Converter station intelligent operation management and control method and system

Abstract

The invention relates to the technical field of power systems, and particularly discloses a converter station intelligent operation management and control method and system.The converter station intelligent operation management and control method comprises the steps that operation data of all subareas of a converter station are monitored in real time, and the operation data comprise element operation data of all the subareas; calculating a concentration trend and a dispersion degree of the operation data of the converter station, and dividing the operation data of the converter station into a plurality of groups of types; constructing a converter station element four-state model based on the element operation data of a plurality of groups of types of each partition; based on the converter station element four-state model, calculating the power outage time of the converter station power transmission system and the energy availability rate in the converter station power transmission system; and calculating the fault risk level of the converter station based on the energy availability rate and the consequence level of the risk, and performing advanced intervention according to the risk level. The method has the advantages that multiple technologies such as real-time monitoring, data analysis, a neural network model and risk assessment are fused, and the intelligent level of the converter station is promoted to be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for intelligent operation control of a converter station. Background Art

[0002] A converter station refers to a site in a high-voltage direct current transmission system that is built to convert alternating current into direct current or vice versa, and to meet the power system's requirements for safety, stability and power quality.

[0003] In the existing technology, during the operation of the converter station, it is necessary to record the operating data of the converter station. Due to the large number of partitions in the converter station, the operating data is large and relatively mixed, which makes subsequent analysis difficult. In addition, traditional analysis also judges the data based on the experience of the staff to determine the operating risk level of the converter station. The degree of automation is low and it is difficult to meet the management and control needs of the converter station. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for intelligent operation control of a converter station.

[0005] The object of the present invention is achieved through the following technical solution: a method for intelligent operation control of a converter station, the method comprising:

[0006] Real-time monitoring of the operating data of each section of the converter station, wherein the operating data includes the component operating data of each section;

[0007] Calculate the central tendency and dispersion of converter station operation data, and classify the converter station operation data into several groups;

[0008] Based on the operating data of several groups of components in each zone, a neural network model is used to determine the optimal state of each zone in the converter station, and a four-state model of the converter station components is constructed;

[0009] Based on the four-state model of converter station components, the power outage time and energy availability of the converter station transmission system are calculated, wherein the energy availability represents the degree to which its transmission capacity is restricted;

[0010] The fault risk level of the converter station is calculated based on the energy availability and the consequence level of the risk, and early intervention is carried out according to the risk level.

[0011] Specifically, the calculation of the concentration trend and dispersion degree of converter station operation data includes the following steps:

[0012] Calculate the average value of the converter station operation data, and calculate the variance of the converter station operation data according to the variance formula;

[0013] The variance is set as the central tendency of the converter station operation data, and a trend identification code is assigned to the central tendency;

[0014] pairing each data in the converter station operation data with the remaining data to form at least one pairing group;

[0015] Calculate the absolute value of the difference between the two data in each paired group, and accumulate the absolute value of the difference between the two data in all paired groups to obtain the discrete value;

[0016] The discrete value is set as the discrete degree of the converter station operation data, and a discrete identification code is assigned to the discrete degree.

[0017] Specifically, the step of dividing the converter station operation data into several groups includes the following steps:

[0018] Retrieve historical data classification criteria from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification criteria;

[0019] Calculate the ratio of the central tendency of the converter station operation data to the central tendency mean of the sample data;

[0020] Calculate the ratio of the discreteness of the converter station operation data to the mean discreteness of the sample data;

[0021] Comparing the first ratio and the second ratio with a second threshold and a third threshold preset in the system respectively;

[0022] If both the ratio 1 and the ratio 2 are less than or equal to the second threshold, it is determined to be the first category;

[0023] If both the ratio 1 and the ratio 2 are greater than the second threshold but less than the third threshold, it is determined to be the second classification;

[0024] If both the ratio 1 and the ratio 2 are greater than or equal to the third threshold, it is determined to be the third category.

[0025] Specifically, the four-state model of the converter station components is:

[0026]

[0027] In the formula, the four states of the converter station components are the normal state (A), the maintenance state (B), the fault state (C), and the repair state after fault removal (D). The probability that a component changes from a normal state to a state where an accident occurs but the fault is not removed; The probability that a component changes from a planned maintenance state to a repaired state after an accident removal; The planned maintenance rate of the component from normal status to planned maintenance status; It is the component repair rate from the normal state to the state where the accident occurs but the fault is not removed; The repair rate of components when the components change from planned maintenance status to accident removal status; The repair rate of components when they change from normal status to planned maintenance status;

[0028] In the stable state of the converter station system, the following relationship exists:

[0029]

[0030] Where, 、 、 、 are the probabilities of the component corresponding to states A, B, C and D respectively.

