first health score threshold and a second health score threshold corresponding to that candidate control cabinet. The first health score threshold is determined by horizontally comparing the health scores of candidate control cabinets in each intelligent excitation system with those corresponding to the candidate control cabinet. The second health score threshold is determined by vertically comparing the health score of the candidate control cabinet with its historical health score. Thus, the local cloud can determine the health score threshold corresponding to any candidate control cabinet based on the first and / or second health score thresholds. The health score threshold is used to determine whether any candidate control cabinet meets the triggering conditions for control takeover.

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

[0095] In some other embodiments, the local cloud can determine 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.

[0096] In the process of obtaining the first and second health score thresholds corresponding to any candidate control cabinet, the local cloud can obtain other health scores. These other health scores include the health scores of candidate control cabinets in other intelligent excitation systems that correspond to the candidate control cabinet. These other intelligent excitation systems include intelligent excitation systems other than the one containing the candidate control cabinet. Thus, the local cloud can determine the first health score threshold based on the average of the other health scores and the health score corresponding to the candidate control cabinet.

[0097] To facilitate understanding by those skilled in the art, Figure 4 provides an application environment diagram for another intelligent excitation system control method based on local cloud computing. As shown in Figure 4, 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 no specific limit is made here). Each intelligent excitation system's candidate control cabinet may 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 demagnetizing cabinet (Demagnetizing Cabinet 1).

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

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

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

[0101] For example, continuing from 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. For example, it can obtain 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 between the three historical health scores of the power cabinet 1 in the intelligent excitation system 1 and the current corresponding health score, and use it as the second health score threshold corresponding to the power cabinet 1 in the intelligent excitation system 1.

[0102] In some other embodiments, when the local cloud determines the health score threshold corresponding to any candidate control cabinet based on the average of the first health score threshold and the second health score threshold, the local cloud can obtain the first weight information corresponding to the first health score threshold and the second weight information corresponding to the second health score threshold. In this way, the weighted average of 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, and used as the health score threshold corresponding to any candidate control cabinet.

[0103] The technical solution of this embodiment obtains 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 candidate control cabinets in each intelligent excitation system with those of any candidate control cabinet. The second health score threshold is determined by vertically comparing the health score of any candidate control cabinet with its historical health score. The average of the first and second health score thresholds is used to determine the health score threshold for any candidate control cabinet. Thus, horizontal comparison allows for a more accurate determination of the relative health status of any candidate control cabinet within multiple intelligent excitation systems; vertical comparison considers the historical health score of the candidate control cabinet itself. Combining both horizontal and vertical comparisons, and taking the average of the first and second health score thresholds as the health score threshold, comprehensively considers both the internal system situation and its own time series, thereby more accurately determining whether the current health status of any candidate control cabinet is within a reasonable range. Furthermore, because it integrates the comparison results from two different dimensions, the determination of the health score threshold is not based on a single standard. This multi-dimensional assessment method can reduce erroneous judgments caused by the limitations of a single comparison method, and enhance the reliability of determining health score thresholds.

[0104] In one embodiment, 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: acquiring the operation status log of the intelligent excitation system; the operation status log includes the operation status data of any candidate control cabinet; acquiring the data calculation rules corresponding to each health indicator, and performing calculation processing on the operation status data of any candidate control cabinet according to the data calculation 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; and obtaining the health status detection result corresponding to any candidate control cabinet based on the health indicator data of any candidate control cabinet.

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

[0106] Among them, the operating status data can refer to data associated with the operating status of the intelligent excitation system. For example, the operating status data can include temperature data, voltage and current data, etc.

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

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

[0109] Other indicators may include at least one of the following: temperature, load, running time, etc.

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

[0111] Among them, the health indicator data can be the indicator data obtained by monitoring the health status of the intelligent excitation system using health indicators.

