Near-zero carbon port multi-energy integrated system and its self-consistent operation method and medium

Abstract

This invention discloses a near-zero carbon port multi-energy integrated system and its self-consistent operation method and medium, specifically relating to the field of wind power monitoring technology. By comprehensively analyzing load variation values, comprehensive wind power conversion values, and wind power conversion turbulence values, a wind power operation status assessment coefficient is calculated. This quantitatively analyzes the operating status of wind turbines to detect potential fault risks in advance, which is beneficial for the management of self-consistent operation of wind turbines. By considering the number of acceptable wind power operation signals generated within the comprehensive early warning set and the time interval between adjacent acceptable wind power operation signals, the reliability of fault prediction can be improved. By comprehensively analyzing and calculating the comprehensive wind power early warning value using the number of acceptable wind power operation signals generated within the comprehensive early warning set and the time interval between adjacent acceptable wind power operation signals, the operating status of wind turbines can be more comprehensively assessed, ensuring the normal operation of the wind power system in the near-zero carbon port multi-energy integrated system.

Description

Technical Field

[0001] This invention relates to the field of wind power monitoring technology, and more specifically, to a near-zero carbon port multi-energy fusion system and its self-consistent operation method and medium. Background Technology

[0002] A near-zero carbon port refers to a port that achieves near-zero annual carbon dioxide emissions during its operation period by optimizing transportation structures and processes, applying energy-saving and emission-reduction technologies, implementing clean energy substitution, and strengthening carbon emission management. Ports require significant amounts of electricity to operate various equipment, such as cranes, stacker cranes, lighting, and communication systems. This energy is used to power these devices and can also supply electricity to shore power facilities or the port's power grid. Clean energy sources include solar and wind power; for example, wind power generates electricity through wind turbines. Near-zero carbon ports integrate wind turbine power generation, solar photovoltaic panel power generation, and traditional grid power generation to form a multi-energy integrated power generation system. Employing clean energy sources such as wind and solar power can significantly reduce port carbon emissions, contributing to addressing climate change and reducing environmental impact.

[0003] However, in the actual utilization of wind energy, the operating status of the wind turbines used has a significant impact on the stability and availability of power production. In practice, alarms are often only issued and measures are taken after a wind turbine malfunctions or when significant instability in power production is detected. Inadequate monitoring of wind turbine operation and failure to provide early warnings about the operating status of wind turbines lead to reduced power generation efficiency, resulting in lost power production, affecting the stability of power supply, and ultimately reducing the availability of multi-energy integration in near-zero carbon ports.

[0004] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a near-zero carbon port multi-energy fusion system and its self-consistent operation method and medium to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The near-zero carbon port multi-energy integration system includes a data processing module, as well as an information acquisition module, a wind power operation judgment module, and a wind power comprehensive early warning module that are connected to the data processing module.

[0008] The information acquisition module collects abnormal bearing load information and sends it to the data processing module, which calculates the load variation value.

[0009] The information acquisition module collects wind energy conversion efficiency information and sends it to the data processing module. The data processing module calculates the comprehensive wind power conversion value and the wind power conversion turbulence value.

[0010] The data processing module normalizes the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient.

[0011] The wind power operation judgment module compares the wind power operation status evaluation coefficient with the first threshold and the second threshold of wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal.

[0012] The data processing module analyzes and calculates the comprehensive early warning ratio and density coefficient based on the generated acceptable wind power operation signals within the comprehensive early warning set.

[0013] The wind power integrated early warning module calculates the wind power integrated early warning value based on the integrated early warning ratio and the density coefficient; it compares the wind power integrated early warning value with the wind power integrated early warning threshold to generate a bad integrated early warning signal or a normal integrated early warning signal.

[0014] In a preferred embodiment, the self-consistent operation method of a near-zero carbon port multi-energy integrated system includes the following steps:

[0015] Step S1: Collect bearing load anomaly information, including load variation values; analyze the variation range of the radial load value on the bearings of the wind turbine and calculate the load variation value;

[0016] Step S2: Collect wind energy conversion efficiency information, which includes comprehensive wind power conversion value and wind power conversion turbulence value; calculate comprehensive wind power conversion value by analyzing actual power generation and theoretical power generation; calculate wind power conversion turbulence value by analyzing the stability of wind turbine power generation efficiency.

