Online monitoring method and system for key components of electrical equipment

Abstract

The invention relates to the technical field of key component monitoring, and discloses an electrical equipment key component online monitoring method and system, and the method comprises the steps: setting a plurality of monitoring points and a plurality of monitoring devices, and collecting real-time monitoring data based on the monitoring devices and a first-stage monitoring instruction; constructing and analyzing a plurality of real-time monitoring data sequences, calculating a state coefficient of a corresponding key part according to an analysis result, and setting an initial state identifier; setting a secondary monitoring instruction corresponding to the key component according to the initial state identifier, acquiring a monitoring data packet corresponding to the key component based on the secondary monitoring instruction, and calculating a compensation coefficient according to the monitoring data packet; a correction state coefficient is generated according to the compensation coefficient and the state coefficient of the corresponding key component, a key component monitoring report is generated according to the correction state coefficient, the monitoring precision is improved, potential fault hidden dangers are found in time, and the reliability and safety of a power system are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of key component monitoring, in particular to an online monitoring method and system for key components of electrical equipment. BACKGROUND

[0002] In a power system, the operation state of key parts such as bushings and cable heads of electrical equipment directly affects the safe and stable operation of the power system.

[0003] Traditional monitoring methods are mostly periodic inspection, which cannot grasp the operation state of the equipment in real time and is difficult to discover potential fault hidden dangers in time. Therefore, a method and system capable of monitoring the operation state of key parts of electrical equipment in real time and accurately are needed to improve the reliability and safety of the power system. SUMMARY

[0004] To solve the above technical problems, the application provides an online monitoring method and system for key components of electrical equipment, which sets multiple monitoring points and monitoring devices, collects real-time monitoring data, and generates an initial state identifier. According to the initial state identifier, a secondary monitoring instruction is set and a compensation coefficient is set. According to the compensation coefficient, a corrected state coefficient is obtained and a key component monitoring report is generated. The online monitoring of the key component is realized, the monitoring accuracy is improved, potential fault hidden dangers are discovered in time, and the reliability and safety of the power system are improved.

[0005] In some embodiments of the application, an online monitoring method for key components of electrical equipment is provided, which comprises: According to the historical parameters of the key components, multiple monitoring points are set, each monitoring point is provided with a plurality of monitoring devices, and real-time monitoring data of the corresponding monitoring point is collected based on the monitoring devices and the first monitoring instruction; According to the real-time monitoring data of the multiple monitoring points of the same key component, multiple real-time monitoring data sequences are constructed and analyzed, and the state coefficient of the corresponding key component is calculated according to the analysis result and the initial state identifier is set; According to the initial state identifier, the secondary monitoring instruction of the corresponding key component is set, the monitoring data packet of the corresponding key component is obtained based on the secondary monitoring instruction, and the compensation coefficient is calculated according to the monitoring data packet; According to the compensation coefficient and the state coefficient of the corresponding key component, a corrected state coefficient is generated, and a key component monitoring report is generated according to the corrected state coefficient.

[0006] Among them, the first monitoring instruction includes a plurality of monitoring periods and a preset monitoring time interval, the initial state identifier includes a normal state, an abnormal state and an unknown state, and the secondary monitoring instruction includes a first correction strategy, a second correction strategy and a maintenance strategy.

[0007] In some embodiments of the present application, a plurality of monitoring points are set according to historical parameters of key components, including: a plurality of historical operation requirements are obtained, and a plurality of demand categories are set according to the historical operation requirements; a historical application coefficient and a historical influence coefficient of each key component for each demand category are generated according to historical operation parameters of each key component for each demand category, and a historical importance coefficient of the corresponding key component for the corresponding demand category is generated in combination with the number of associated key components of the corresponding key component in the corresponding demand category; a key component sequence of the corresponding demand category is constructed based on the historical importance coefficient of each key component for the same demand category; a real-time operation requirement is obtained, and a demand category corresponding to the real-time operation requirement is determined; the number of monitoring points of the corresponding key component is set according to the historical importance coefficient of the key component in the key component sequence of the demand category corresponding to the real-time operation requirement; the monitoring points are set based on the number of monitoring points and the preset key point positions of each key component.

[0008] In some embodiments of the present application, an initial state identifier of the corresponding key component is generated according to the analysis result, including: a plurality of standard monitoring data intervals of each key component are set in advance; each real-time monitoring data in each real-time monitoring data sequence of the same monitoring point position of the same key component is compared with the corresponding standard monitoring data interval, a first data set of the corresponding real-time monitoring data sequence is constructed according to the real-time monitoring data in the standard monitoring data interval, and a second data set of the corresponding real-time monitoring data sequence is constructed according to the real-time monitoring data not in the standard monitoring data interval; a sub-state coefficient of the corresponding monitoring point position is calculated according to the first data set and the second data set of all real-time monitoring data sequences of the same monitoring point position; a state coefficient is generated according to the sub-state coefficients of all monitoring point positions of the same key component; the initial state identifier of the corresponding key component is set according to the state coefficient.