[0112] In specific implementation, during the process of detecting the health status of each candidate control cabinet and obtaining the corresponding health status detection results, the local cloud can acquire the operating status log of the intelligent excitation system. The operating status log includes the operating status data of any candidate control cabinet. The local cloud can obtain the data calculation rules corresponding to each health indicator, and process the operating status data of any candidate control cabinet according to the data calculation rules corresponding to each health indicator to obtain the health indicator data of that candidate control cabinet. Based on the health indicator data of that candidate control cabinet, the corresponding health status detection result is obtained. Thus, based on the same method, the health status detection result for each candidate control cabinet can be obtained.

[0113] The technical solution of this embodiment acquires the operating status log of the intelligent excitation system. The operating status log includes the operating status data of any candidate control cabinet. Data calculation rules corresponding to each health indicator are acquired, and the operating status data of any candidate control cabinet is processed according to these rules to obtain health indicator data for that candidate control cabinet. Health indicators are used to evaluate the health status of any candidate control cabinet. Based on the health indicator data of any candidate control cabinet, a health status detection result is obtained. Thus, by acquiring the operating status log of the intelligent excitation system, which includes the operating status data of any candidate control cabinet, and processing the operating status data of any candidate control cabinet according to the data calculation rules corresponding to each health indicator to obtain health indicator data, the health status of candidate control cabinets can be detected more accurately, effectively improving the accuracy and reliability of health status detection in the intelligent excitation system.

[0114] Furthermore, the operation log data can include operation status data to be processed; the local cloud can remove operation status data that does not meet the preset conditions from the operation status data to be processed, and obtain the removed operation status data. Then, according to the data calculation rules corresponding to each health indicator, the removed operation status data can be processed to obtain health indicator data.

[0115] Among these, operational status data that does not meet preset conditions can refer to at least one of the following: data with data acquisition errors, data with incorrect data formats, and data that does not conform to logic. For example, data acquisition errors can refer to data collected due to sensor malfunctions, communication interruptions, or data transmission errors that does not conform to the actual situation; incorrect data formats can refer to data whose format does not conform to predefined rules; and data that does not conform to logic can refer to data that, from the perspective of system operation, does not conform to the normal operating logic of the intelligent excitation system. For example, under normal circumstances, the excitation current increases appropriately with the increase of generator load, but the operation log shows data records of the excitation current suddenly dropping to zero when the generator load increases. This is different from the normal operating logic of the system and is likely caused by data recording errors or abnormal conditions in the system. Such data that does not conform to logic will be identified as erroneous data.

[0116] In other embodiments, during the process of obtaining the health status detection result of any candidate control cabinet based on the health index data of any candidate control cabinet, the local cloud can input the health index data of any candidate control cabinet into a pre-trained health status assessment model and output the health status detection result of any 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 index and the health index data corresponding to each health index.

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

[0118] The excitation system health assessment system refers to an indicator system for assessing the health status of intelligent excitation systems. This indicator system can include various health indicators at different levels.

[0119] In practice, the local cloud can input the health index 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 based on the weight information and health index data corresponding to each health index, 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 further includes: acquiring health indicators set for the intelligent excitation system; and constructing an excitation system health assessment system by combining the state of the intelligent excitation system from the whole to the parts in the form of hierarchical components based on each health indicator; the excitation system health assessment system includes health indicators with a corresponding hierarchical structure.

[0121] In practice, for the construction of the excitation system health assessment system, the local cloud can obtain the health indicators set for the intelligent excitation system. Based on each health indicator, the state of the intelligent excitation system is combined from the whole to the part 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, the process of constructing a health assessment system for the excitation system involves organically combining the state of the excitation system from the overall system to the local level in the form of hierarchical components. The health status of the intelligent excitation system is then assessed using the Analytic Hierarchy Process (AHP), and scoring rules are specified.

[0123] Specifically, it includes:

[0124] Step 1: Determine the assessment objectives and criteria; Assessment objective: Assess the health status of the intelligent excitation system. Assessment criteria: Determine the main health indicators affecting the health status. These health indicators may include at least one of the following: conventional failure rate, accelerated failure rate, conventional mean time between failures (MTBF), accelerated mean time between failures (MTBF), and other indicators (such as temperature, load, uptime, etc., depending on the specific excitation system).