[0017] Step S3: Normalize the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient; compare the wind power operation status evaluation coefficient with the first threshold for wind power operation judgment and the second threshold for wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal.

[0018] Step S4: Analyze and calculate the comprehensive warning ratio and density coefficient for the generation of acceptable wind power operation signals within the comprehensive warning set; calculate the comprehensive wind power warning value based on the comprehensive warning ratio and density coefficient; compare the comprehensive wind power warning value with the comprehensive wind power warning threshold to generate a poor comprehensive warning signal or a normal comprehensive warning signal.

[0019] In a preferred embodiment, the specific logic for obtaining the load variation value in step S1 is as follows:

[0020] Set a load anomaly monitoring interval; detect load anomalies within the load anomaly monitoring interval. The radial load value of the bearings of a wind turbine is calculated; the load variation value is expressed as follows: ,in, This represents the number of radial load values ​​detected on the bearings of the wind turbine within the load anomaly monitoring interval. This refers to the number of the radial load value of the wind turbine bearings monitored within the load anomaly monitoring interval. , All are positive integers greater than or equal to 1; These are the load variation value and the load anomaly monitoring interval. The radial load value of the bearing of the monitored wind turbine and the load anomaly monitoring interval within the first monitored interval. The radial load value of the bearings of a wind turbine that was monitored.

[0021] In a preferred embodiment, in step S2, a wind power efficiency monitoring interval is set; the wind power efficiency monitoring interval is divided into equal parts. Calculate the theoretical power generation of each small interval; obtain the actual power generation of each small interval.

[0022] Calculate the comprehensive wind power conversion value: The comprehensive wind power conversion value is the ratio of the sum of the actual power generation of all inter-intervals within the wind power efficiency monitoring interval to the sum of the theoretical power generation of all inter-intervals within the wind power efficiency monitoring interval;

[0023] The specific logic for obtaining the wind power conversion turbulence value is as follows: The ratio of the actual power generation between small intervals to the theoretical power generation between small intervals is marked as the wind power efficiency value between small intervals; the average value of the wind power efficiency values ​​between small intervals within the wind power efficiency monitoring interval is calculated; discrete analysis is performed on the wind power efficiency values ​​corresponding to all small intervals within the wind power efficiency monitoring interval to calculate the wind power conversion turbulence value, the expression of which is: ,in, These represent the number of sub-intervals within the wind power efficiency monitoring interval and the inter-sub-interval numbering within the wind power efficiency monitoring interval. , All are positive integers greater than 1; These are respectively the wind power conversion turbulence value and the first value within the wind power efficiency monitoring interval. The wind power efficiency value of the corresponding interval and the average value of the wind power efficiency value of the interval within the wind power efficiency monitoring interval.

[0024] In a preferred embodiment, in step S3, the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value are normalized, and the wind power operation status evaluation coefficient is calculated using the normalized load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value.

[0025] Set a first threshold and a second threshold for judging wind power operation, wherein the first threshold for judging wind power operation is less than the second threshold for judging wind power operation.

[0026] When the wind power operation status evaluation coefficient is less than the first threshold for wind power operation judgment, a good wind power operation signal is generated; when the wind power operation status evaluation coefficient is greater than or equal to the first threshold for wind power operation judgment and less than or equal to the second threshold for wind power operation judgment, an acceptable wind power operation signal is generated; when the wind power operation status evaluation coefficient is greater than the second threshold for wind power operation judgment, a bad wind power operation signal is generated.

[0027] In a preferred embodiment, in step S4, if no bad wind power operation signal is generated in the comprehensive early warning set, and the number of times an acceptable wind power operation signal is generated in the comprehensive early warning set is greater than or equal to 2, an early warning analysis signal is generated.

[0028] When an early warning analysis signal is generated, the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set is obtained, and the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is obtained; the ratio of the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set to the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is marked as the comprehensive early warning ratio;

[0029] Obtain the time interval between adjacent acceptable wind power operation signals; set an interval threshold; obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set; obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set whose time intervals are less than the interval threshold; mark the ratio of the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set to the total number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set as the compactness coefficient;

[0030] The comprehensive early warning value for wind power is the product of the comprehensive early warning ratio and the density coefficient;

[0031] Set a comprehensive wind power early warning threshold; when the comprehensive wind power early warning value is greater than the comprehensive wind power early warning threshold, a comprehensive early warning bad signal is generated; when the comprehensive wind power early warning value is less than or equal to the comprehensive wind power early warning threshold, a comprehensive early warning normal signal is generated.