[0031] Specifically, the formula for calculating the power outage time of the converter station transmission system is:

[0032]

[0033] Where i is the operating state of the converter station transmission system, i=1, 2,…, n; is the rated transmission capacity; is the outage capacity of operating state i; is the running time of running state i.

[0034] Specifically, the formula for calculating the energy availability rate in the converter station transmission system is:

[0035]

[0036]

[0037] Where, is the total operating time of the converter station transmission system in each operating state; i is the operating state of the converter station transmission system, i=1, 2,…, n; is the running time of running state i; It is the power outage time of the converter station transmission system.

[0038] Specifically, the formula for calculating the fault risk level of the converter station based on the energy availability and the risk consequence level is:

[0039]

[0040] Where, Indicates the fault risk level of the converter station; Indicates the consequence level of the risk, The weight value is between (0,1); The energy availability in the transmission system of the converter station; The function rounds to an integer.

[0041] A converter station intelligent operation control system, comprising:

[0042] A real-time monitoring module, which is used to monitor the operating data of each partition of the converter station in real time, wherein the operating data includes the component operating data of each partition;

[0043] a classification module, the classification module being used to calculate the central tendency and dispersion degree of the converter station operation data, and to classify the converter station operation data into a plurality of groups, the categories including status information data of different components;

[0044] a model building module, the model building module being used to determine the optimal state of each zone of the converter station using a neural network model based on the operating data of several groups of types of components in each zone, and to build a four-state model of the converter station components;

[0045] a calculation module, the calculation module being configured to calculate the power outage time and energy availability of the converter station transmission system based on a four-state model of converter station components, wherein the energy availability represents the degree to which its transmission capacity is restricted;

[0046] A risk level determination module is used to calculate the fault risk level of the converter station based on the energy availability and the consequence level of the risk.

[0047] The present invention has the following advantages:

[0048] By monitoring the operating data of components in each section of the converter station in real time, the present invention can comprehensively grasp the operating status of the converter station and avoid the limitations of a single data source. By calculating the concentration trend and the degree of dispersion, the operating data is grouped and classified, which helps to identify the status information of different components and provides structured data support for subsequent analysis. A four-state model of the converter station components is constructed to provide a theoretical basis for the subsequent calculation of power outage time and energy availability. Based on the four-state model, the power outage time of the converter station transmission system is calculated, which helps to evaluate the system's recovery capability in the event of a fault and provide data support for operation and maintenance decisions. By calculating the energy availability, the degree of transmission capacity limitation is quantified, providing a basis for system optimization and expansion. The fault risk level of the converter station is calculated by combining the energy availability and the risk consequence level, realizing a quantitative risk assessment. Early intervention based on the risk level can timely take preventive measures to reduce the probability of failure. Therefore, the present invention integrates multiple technologies such as real-time monitoring, data analysis, neural network models and risk assessment to promote the improvement of the intelligent level of converter stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the control method of the present invention;

[0050] Figure 2 Schematic diagram of the method for calculating the central tendency and dispersion degree of converter station operation data in the present invention;

[0051] Figure 3 Schematic diagram of the method for classifying converter station operation data into several groups in the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for the purpose of explaining the present invention and are not intended to limit the present invention. That is, the embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0054] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0055] The present invention will be further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0056] like Figures 1 to 3 As shown, a converter station intelligent operation control method includes:

[0057] Real-time monitoring of the operating data of each section of the converter station, including the component operating data of each section; operating data includes voltage, current, converter station line transmission capacity, instrumentation, sensor and transformer operating data, etc., which can fully grasp the operating status of the converter station and avoid the limitations of a single data source.