[0125] Step 2: Construct a hierarchical structure; construct a hierarchical structure from the overall excitation system to the local components in the form of graded components; determine the hierarchical structure in which each health indicator is located.

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

[0128] Thus, in some embodiments, a hierarchical structure is constructed from the whole to the parts of the excitation system in the form of graded components; by determining the hierarchical structure of each health indicator, the faulty components in the intelligent excitation system can be detected by using the health indicator data obtained from monitoring the health indicators of the intelligent excitation system.

[0129] Step 3: Perform pairwise comparisons: Using the pairwise comparison method, determine the importance of each health indicator for health status monitoring in pairs to 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 using 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: converting the corresponding health indicator data into score values ​​according to the scoring rules corresponding to each health indicator; obtaining the product result between the weight information corresponding to each health indicator and the corresponding score value; obtaining the sum of each product result, generating a health status score and / or health status level based on the sum of each product result; and outputting the health status detection result based on the health status score and / or health status level.

[0132] In practice, the pre-trained health status assessment model can convert the corresponding health indicator data into score values ​​according to the scoring rules corresponding to each health indicator; obtain the product results between the weight information corresponding to each health indicator and the corresponding score value; add the product results to obtain the sum of the product results; generate a health status score and / or health status level based on the sum of the product results; and 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 can be generated based on the sum of the product results, and then the corresponding health status level can be determined based on the health status score.

[0134] The technical solution of this embodiment converts the corresponding health indicator data into score values ​​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 score value; obtains the sum of each product result; generates a health status score and / or health status level based on the sum of each product result; and outputs the health status detection result based on the health status score and / or 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 level are generated by summing the product of the score value obtained by converting the health indicator data corresponding to each health indicator and the corresponding weight. The complex system operation status is transformed into intuitive and easy-to-understand values ​​and categories, enabling operation and maintenance personnel to quickly and accurately grasp the overall health status of the excitation system and effectively improving the intelligence of the health assessment of the excitation system.

[0136] In some embodiments, the construction of the pre-trained health status assessment model can be based on existing excitation fault diagnosis data and expert database data to establish a data-driven health status assessment model for the intelligent excitation system. This 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 the pre-trained health status assessment model.

[0137] Thus, relying on existing excitation fault diagnosis data, which contains crucial information such as numerous past fault cases, fault characteristics, and corresponding solutions encountered in actual operation, the model receives highly realistic and targeted learning materials. Furthermore, the introduction of expert database data further enhances the model's professionalism. Continuous training of the model using training sample data allows it to flexibly adjust to dynamic changes in the excitation system's operating environment, conditions, and the equipment's own state. Comparing health status detection results with actual experimental data provides direct evidence for dynamic model updates. This real-time feedback and optimization mechanism ensures the model does not deviate from reality, continuously improving the reliability of predictions.

[0138] In one embodiment, the method further includes: receiving requests sent by each candidate control cabinet; determining, upon detecting a target request, that the candidate control cabinet sending the target request meets the triggering condition for control takeover; the target request is used to instruct the local cloud to take over control; and designating the candidate control cabinet that sent the target request as the target control cabinet.

[0139] In practice, each candidate control cabinet can communicate with the local cloud and send requests to the local cloud. The local cloud can receive requests from each candidate control cabinet. If it detects that the received request is a target request that instructs the local cloud to take over its control, it determines that the candidate control cabinet that sent the target request meets the triggering condition for the takeover of control and designates the candidate control cabinet that sent the target request as the target control cabinet.

[0140] In practical applications, candidate control cabinets can send target requests to instruct the local cloud to take over control in the following scenarios:

[0141] 1. Self-fault warning:

[0142] When the candidate control cabinet detects potential hardware malfunctions, such as overheating of critical chips, frequent memory read / write errors, or software anomalies, such as a program getting stuck in an infinite loop or a critical process crashing and restarting more than a threshold, it will proactively request local cloud control to take over in order to ensure the excitation system continues to operate normally.