[0032] In a preferred embodiment, the self-consistent operating medium of the near-zero carbon port multi-energy fusion system is a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to execute the self-consistent operating method of the near-zero carbon port multi-energy fusion system described in any of the above-described technical solutions.

[0033] The technical effects and advantages of the near-zero carbon port multi-energy fusion system, its self-consistent operation method, and the medium of this invention are as follows:

[0034] 1. By comprehensively analyzing load variability, comprehensive wind power conversion value, and wind power conversion turbulence value, a wind power operation status assessment coefficient is calculated. This quantitative analysis of wind turbine operation status helps to identify potential fault risks in wind turbines in advance, enabling maintenance measures to be taken before problems become severe, reducing downtime and losses. Comparing the wind power operation status assessment coefficient with the first and second thresholds for wind power operation judgment generates different signals, allowing for a more accurate classification of wind turbine operation status. This facilitates the management of self-consistent wind turbine operation and helps ensure the reliable operation of near-zero carbon port multi-energy integrated systems.

[0035] 2. By generating a poor wind power operation signal within the comprehensive early warning set, early warnings of wind turbine operation status issues can be provided, reducing losses caused by faults. By considering the number of acceptable wind power operation signals generated within the comprehensive early warning set and the time interval between adjacent acceptable wind power operation signals, the reliability of fault prediction can be improved. By comprehensively analyzing and calculating the comprehensive wind power early warning value based on the number of acceptable wind power operation signals generated within the comprehensive early warning set and the time interval between adjacent acceptable wind power operation signals, a more comprehensive assessment of the wind turbine operation status can be achieved. Based on the generated comprehensive early warning poor signal or comprehensive early warning normal signal, an automatic decision can be made on whether to stop the wind turbine operation, ensuring the normal operation of the wind power system in the near-zero carbon port multi-energy integration system. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the self-consistent operation method of the near-zero carbon port multi-energy fusion system of the present invention;

[0037] Figure 2 This is a schematic diagram of the near-zero carbon port multi-energy fusion system of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0039] Example 1: Figure 2 A schematic diagram of the near-zero carbon port multi-energy fusion system of the present invention is given. The near-zero carbon port multi-energy fusion system includes a data processing module, an information acquisition module, a wind power operation judgment module, and a wind power comprehensive early warning module that are connected to the data processing module by signal.

[0040] The information acquisition module collects abnormal bearing load information and sends it to the data processing module, which then calculates the load variation value.

[0041] The information acquisition module collects wind energy conversion efficiency information and sends it to the data processing module. The data processing module calculates the comprehensive wind power conversion value and the wind power conversion turbulence value.

[0042] The data processing module normalizes the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient.

[0043] The wind power operation judgment module compares the wind power operation status evaluation coefficient with the first threshold and the second threshold of wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal.

[0044] The data processing module analyzes and calculates the comprehensive early warning ratio and density coefficient based on the generated acceptable wind power operation signals within the comprehensive early warning set.

[0045] The wind power integrated early warning module calculates the wind power integrated early warning value based on the integrated early warning ratio and the density coefficient; it compares the wind power integrated early warning value with the wind power integrated early warning threshold to generate a bad integrated early warning signal or a normal integrated early warning signal.

[0046] Example 2: The difference between Example 2 and Example 1 is that this example introduces a self-consistent operation method for a near-zero carbon port multi-energy fusion system.

[0047] Figure 1 A self-consistent operation method for the near-zero carbon port multi-energy fusion system of the present invention is provided, which includes the following steps:

[0048] Step S1: Collect bearing load anomaly information, including load variation value; analyze the variation range of the radial load value on the bearing of the wind turbine and calculate the load variation value.

[0049] Step S2: Collect wind energy conversion efficiency information, which includes the comprehensive wind power conversion value and the wind power conversion turbulence value; calculate the comprehensive wind power conversion value by analyzing the actual power generation and theoretical power generation; calculate the wind power conversion turbulence value by analyzing the stability of the power generation efficiency of the wind turbine.

[0050] Step S3: Normalize the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient; compare the wind power operation status evaluation coefficient with the first threshold for wind power operation judgment and the second threshold for wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal.