[0058] Calculate the central tendency and dispersion of converter station operation data and classify the data into several groups, which helps identify the status information of different components and provides structured data support for subsequent analysis;

[0059] The calculation of the central tendency and dispersion degree of the converter station operation data specifically includes the following steps:

[0060] Calculate the average value of the converter station operation data, and calculate the variance of the converter station operation data according to the variance formula;

[0061] The variance is set as the central trend of the converter station operation data, and a trend identification code is assigned to the central trend; the trend identification code is used to quantify the data fluctuation characteristics: the variance is a quantitative indicator of the central trend, and the trend identification code gives it a computable identifier. Through this identification code, it is possible to intuitively judge whether the converter station operation data is in a stable fluctuation state, with low variance corresponding to a low-risk trend code, or whether there is an abnormal fluctuation trend, with high variance corresponding to a high-risk trend code. For example, if the variance of the converter station voltage data suddenly increases, the trend identification code will indicate that the voltage fluctuation has intensified, and attention should be paid to equipment stability; in the operation and maintenance of the converter station, the trend identification code provides the operation and maintenance personnel with an "early warning signal" of data fluctuations. When the trend code exceeds the threshold, it can trigger operations such as equipment inspection and parameter adjustment to avoid equipment failure or system instability caused by data fluctuations;

[0062] Each data in the converter station operation data is paired with the remaining data to form at least one pairing group; the remaining data refers to all other data points in the same data set except the current data.

[0063] Calculate the absolute value of the difference between the two data in each paired group, and accumulate the absolute value of the difference between the two data in all paired groups to obtain the discrete value;

[0064] The discrete value is set as the degree of discreteness of the converter station operating data, and a discrete identification code is assigned to the discrete degree. The discrete identification code reflects the degree of data difference. The discrete value is accumulated by the absolute value of the paired group difference to quantify the degree of difference between the data. The discrete identification code gives this value a comparable identifier and is used to describe the degree of discreteness of the converter station operating data. A high discrete identification code indicates large data differences, which may include multiple abnormal points or inconsistent equipment performance; a low discrete identification code indicates high data consistency and stable system operation. In converter station fault analysis, the discrete identification code can help locate the problem. For example, if the discrete identification code of the current data increases significantly within a certain period of time, it may indicate the presence of multiple equipment parameter anomalies or external interference, and the specific cause needs to be further investigated. Trend identification code: reflects the centralized trend of data fluctuations and is used to evaluate data stability and identify abnormal fluctuations. Discrete identification code: reflects the degree of difference between data and is used to evaluate the range of data fluctuations and assist in fault diagnosis.

[0065] The step of dividing the converter station operation data into several groups specifically includes the following steps:

[0066] Retrieve historical data classification criteria from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification criteria;

[0067] Calculate the ratio of the central tendency of the converter station operation data to the central tendency mean of the sample data;

[0068] Calculate the ratio of the discreteness of the converter station operation data to the mean discreteness of the sample data;

[0069] Comparing the first ratio and the second ratio with a second threshold and a third threshold preset in the system respectively;

[0070] If both the ratio 1 and the ratio 2 are less than or equal to the second threshold, it is determined to be the first category;

[0071] If both ratio 1 and ratio 2 are greater than the second threshold but less than the third threshold, it is determined to be the second classification;

[0072] If both the ratio 1 and the ratio 2 are greater than or equal to the third threshold, it is determined to be the third category.

[0073] The second threshold is a limit used to determine whether the central tendency and dispersion of converter station operating data are within normal ranges. Standards from the power industry or related fields may be referenced, and these standards may be developed based on extensive historical data and expert experience. By analyzing historical converter station operating data, typical ranges of central tendency and dispersion can be determined, thereby setting the second threshold. When both Ratio 1 and Ratio 2 are less than or equal to the second threshold, the first classification is determined, indicating that the converter station operating data is normal.

[0074] The third threshold is another critical value used to determine whether the concentration trend and dispersion of converter station operating data exceed normal ranges, reaching a level that requires high attention or emergency measures. The third threshold is set based on the potential risk posed by abnormal converter station operating data. The higher the risk, the lower the third threshold may be set.

[0075] When both ratio 1 and ratio 2 are greater than or equal to the third threshold, it is determined to be the third category, indicating that the converter station operation data is in a highly abnormal state and immediate measures need to be taken to handle it.

[0076] Based on the operating data of several groups of components in each zone, a neural network model is used to determine the optimal state of each zone in the converter station. A four-state model of the converter station components is also constructed, providing a theoretical basis for subsequent calculations of power outage time and energy availability.

[0077] The four-state model of the converter station components is:

[0078]

[0079] In the formula, the four states of the converter station components are the normal state (A), the maintenance state (B), the fault state (C), and the repair state after fault removal (D). The probability that a component changes from a normal state to a state where an accident occurs but the fault is not removed; The probability that a component changes from a planned maintenance state to a repaired state after an accident removal; The planned maintenance rate of a component from a normal state to a planned maintenance state; It is the component repair rate from the normal state to the state where the accident occurs but the fault is not removed; The repair rate of components when the components change from planned maintenance status to accident removal status; The repair rate of components when they change from normal status to planned maintenance status;

[0080] In the stable state of the converter station system, the following relationship exists:

[0081]

[0082] Where, 、 、 、 are the probabilities of the component corresponding to states A, B, C and D respectively.