[0143] 2. Network communication error:

[0144] If the network connection between the candidate control cabinet and external devices (such as generator monitoring sensors, host computer, etc.) experiences frequent interruptions or excessively high packet loss rates (e.g., packet loss exceeding 20% ​​within 5 consecutive minutes), it will be unable to obtain accurate operating status data in a timely manner, making it difficult to precisely control the excitation system. To ensure system stability, it will choose to let the local cloud take over control, maintaining 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 or its parameters updated, 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 takeover period in the local cloud.

[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 control. After receiving the target request, the local cloud can take over control of the candidate control cabinet. This control takeover method can be named passive takeover.

[0148] In one embodiment, a local cloud computing-based intelligent excitation system is provided. The system includes: candidate control cabinets and a local cloud platform; the candidate control cabinets include at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet; the local cloud platform is used to detect the health status of each candidate control cabinet in the intelligent excitation system and obtain the corresponding health status detection results for each candidate control cabinet; the local cloud platform is used to determine a target control cabinet among the candidate control cabinets based on the corresponding health status detection results; the target control cabinet is a candidate control cabinet that meets the triggering conditions for control takeover; the local cloud platform is used to acquire key data of the target control cabinet; the key data includes key operating parameters and / or instruction execution logic; the local cloud platform is used to update the local cloud platform based on the key data, so that the updated local cloud platform serves as a twin of the target control cabinet; the twin of the target control cabinet is used to replace the target control cabinet in controlling the operation of the intelligent excitation system.

[0149] As shown in Figure 4, an intelligent excitation system may include: a candidate control cabinet and a local cloud; the candidate control cabinet may include 2 regulating cabinets, 3 power cabinets, and 1 demagnetizing cabinet. The 2 regulating cabinets include a main regulating cabinet and a backup regulating cabinet. When the main regulating cabinet fails, control is transferred to the backup regulating cabinet. When the backup regulating cabinet's health status indicates a failure, or when the backup regulating cabinet sends a target request to the local cloud, the local cloud takes over control of the backup regulating cabinet.

[0150] Furthermore, the regulating cabinet, power cabinet, demagnetizing cabinet, and local cloud can be located in different computer rooms. The regulating cabinet, power cabinet, demagnetizing cabinet, and local cloud (which can also be named cloud service host, local cloud cabinet (twin)) communicate with the regulating cabinet, power cabinet, and demagnetizing cabinet via optical fiber and use FPGA (Field Programmable Gate Array) modules to process optical signals.

[0151] Local cloud can take over control of candidate control cabinets 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 if a candidate control cabinet fails.

[0153] Passive takeover: The candidate control cabinet requests local cloud takeover of its control.

[0154] The controllers in the regulating cabinet, power cabinet, and demagnetizing cabinet are all equipped with control interfaces for control by the local cloud. These control interfaces can be fiber optic communication modules or wireless network modules (as backup). The regulating cabinet, power cabinet, and demagnetizing cabinet can receive control commands sent by the local cloud via a local area network through these control interfaces. During 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 each component in the intelligent excitation system. When the local cloud takes over the control of the excitation system, it can operate in at least one of the constant voltage and constant current modes. The excitation system can continue to receive "increase magnetization," "decrease magnetization," and "stop" commands. Specifically, the local cloud receives these commands and then responds by sending corresponding control commands to each component in the excitation system to enable the excitation system to complete the "increase magnetization," "decrease magnetization," and "stop" commands. In practical applications, wireless network modules can adopt the 802.11be (wifi7) protocol. wifi7 has high throughput, low latency, anti-interference, and wide coverage, and can reduce latency to 1-10 milliseconds (ms).

[0155] Thus, when the candidate control cabinet meets the triggering conditions for control takeover, control of the candidate control cabinet is taken over by the local cloud, and the various components of the intelligent excitation system are controlled. This avoids the situation where the intelligent excitation system cannot continue to operate when the candidate control cabinet cannot control its operation, effectively improving the reliability of the intelligent excitation system.

[0156] In another embodiment, as shown in Figure 5, a smart excitation system control method based on local cloud computing is provided. Taking the application of this method to a local cloud as an example, it includes steps S200 to S240:

[0157] Step S200: Receive requests from each candidate control cabinet.