[0051] Step S4: Analyze and calculate the comprehensive warning ratio and density coefficient for the generation of acceptable wind power operation signals within the comprehensive warning set; calculate the comprehensive wind power warning value based on the comprehensive warning ratio and density coefficient; compare the comprehensive wind power warning value with the comprehensive wind power warning threshold to generate a poor comprehensive warning signal or a normal comprehensive warning signal.

[0052] In step S1, abnormal bearing load information is collected. Bearings are key components of wind turbines, responsible for supporting and limiting the movement of rotating parts. The real-time operating status of bearings is crucial to the reliability and performance of wind turbines.

[0053] Small, sporadic loads can usually be easily handled by bearings without causing serious problems. Bearings are typically designed with a certain amount of extra load capacity to handle momentary additional loads. However, if sporadic load changes are frequent or significant, they can cause problems such as bearing fatigue or shortened lifespan. Furthermore, if the load exceeds the bearing's rated load capacity, the bearing may be damaged or fail. By monitoring the load on the bearing, potential problems can be detected early.

[0054] Bearings are typically used to support the weight and inertial forces of rotating components, which are usually radial loads. Therefore, monitoring the impact of radial loads on the lifespan and performance of wind turbines is quite significant.

[0055] Bearing load anomaly information includes load variation values. The specific logic for obtaining load variation values ​​is as follows:

[0056] The load anomaly monitoring interval is set. The time length corresponding to the load anomaly monitoring interval is set by professionals in this field based on the actual monitoring needs of the wind turbine load. The time length corresponding to the load anomaly monitoring interval is a fixed value, but the range of the load anomaly monitoring interval changes with the real time. That is, a critical point of the load anomaly monitoring interval is always the real time. For example, if the real time is 07:20 and the time length of the load anomaly monitoring interval is set to 2 minutes, then the load anomaly monitoring interval monitors the time interval between 07:18 and 07:20.

[0057] Analyzing the load conditions of a single wind turbine: Anomalies were detected within the load anomaly monitoring range. The radial load value experienced by the bearings of a wind turbine. The magnitude is related to the frequency of monitoring the radial load value of the bearings of the wind turbine.

[0058] Radial load value refers to the load on a bearing in the direction perpendicular to the axis. A force sensor can be used to directly measure the radial load on the bearing. Force sensors are usually installed on the components supported by the bearing to measure the force in the direction perpendicular to the axis. The data output by the force sensor can be used to determine the radial load value.

[0059] The load anomaly monitoring range detected The variation of radial load on the bearings of a wind turbine is analyzed, and the load variation value is calculated. The expression is as follows: ,in, This represents the number of radial load values ​​detected on the bearings of the wind turbine within the load anomaly monitoring interval. This refers to the number of the radial load value of the wind turbine bearings monitored within the load anomaly monitoring interval. , All are positive integers greater than or equal to 1; These are the load variation value and the load anomaly monitoring interval. The radial load value of the bearing of the monitored wind turbine and the load anomaly monitoring interval within the first monitored interval. The radial load value of the bearings of a wind turbine that was monitored.

[0060] The greater the load variation value, the greater and more frequent the change in radial load value of the wind turbine bearing within the load anomaly monitoring range, and the greater the adverse impact on the operation of the wind turbine itself. The wind turbine may have rotor operation or mechanical failure, resulting in fluctuations in bearing load, which in turn affects the power output of the wind turbine.

[0061] In step S2, wind energy conversion efficiency information is collected. The wind energy conversion efficiency of a wind turbine refers to its efficiency in successfully converting wind energy into electrical energy. It reflects the efficiency and performance of the wind turbine when utilizing wind energy. The wind energy conversion efficiency reflects the efficiency of the wind turbine in converting wind energy into electrical energy. High efficiency means that the wind turbine can make fuller use of available wind resources.

[0062] Poor wind energy conversion efficiency means that a large amount of wind energy is not effectively converted into electrical energy. Poor wind energy conversion efficiency usually reflects the operating status and performance of wind turbines. Poor wind energy conversion efficiency may indicate that wind turbines fail to effectively convert wind energy into electrical energy at specific wind speeds. This may be caused by blade damage, turbine mechanical component failure, control system problems or other factors.

[0063] Wind energy conversion efficiency information includes the overall wind power conversion value and the wind power conversion turbulence value.