[0083] Based on the four-state model of converter station components, the power outage time and energy availability of the converter station transmission system are calculated. Calculating the power outage time of the converter station transmission system helps evaluate the system's recovery capability in the event of a fault, providing data support for operation and maintenance decisions. Furthermore, by calculating the energy availability, the degree of transmission capacity restriction is quantified, providing a basis for system optimization and expansion.

[0084] The formula for calculating the power outage time of the converter station transmission system is:

[0085]

[0086] Where i is the operating state of the converter station transmission system, i=1, 2,…, n; is the rated transmission capacity; is the outage capacity of operating state i; is the running time of running state i.

[0087] The formula for calculating the energy availability rate in the converter station transmission system is:

[0088]

[0089]

[0090] Where, is the total operating time of the converter station transmission system in each operating state; i is the operating state of the converter station transmission system, i=1, 2,…, n; is the running time of running state i; It is the power outage time of the converter station transmission system.

[0091] The fault risk level of the converter station is calculated based on the energy availability and the consequence level of the risk. Early intervention is carried out according to the risk level to achieve quantitative risk assessment. Early intervention based on the risk level can enable timely preventive measures to reduce the probability of faults.

[0092] The formula for calculating the fault risk level of the converter station based on the energy availability and the consequence level of the risk is:

[0093]

[0094] Where, Indicates the fault risk level of the converter station; Indicates the consequence level of the risk, The weight value is between (0,1); The energy availability in the transmission system of the converter station; The function rounds to an integer.

[0095] A converter station intelligent operation control system, comprising:

[0096] A real-time monitoring module is used to monitor the operating data of each partition of the converter station in real time. The operating data includes the operating data of the components of each partition; the operating data includes voltage, current, transmission capacity of the converter station line, instrumentation, sensor and transformer operating data, etc.

[0097] a classification module, the classification module being used to calculate the central tendency and dispersion degree of the converter station operation data, and to classify the converter station operation data into a plurality of groups, the categories including status information data of different components;

[0098] a model building module, the model building module being used to determine the optimal state of each zone of the converter station using a neural network model based on the operating data of several groups of types of components in each zone, and to build a four-state model of the converter station components;

[0099] a calculation module, the calculation module being configured to calculate the power outage time and energy availability of the converter station transmission system based on a four-state model of converter station components, wherein the energy availability represents the degree to which its transmission capacity is restricted;

[0100] A risk level determination module is used to calculate the fault risk level of the converter station based on the energy availability and the consequence level of the risk.

[0101] An electronic device. Electronic device is intended to refer to various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device may also refer to various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided for example only and are not intended to limit the implementation of the inventions described and / or claimed herein.

[0102] Electronic devices include a computing unit that can perform various appropriate actions and processes based on computer programs stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). RAM can also store various programs and data required for the operation of the electronic device. The computing unit, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0103] Many components in an electronic device are connected to the I / O interface, including: input units, such as a keyboard and mouse; output units, such as various types of displays and speakers; storage units, such as magnetic disks and optical disks; and communication units, such as network cards, modems, and wireless communication transceivers. The communication unit allows the electronic device to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0104] The computing unit can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of computing units include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any suitable processors, controllers, microcontrollers, etc. The computing unit performs the various methods and processes described above, which can be implemented as computer software programs that are tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the computing unit, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit can be configured to perform the method by any other suitable means (e.g., by means of firmware).

[0105] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computer system may include clients and servers. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises from computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server incorporating a blockchain.

[0111] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make many possible changes and modifications to the technical solution of the present invention using the above technical content, or modify it into an equivalent embodiment with equivalent changes. Therefore, any changes, modifications, equivalent changes, and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the present technical solution.

Claims

1. A converter station intelligent operation control method, characterized by: The method includes: Real-time monitoring of the operating data of each section of the converter station, wherein the operating data includes the component operating data of each section; Calculate the central tendency and dispersion of converter station operation data, and classify the converter station operation data into several groups; Based on the operating data of several groups of components in each zone, a neural network model is used to determine the optimal state of each zone in the converter station, and a four-state model of the converter station components is constructed; The power outage time and energy availability of the converter station transmission system are calculated based on the four-state model of converter station components. The fault risk level of the converter station is calculated based on the energy availability and the consequence level of the risk, and early intervention is carried out according to the risk level.