[0009] In some embodiments of the present application, the initial state identifier of the corresponding key component is set according to the state coefficient, including: the calculation formula of the state coefficient is: ; wherein, T is the state coefficient, n is the number of monitoring point positions of the corresponding key component, t0i is the sub-state coefficient of the i th monitoring point position, and a i is the weight coefficient of the i th monitoring point position; the calculation formula of the sub-state coefficient is: ; wherein t0 is a sub-state coefficient, w is a real-time monitoring data sequence number corresponding to a monitoring point, e1 is a state transition coefficient corresponding to the first data set, e2 is a state transition coefficient corresponding to the second data set, m1 is a data number in the first data set, m2 is a data number in the second data set, is a middle value of a standard monitoring data interval corresponding to the jth real-time monitoring data sequence, is the s1th real-time monitoring data in the first data set of the jth real-time monitoring data sequence, is a standard monitoring data threshold value of a standard monitoring data interval corresponding to the jth real-time monitoring data sequence, is the s2th real-time monitoring data in the second data set of the jth real-time monitoring data sequence; a first preset state coefficient threshold value and a second preset state coefficient threshold value ; when , the initial state identifier of the corresponding key component is set as an abnormal state; when , the initial state identifier of the corresponding key component is set as an unknown state; when , the initial state identifier of the corresponding key component is set as a normal state.

[0010] In some embodiments of the present application, the secondary monitoring instruction of the corresponding key component is set according to the initial state identifier, comprising: randomly selecting one key component with an initial state identifier as an unknown state in a current monitoring period as a target first key component; calculating a preset state coefficient middle value between the first preset state coefficient threshold value and the second preset state coefficient threshold value ; when , a first state coefficient difference value is calculated, and a first correction strategy is generated, wherein the first correction strategy comprises correcting a preset monitoring time interval of a remaining monitoring period of a current monitoring period of the target first key component according to the first state coefficient difference value ; ; when , a second state coefficient difference value is calculated, and a first correction strategy is generated, wherein the first correction strategy comprises correcting a preset monitoring time interval of a remaining monitoring period of a current monitoring period of the target first key component according to the second state coefficient difference value ; ; when a third state coefficient difference value is calculated and a first correction strategy is generated, the first correction strategy including correcting a preset monitoring time interval of a remaining monitoring period of a current monitoring cycle of the target first key component according to the third state coefficient difference value , wherein ; when a fourth state coefficient difference value is calculated and a first correction strategy is generated, the first correction strategy including correcting a preset monitoring time interval of a remaining monitoring period of a current monitoring cycle of the target first key component according to the fourth state coefficient difference value , wherein ; a first correction strategy of each key component with an initial state identifier being an unknown state is generated in turn.

[0011] In some embodiments of the present application, the secondary monitoring instruction of the corresponding key component is set according to the initial state identifier, and further includes: randomly selecting one key component with an initial state identifier being a normal state in the current monitoring cycle as a target second key component; obtaining data variation characteristics of each monitoring point of the target second key component in the current monitoring cycle, the data variation characteristics including a data variation trend, a data variation rate and a data variation value; generating a data variation evaluation value of the target second key component according to the data variation characteristics of all monitoring points of the target second key component in the current monitoring cycle; pre-setting a first preset data variation evaluation value threshold and a second preset data variation evaluation value threshold; if the data variation evaluation value is less than the first preset data variation evaluation value threshold, a first data variation evaluation value difference value is calculated, and the second correction strategy includes correcting a preset monitoring time interval of a next monitoring cycle according to the first data variation evaluation value difference value; (increasing the preset monitoring time interval); if the data variation evaluation value is between the first preset data variation evaluation value threshold and the second preset data variation evaluation value threshold, a data variation evaluation value threshold median value is calculated, and a second data variation evaluation value difference value is generated according to the data variation evaluation value and the data variation evaluation value threshold median value; the second correction strategy includes correcting a preset monitoring time interval of a next monitoring cycle according to the second data variation evaluation value difference value; If the data change evaluation value is greater than a second preset data change evaluation value threshold, a third data change evaluation value difference is calculated, and the second correction strategy includes correcting a preset monitoring time interval of a remaining monitoring period of the current monitoring period according to the third data change evaluation value difference; The second correction strategy of each key component with the initial state identifier being the normal state is sequentially generated.

[0012] In some embodiments of the present application, the secondary monitoring instruction of the corresponding key component is set according to the initial state identifier, and the method further includes: A key component with the initial state identifier being the abnormal state in the current monitoring period is randomly selected as a target third key component; The state coefficient and the corresponding abnormal type of the target third key component are analyzed based on the abnormal component reference library to obtain a similarity; The abnormal component reference library includes preset abnormal types of a plurality of key components, each preset abnormal type is mapped with a plurality of preset state coefficients, and each preset state coefficient is associated with a plurality of preset influence characteristic parameters and a preset maintenance strategy; The preset maintenance strategy corresponding to the maximum similarity is extracted and set as the maintenance strategy of the target third key component; The maintenance strategy of each key component with the initial state identifier being the abnormal state is sequentially generated.