[0064] The wind power efficiency monitoring interval is set. The corresponding time length of the wind power efficiency monitoring interval is set by professionals in this field based on the actual monitoring needs of the efficiency of wind turbines in successfully converting wind energy into electrical energy. The corresponding time length of the wind power efficiency monitoring interval is a fixed value, but the range of the wind power efficiency monitoring interval changes with the real-time time. That is, a critical point of the wind power efficiency monitoring interval is always the real-time time. For example, if the real-time time is 07:20 and the time length of the wind power efficiency monitoring interval is set to 5 minutes, then the wind power efficiency monitoring interval monitors the time interval between 07:15 and 07:20.

[0065] Divide the wind power efficiency monitoring interval equally. The theoretical power generation of each small interval is calculated; the actual power generation of each small interval is obtained. The existing technology for obtaining the actual power generation of the small interval is relatively mature, so it will not be elaborated here.

[0066] Calculate the comprehensive wind power conversion value: The comprehensive wind power conversion value is the ratio of the sum of the actual power generation of all sub-intervals within the wind power efficiency monitoring interval to the sum of the theoretical power generation of all sub-intervals within the wind power efficiency monitoring interval. The higher the comprehensive wind power conversion value, the higher the efficiency of the wind turbine in converting wind energy into electricity; conversely, the lower the efficiency of the wind turbine in converting wind energy into electricity, the less conducive it is to the normal use of multi-energy integration in near-zero carbon ports.

[0067] The method for obtaining the theoretical power generation between communities is as follows:

[0068] Calculate the average wind speed for each interval, which is the average wind speed of the wind turbines within the interval; obtain the theoretical power generation corresponding to the average wind speed for each interval; multiply the theoretical power generation between intervals by the corresponding time length to calculate the theoretical power generation for each interval.

[0069] It is worth noting that the theoretical power generation corresponding to the average wind speed in each interval is obtained based on the power curve, which is existing technology and will not be elaborated further.

[0070] It is worth noting that, It should be as large as possible to better and more accurately reflect the average wind speed between communities.

[0071] The specific logic for obtaining the wind power conversion turbulence value is as follows: the ratio of the actual power generation between small intervals to the theoretical power generation between small intervals is marked as the wind power efficiency value of the small interval; that is, each small interval corresponds to a wind power efficiency value, and the average value of the wind power efficiency values ​​of the small intervals within the wind power efficiency monitoring interval is calculated; the wind power conversion turbulence value is calculated by performing discrete analysis on the wind power efficiency values ​​corresponding to all small intervals within the wind power efficiency monitoring interval, and its expression is: ,in, These represent the number of sub-intervals within the wind power efficiency monitoring interval and the inter-sub-interval numbering within the wind power efficiency monitoring interval. , All are positive integers greater than 1; These are respectively the wind power conversion turbulence value and the first value within the wind power efficiency monitoring interval. The wind power efficiency value of the corresponding interval and the average value of the wind power efficiency value of the interval within the wind power efficiency monitoring interval.

[0072] Among them, the average value of wind power efficiency of sub-intervals within the wind power efficiency monitoring interval is the ratio of the sum of wind power efficiency values ​​of all sub-intervals within the wind power efficiency monitoring interval to the number of sub-intervals.

[0073] The larger the wind power conversion turbulence value, the greater the deviation between wind power efficiency values ​​within the small interval of the wind power efficiency monitoring range. This means that the efficiency of wind turbines in converting wind energy into electrical energy is unstable. Unstable wind power conversion efficiency may lead to fluctuations in power output. This means that when near-zero carbon ports rely on power supplied by wind turbines, the reliability of power will decrease. Unstable power output may affect the normal operation of the port, especially during peak power demand periods. If the supply of clean energy is unstable, the port may need to rely on traditional energy to make up the gap, which will affect the carbon emission control target.

[0074] In step S3, the abnormal bearing load information and wind energy conversion efficiency information are comprehensively analyzed to analyze the operating status of the wind turbine and detect potential fault risks in advance.

[0075] The load variability value, comprehensive wind power conversion value, and wind power conversion turbulence value are normalized. The wind power operation status assessment coefficient is then calculated using these normalized values. For example, the present invention can use the following formula to calculate the wind power operation status assessment coefficient: ,in, These are the wind power operation status assessment coefficient, load variation value, comprehensive wind power conversion value, and wind power conversion turbulence value, respectively. These are the preset proportional coefficients for load variation value, comprehensive wind power conversion value, and wind power conversion turbulence value, respectively. Greater than 0, Less than 0.