2. The method for intelligent operation control of a converter station according to claim 1, characterized in that: The calculation of the central tendency and dispersion degree of the converter station operation data specifically includes the following steps: Calculate the average value of the converter station operation data, and calculate the variance of the converter station operation data according to the variance formula; The variance is set as the central tendency of the converter station operation data, and a trend identification code is assigned to the central tendency; pairing each data in the converter station operation data with the remaining data to form at least one pairing group; Calculate the absolute value of the difference between the two data in each paired group, and accumulate the absolute value of the difference between the two data in all paired groups to obtain the discrete value; The discrete value is set as the discrete degree of the converter station operation data, and a discrete identification code is assigned to the discrete degree.

3. The intelligent operation control method of a converter station according to claim 2, characterized in that: The step of dividing the converter station operation data into several groups specifically includes the following steps: Retrieve historical data classification criteria from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification criteria; Calculate the ratio of the central tendency of the converter station operation data to the central tendency mean of the sample data; Calculate the ratio of the discreteness of the converter station operation data to the mean discreteness of the sample data; Comparing the first ratio and the second ratio with a second threshold and a third threshold preset in the system respectively; If both the ratio 1 and the ratio 2 are less than or equal to the second threshold, it is determined to be the first category; If both ratio 1 and ratio 2 are greater than the second threshold but less than the third threshold, it is determined to be the second classification; If both the ratio 1 and the ratio 2 are greater than or equal to the third threshold, it is determined to be the third category.

4. The intelligent operation control method of a converter station according to claim 3, characterized in that: The four-state model of the converter station components is: ； Where, The probability that a component changes from a normal state to a state where an accident occurs but the fault is not removed; The probability that a component changes from a planned maintenance state to a repaired state after an accident removal; The planned maintenance rate of a component from a normal state to a planned maintenance state; 、 、 is the component repair rate; the four states of the converter station components are normal state (A), maintenance state (B), fault state (C), and repair state after fault removal (D). It is the component repair rate from the normal state to the state where the accident occurs but the fault is not removed; The repair rate of components when the components change from planned maintenance status to accident removal status; The repair rate of components when they change from normal status to planned maintenance status; In the stable state of the converter station system, the following relationship exists: ； Where, 、 、 、 are the probabilities of the component corresponding to states A, B, C and D respectively.

5. The method for intelligent operation control of a converter station according to claim 1, characterized in that: The formula for calculating the power outage time of the converter station transmission system is: ； Where i is the operating state of the converter station transmission system, i=1, 2,…, n; is the rated transmission capacity; is the outage capacity of operating state i; is the running time of running state i.

6. The intelligent operation control method of a converter station according to claim 5, characterized in that: The formula for calculating the energy availability rate in the converter station transmission system is: ； ； Where, is the total operating time of the converter station transmission system in each operating state; i is the operating state of the converter station transmission system, i=1, 2,…, n; is the running time of running state i; It is the power outage time of the converter station transmission system.

7. The intelligent operation control method of a converter station according to claim 6, characterized in that: The formula for calculating the fault risk level of the converter station based on the energy availability and the consequence level of the risk is: ； Where, Indicates the fault risk level of the converter station; Indicates the consequence level of the risk, The weight value is between (0,1); The energy availability in the transmission system of the converter station; The function rounds to an integer.

8. A converter station intelligent operation control system, used to implement a converter station intelligent operation control method according to any one of claims 1 to 7, characterized in that: include: A real-time monitoring module, which is used to monitor the operating data of each partition of the converter station in real time, wherein the operating data includes the component operating data of each partition; a classification module, the classification module being used to calculate the central tendency and dispersion degree of the converter station operation data, and to classify the converter station operation data into a plurality of groups, the categories including status information data of different components; a model building module, the model building module being used to determine the optimal state of each zone of the converter station using a neural network model based on the operating data of several groups of types of components in each zone, and to build a four-state model of the converter station components; a calculation module, the calculation module being configured to calculate the power outage time and energy availability of the converter station transmission system based on a four-state model of converter station components, wherein the energy availability represents the degree to which its transmission capacity is restricted; A risk level determination module is used to calculate the fault risk level of the converter station based on the energy availability and the consequence level of the risk.

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