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

[0159] Step S204: Select the candidate control cabinet that sent the target request as the target control cabinet.

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

[0161] 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: Obtain other health scores; other health scores include the health scores of 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.

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

[0164] Step S2012: Obtain historical health scores; historical health scores include health scores obtained from testing any candidate control cabinet within a historical time period.

[0165] Step S2014: 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.

[0166] Step S203: Determine 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 health score threshold is used to determine whether any candidate control cabinet meets the triggering condition for control to be taken over.

[0167] Step S203, which determines 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, includes steps S2031 to S2033:

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

[0169] Step S2033: 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, and use it as the health score threshold corresponding to any candidate control cabinet.

[0170] Step S210: Detect the health status of each candidate control cabinet of the intelligent excitation system and obtain the health status detection results corresponding to each candidate control cabinet.

[0171] Step S210 involves detecting the health status of each candidate control cabinet in the intelligent excitation system and obtaining the health status detection results for each candidate control cabinet, including steps S2102 to S2106:

[0172] Step S2102: Obtain the operating status log of the intelligent excitation system; the operating status log includes the operating status data of any candidate control cabinet.

[0173] Step S2104: Obtain the data calculation rules corresponding to each health indicator, and process the operating status data of any candidate control cabinet according to the data calculation rules corresponding to each health indicator to obtain the health indicator data of any candidate control cabinet.

[0174] Step S2106: Based on the health indicator data of any candidate control cabinet, obtain the health status detection result corresponding to any candidate control cabinet.

[0175] Step S220: Based on the health status detection results of each candidate control cabinet, determine the target control cabinet from among the candidate control cabinets.

[0176] Step S220: Based on the health status detection results of each candidate control cabinet, determine the target control cabinet from among the candidate control cabinets, including steps S2202 to S2204:

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

[0178] Step S2204: If the health score of any candidate control cabinet is lower than the health score threshold of any candidate control cabinet, then any candidate control cabinet is determined to meet the triggering condition for control to be taken over, and any candidate control cabinet is selected as the target control cabinet.

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

[0180] Step S240: Update the local cloud based on key data to use the updated local cloud as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

[0181] It should be noted that the specific limitations of the above steps can be found in the above description of the specific limitations of a local cloud computing-based intelligent excitation system control method.

[0182] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0183] Based on the same inventive concept, this application also provides a local cloud computing-based intelligent excitation system control device for implementing the aforementioned local cloud computing-based intelligent excitation system control method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations in one or more embodiments of the local cloud computing-based intelligent excitation system control device provided below can be found in the limitations of the local cloud computing-based intelligent excitation system control method described above, and will not be repeated here.

[0184] In an exemplary embodiment, as shown in FIG6, a smart excitation system control device based on local cloud computing is provided, applied to a local cloud, including: 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 of each candidate control cabinet; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetizing cabinet.

[0186] The determination module 620 is used to determine the target control cabinet among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet; the target control cabinet is a candidate control cabinet that meets the triggering condition for control 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 based on the key data, so as to use the updated local cloud as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

[0189] In one embodiment, the health status detection result includes a health score. The determining module 620 is specifically used to obtain a health score threshold corresponding to 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 the candidate control cabinet meets the triggering condition for control to be taken over, and the candidate control cabinet is used as the target control cabinet.

[0190] In one embodiment, there are multiple intelligent excitation systems. The determining 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 candidate control cabinets in each of the intelligent excitation systems with those of any candidate control cabinet. The second health score threshold is determined by vertically comparing the health score of any candidate control cabinet with the historical health score of any candidate control cabinet. The health score threshold corresponding to any candidate control cabinet is determined based on the average value between the first health score threshold and the second health score threshold.

[0191] In one embodiment, the determining module 620 is specifically used to obtain other health scores; the other health scores include the health scores of candidate control cabinets corresponding to any candidate control cabinet in other intelligent excitation systems; the other intelligent excitation systems include intelligent excitation systems other than the intelligent excitation system where any candidate control cabinet is located; and 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 determining module 620 is specifically used to obtain historical health scores; the historical health scores include health scores obtained by detecting any candidate control cabinet during a historical time period; and a second health score threshold is determined based on the average value between the historical health scores and the health scores corresponding to any candidate control cabinet.