[0076] The higher the wind power operation status assessment coefficient, the worse the wind turbine's operation status and the worse its wind power conversion capability, which leads to a reduction in the availability of multi-energy integration in near-zero carbon ports.

[0077] Set a first threshold and a second threshold for judging wind power operation, wherein the first threshold for judging wind power operation is less than the second threshold for judging wind power operation.

[0078] The wind power operation status assessment coefficient is compared with the first threshold and the second threshold for wind power operation judgment:

[0079] When the wind power operation status assessment coefficient is less than the first threshold for wind power operation judgment, a good wind power operation signal is generated. At this time, the wind turbine is operating normally and no action is required.

[0080] When the wind power operation status assessment coefficient is greater than or equal to the first threshold for wind power operation judgment, and less than or equal to the second threshold for wind power operation judgment, an acceptable wind power operation signal is generated. At this time, there may be a problem with the operation status of the wind turbine. However, a single occurrence will not affect the normal operation of the wind turbine. But too many acceptable wind power operation signals may indicate that the wind turbine may be at risk of imminent failure.

[0081] When the wind power operation status assessment coefficient exceeds the second threshold for wind power operation judgment, a wind power operation failure signal is generated. At this time, the wind turbine is in poor operating condition and cannot operate normally, which will affect the normal operation of the near-zero carbon port's multi-energy integration. Based on the generated wind power operation failure signal, the wind turbine is shut down, professional technicians are arranged to inspect and repair the wind turbine, and the power generation is switched to traditional grid power generation or other clean energy power generation to replace the wind turbine's power generation.

[0082] The first and second thresholds for judging wind power operation are set by professionals in the field based on the magnitude of the wind power operation status assessment coefficient and other practical situations such as the requirements for wind turbines in the multi-energy integration scenario of near-zero carbon ports. These will not be elaborated here.

[0083] In step S4, it is determined whether a bad wind power operation signal is generated within the comprehensive early warning set. If no bad wind power operation signal is generated within the comprehensive early warning set, and the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set is greater than or equal to 2, an early warning analysis signal is generated.

[0084] When a bad wind turbine operation signal is generated within the comprehensive early warning set, it indicates that the wind turbine has already experienced a significant malfunction. Since the number of acceptable wind turbine operation signals generated within the comprehensive early warning set is less than 2, the impact on the state of the wind turbine is small and has no significance for the state assessment of the wind turbine, so it is no longer considered.

[0085] When an early warning analysis signal is generated, the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set is obtained, and the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is obtained. The ratio of the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set to the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is marked as the comprehensive early warning ratio.

[0086] The time interval between adjacent acceptable wind turbine operation signals is obtained. The smaller the time interval between adjacent acceptable wind turbine operation signals, the closer the occurrence of a possible problem with the wind turbine's operating status.

[0087] An interval threshold is set based on the time interval between adjacent acceptable wind power operation signals and the actual safety requirements for the time interval between adjacent acceptable wind power operation signals. If the time interval between adjacent acceptable wind power operation signals is less than the interval threshold, it indicates that there is a high probability that there may be a problem with the wind turbine's operating status.

[0088] Obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set; obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set whose time intervals are less than the interval threshold; and label the ratio of the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set to the total number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set as the compactness coefficient.

[0089] With the same overall early warning ratio, the higher the density coefficient, the greater the probability of wind turbine failure.

[0090] Therefore, the comprehensive wind power early warning value is calculated as the product of the comprehensive early warning ratio and the compactness coefficient. The higher the comprehensive wind power early warning value, the greater the probability of wind turbine failure and the greater the adverse impact on the normal operation of near-zero carbon ports with multi-energy integration.

[0091] The wind power comprehensive early warning threshold is set by professionals in this field based on the magnitude of the wind power comprehensive early warning value and other actual conditions such as the required standards for the operating status of wind turbines. It will not be elaborated here.

[0092] Compare the comprehensive wind power early warning value and the comprehensive wind power early warning threshold:

[0093] When the comprehensive wind power warning value is greater than the comprehensive wind power warning threshold, a bad comprehensive warning signal is generated; when the comprehensive wind power warning value is less than or equal to the comprehensive wind power warning threshold, a normal comprehensive warning signal is generated.

[0094] When a comprehensive warning bad signal is generated, it indicates that there is a significant problem with the operation of the wind turbine within the comprehensive warning set. At this time, based on the generated comprehensive warning bad signal, the operation of the wind turbine is stopped, professional technicians are arranged to inspect and repair the wind turbine, and the power generation is switched to traditional grid power generation or other clean energy power generation to replace the power generation of the wind turbine.

[0095] A normal comprehensive early warning signal is generated, indicating that the operating status of the wind turbines within the comprehensive early warning set is within an acceptable range, and no action is required.

[0096] It is worth noting that the comprehensive early warning set includes generated wind power operation failure signals, wind power operation acceptable signals, and wind power operation normal signals. The total number of wind power operation failure signals, wind power operation acceptable signals, and wind power operation normal signals generated in the comprehensive early warning set is fixed, and the comprehensive early warning set collects the wind power operation failure signals, wind power operation acceptable signals, and wind power operation normal signals generated most recently in real time. The total number of wind power operation failure signals, wind power operation acceptable signals, and wind power operation normal signals generated in the comprehensive early warning set is set according to the actual monitoring needs of wind turbines, which will not be elaborated here.

[0097] Example 3: A self-consistent operating medium for a near-zero carbon port multi-energy fusion system, wherein the self-consistent operating medium for the near-zero carbon port multi-energy fusion system is a computer-readable storage medium storing multiple program codes, which are adapted to be loaded and run by a processor to execute the self-consistent operating method for the near-zero carbon port multi-energy fusion system described in any of the above-described technical solutions.

[0098] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0103] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A near-zero carbon port multi-energy integrated system, characterized in that, It includes a data processing module, as well as an information acquisition module, a wind power operation judgment module, and a wind power comprehensive early warning module that are connected to the data processing module. The information acquisition module collects abnormal bearing load information and sends it to the data processing module, which calculates the load variation value. The information acquisition module collects wind energy conversion efficiency information and sends it to the data processing module. The data processing module calculates the comprehensive wind power conversion value and the wind power conversion turbulence value. The data processing module normalizes the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient. The wind power operation judgment module compares the wind power operation status evaluation coefficient with the first threshold and the second threshold of wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal. The data processing module analyzes and calculates the comprehensive early warning ratio and density coefficient based on the generated acceptable wind power operation signals within the comprehensive early warning set. The wind power integrated early warning module calculates the wind power integrated early warning value based on the integrated early warning ratio and the density coefficient; The comprehensive wind power early warning value and the comprehensive wind power early warning threshold are compared to generate a comprehensive early warning bad signal or a comprehensive early warning normal signal.

2. A self-consistent operation method for a near-zero carbon port multi-energy integrated system, used to realize the near-zero carbon port multi-energy integrated system as described in claim 1, characterized in that, Includes the following steps: Step S1: Collect bearing load anomaly information, including load variation values; analyze the variation range of the radial load value on the bearings of the wind turbine and calculate the load variation value; Step S2: Collect wind energy conversion efficiency information, which includes comprehensive wind power conversion value and wind power conversion turbulence value; calculate comprehensive wind power conversion value by analyzing actual power generation and theoretical power generation; calculate wind power conversion turbulence value by analyzing the stability of wind turbine power generation efficiency. Step S3: Normalize the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value to calculate the wind power operation status evaluation coefficient; compare the wind power operation status evaluation coefficient with the first threshold for wind power operation judgment and the second threshold for wind power operation judgment to generate a good wind power operation signal, an acceptable wind power operation signal, or a bad wind power operation signal. Step S4: Analyze and calculate the comprehensive early warning ratio and compactness coefficient for the generation of acceptable wind power operation signals within the comprehensive early warning set, and calculate the comprehensive early warning value for wind power based on the comprehensive early warning ratio and compactness coefficient; The comprehensive wind power early warning value and the comprehensive wind power early warning threshold are compared to generate a comprehensive early warning bad signal or a comprehensive early warning normal signal.

3. The self-consistent operation method of the near-zero carbon port multi-energy integrated system according to claim 2, characterized in that, In step S1, the specific logic for obtaining the load variation value is as follows: Set a load anomaly monitoring interval; detect load anomalies within the load anomaly monitoring interval. The radial load value of the bearings of a wind turbine is calculated; the load variation value is expressed as follows: ,in, This represents the number of radial load values ​​detected on the bearings of the wind turbine within the load anomaly monitoring interval. This refers to the number of the radial load value of the wind turbine bearings monitored within the load anomaly monitoring interval. , All are positive integers greater than or equal to 1; These are the load variation value and the load anomaly monitoring interval. The radial load value of the bearing of the monitored wind turbine and the load anomaly monitoring interval within the first monitored interval. The radial load value of the bearings of a wind turbine that was monitored.