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

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

[0195] In one embodiment, the determining module 620 is further configured to receive requests sent by each of the candidate control cabinets; upon detecting a target request, determine that the candidate control cabinet that sent the target request meets the triggering 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 that sent the target request is designated as the target control cabinet.

[0196] In one embodiment, 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] The modules in the aforementioned intelligent excitation system control device based on local cloud computing can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0198] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 7. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device stores key data of the target control cabinet. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart excitation system control method based on local cloud computing.

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

[0200] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0201] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0204] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0205] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method for an intelligent excitation system based on local cloud computing, characterized in that, Applied to a local cloud environment, the method includes: The health status of each candidate control cabinet in the intelligent excitation system is detected, and the health status detection result corresponding to each candidate control cabinet is obtained; the candidate control cabinet includes at least a regulating cabinet, a power cabinet, and a demagnetizing cabinet. Based on the health status detection results of each candidate control cabinet, 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 control 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 based on the key data, so that the updated local cloud serves as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

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, obtain the first health score threshold and the second health score threshold corresponding to that candidate control cabinet; The first health score threshold is determined by horizontally comparing the health scores of 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 of any candidate control cabinet with the historical health score of any candidate control cabinet; the health score threshold corresponding to any candidate control cabinet is determined based on 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.

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: Obtain other health scores; the other health scores include the health scores of candidate control cabinets in other intelligent excitation systems that correspond to any of the candidate control cabinets; the other intelligent excitation systems include intelligent excitation systems other than the intelligent excitation system where any of the candidate control cabinets 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.

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: Obtain historical health scores; the historical health scores include health scores obtained by detecting any candidate control cabinet within a historical time period. The second health score threshold is determined based on the average of the historical health scores and the health scores corresponding to any candidate control cabinet.

5. The method according to claim 2, characterized in that, 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 includes: 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, 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 results include a health score. The step of determining the target control cabinet from among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet includes: For any candidate control cabinet, obtain the health score threshold corresponding to that candidate control cabinet; If the health score of any candidate control cabinet is lower than the health score threshold of any candidate control cabinet, then the candidate control cabinet is determined to meet the triggering condition for control to be taken over, and the candidate control cabinet is selected as the target control cabinet.

7. The method according to claim 1, characterized in that, The detection of the health status of each candidate control cabinet in the intelligent excitation system yields the health status detection results for each candidate control cabinet, including: Obtain the operating status log of the intelligent excitation system; the operating status log includes the operating status data of any candidate control cabinet; Obtain the data calculation rules corresponding to each health indicator, and process the operating status data of any candidate control cabinet according to the data calculation 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. Based on the health indicator data of any candidate control cabinet, the 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 includes: Receive requests sent by each of the candidate control cabinets; Upon detecting a target request, it is determined that the candidate control cabinet that sent the target request meets the triggering 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 will be designated 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 platform; the candidate control cabinet includes at least a regulating cabinet, a power cabinet, and a demagnetizing 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 of each candidate control cabinet. The local cloud is used to determine the target control cabinet from among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet; the target control cabinet is a candidate control cabinet that meets the triggering conditions for control to be taken over. The local cloud is used to acquire 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 itself based on the key data, so that the updated local cloud serves as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

10. A smart excitation system control device based on local cloud computing, characterized in that, The device, applied locally to the cloud, includes: The 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 of each candidate control cabinet; the candidate control cabinet includes at least a regulating cabinet, a power cabinet and a demagnetizing cabinet. The determination module is used to determine the target control cabinet among the candidate control cabinets based on the health status detection results corresponding to each candidate control cabinet; the target control cabinet is a candidate control cabinet that meets the triggering condition for control to be taken over. The acquisition module is 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 based on the key data, so as to use the updated local cloud as a twin of the target control cabinet; the twin of the target control cabinet is used to control the operation of the intelligent excitation system in place of the target control cabinet.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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

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

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