4. The self-consistent operation method of the near-zero carbon port multi-energy integrated system according to claim 2, characterized in that, In step S2, a wind power efficiency monitoring interval is set; the wind power efficiency monitoring interval is divided into equal parts. Calculate the theoretical power generation of each small interval; obtain the actual power generation of each small interval. Calculate the comprehensive wind power conversion value: The comprehensive wind power conversion value is the ratio of the sum of the actual power generation of all inter-intervals within the wind power efficiency monitoring interval to the sum of the theoretical power generation of all inter-intervals within the wind power efficiency monitoring interval; The specific logic for obtaining the wind power conversion turbulence value is as follows: The ratio of the actual power generation between small intervals to the theoretical power generation between small intervals is marked as the wind power efficiency value between small intervals; the average value of the wind power efficiency values ​​between small intervals within the wind power efficiency monitoring interval is calculated; discrete analysis is performed on the wind power efficiency values ​​corresponding to all small intervals within the wind power efficiency monitoring interval to calculate the wind power conversion turbulence value, the expression of which is: ,in, These represent the number of sub-intervals within the wind power efficiency monitoring interval and the inter-sub-interval numbering within the wind power efficiency monitoring interval. , All are positive integers greater than 1; These are respectively the wind power conversion turbulence value and the first value within the wind power efficiency monitoring interval. The wind power efficiency value of the corresponding interval and the average value of the wind power efficiency value of the interval within the wind power efficiency monitoring interval.

5. The self-consistent operation method of the near-zero carbon port multi-energy integrated system according to claim 2, characterized in that, In step S3, the load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value are normalized, and the wind power operation status evaluation coefficient is calculated using the normalized load variation value, the comprehensive wind power conversion value, and the wind power conversion turbulence value. Set a first threshold and a second threshold for judging wind power operation, wherein the first threshold for judging wind power operation is less than the second threshold for judging wind power operation. When the wind power operation status assessment coefficient is less than the first threshold for wind power operation judgment, a good wind power operation signal is generated. When the wind power operation status evaluation coefficient is greater than or equal to the first threshold for wind power operation judgment and less than or equal to the second threshold for wind power operation judgment, an acceptable wind power operation signal is generated; when the wind power operation status evaluation coefficient is greater than the second threshold for wind power operation judgment, a poor wind power operation signal is generated.

6. The self-consistent operation method of the near-zero carbon port multi-energy integrated system according to claim 2, characterized in that, In step S4, if no bad wind power operation signal is generated in the comprehensive early warning set, and the number of times an acceptable wind power operation signal is generated in the comprehensive early warning set is greater than or equal to 2, an early warning analysis signal is generated. When an early warning analysis signal is generated, the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set is obtained, and the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is obtained; the ratio of the number of times an acceptable wind power operation signal is generated within the comprehensive early warning set to the total number of acceptable wind power operation signals and good wind power operation signals generated within the comprehensive early warning set is marked as the comprehensive early warning ratio; Obtain the time interval between adjacent acceptable wind power operation signals; Set an interval threshold; Obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set; obtain the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set whose time intervals are less than the interval threshold; mark the ratio of the number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set to the total number of time intervals between adjacent acceptable wind power operation signals within the comprehensive early warning set as the compactness coefficient; The comprehensive early warning value for wind power is the product of the comprehensive early warning ratio and the density coefficient; Set a comprehensive early warning threshold for wind power; When the comprehensive wind power warning value exceeds the comprehensive wind power warning threshold, a bad comprehensive warning signal is generated. When the comprehensive wind power warning value is less than or equal to the comprehensive wind power warning threshold, a normal comprehensive warning signal is generated.

7. A self-consistent operating medium for a near-zero carbon port multi-energy fusion system, used to implement the self-consistent operation method of the near-zero carbon port multi-energy fusion system as described in any one of claims 2-6, characterized in that, The self-consistent operation medium of the near-zero carbon port multi-energy fusion system is a computer-readable storage medium that stores multiple program codes. These program codes are adapted to be loaded and run by a processor to execute the self-consistent operation method of the near-zero carbon port multi-energy fusion system described in any of the above-mentioned technical solutions.

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