[0013] In some embodiments of the present application, the compensation coefficient is calculated according to the monitoring data packet, including: The real-time monitoring data of the remaining monitoring period is collected according to the preset monitoring time interval corrected by the first correction strategy to generate the monitoring data packet of the key component with the unknown state; A plurality of risk evaluation indexes are preset; The monitoring data packet of each key component with the unknown state is evaluated based on the plurality of risk evaluation indexes to generate an abnormal risk evaluation value; The compensation coefficient of the corresponding key component with the unknown state is set according to the abnormal risk evaluation value; If the second correction strategy is to correct the preset monitoring time interval of the remaining monitoring period of the current monitoring period, the monitoring data packet of the key component with the normal state is generated by collecting the real-time monitoring data of the remaining monitoring period according to the preset monitoring time interval corrected by the second correction strategy; A plurality of deviation evaluation indexes are preset; The monitoring data packet of each key component with the normal state is evaluated based on the plurality of deviation evaluation indexes to generate a data deviation evaluation value; The compensation coefficient of the corresponding key component with the normal state is set according to the data deviation evaluation value; If the second correction strategy is to correct the preset monitoring time interval of the next monitoring period, the curve trend of the current monitoring period is extrapolated to obtain a predicted data change curve of the next monitoring period; The predicted data change curve includes predicted monitoring data at a plurality of preset monitoring time intervals corrected by the second correction strategy; A monitoring data packet of the key component in the normal state is generated according to a plurality of predicted monitoring data in the predicted data change curve; The monitoring data packet of the key component in the normal state is evaluated based on a plurality of deviation evaluation indexes to generate a predicted data deviation evaluation value; The predicted data deviation evaluation value is corrected according to the curve fluctuation degree in the predicted data change curve, and a compensation coefficient corresponding to the key component in the normal state is set according to the corrected predicted data deviation evaluation value; A monitoring data packet of the key component in the abnormal state is generated according to a plurality of preset influence characteristics mapped by the preset maintenance strategy corresponding to the maximum similarity; A plurality of influence evaluation indexes are preset; An influence evaluation value is generated by evaluating the monitoring data packet of the key component in the abnormal state based on a plurality of influence evaluation indexes; A compensation coefficient corresponding to the key component in the abnormal state is set according to the influence evaluation value.

[0014] In some embodiments of the present application, the key component monitoring report is generated according to the corrected state coefficient, including: The state coefficient of the corresponding key component is corrected according to the compensation coefficient to obtain a corrected state coefficient; The final state identifier of the corresponding key component is determined according to the corrected state coefficient, and the final state identifier includes a normal state and an abnormal state; The key component monitoring report of the current monitoring period is generated according to the final state identifier of each key component, and the key component monitoring report includes a plurality of key components in abnormal states, corresponding maintenance strategies, and maintenance sequences.

[0015] In some embodiments of the present application, an online monitoring system for key components of an electrical equipment is further included, comprising: A setting module is configured to set a plurality of monitoring points according to historical parameters of key components, each monitoring point is provided with a plurality of monitoring devices, and real-time monitoring data of the corresponding monitoring point is collected based on the monitoring devices and a first-level monitoring instruction; An analysis module is configured to construct a plurality of real-time monitoring data sequences and analyze the real-time monitoring data sequences according to a plurality of monitoring points of the same key component, calculate a state coefficient of the corresponding key component according to the analysis result, and set an initial state identifier; The calculation module is used to set the secondary monitoring instructions for the corresponding key components according to the initial state identifier, obtain the monitoring data packets of the corresponding key components based on the secondary monitoring instructions, and calculate the compensation coefficient based on the monitoring data packets. The generation module is used to generate corrected state coefficients based on the compensation coefficients and the state coefficients of the corresponding key components, and to generate key component monitoring reports based on the corrected state coefficients.

[0016] The online monitoring method and system for key components of electrical equipment according to embodiments of this application have the following advantages compared with the prior art: By setting up multiple monitoring points and monitoring equipment, real-time monitoring data is collected and an initial state identifier is generated. Based on the initial state identifier, secondary monitoring instructions are set and compensation coefficients are set. Based on the compensation coefficients, the corrected state coefficients are obtained and a key component monitoring report is generated, thereby realizing online monitoring of key components, improving monitoring accuracy, timely detection of potential fault hazards, and improving the reliability and safety of the power system. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an online monitoring method for key components of electrical equipment according to an embodiment of this application; Figure 2 This is a schematic diagram of an online monitoring system for key components of electrical equipment according to an embodiment of this application. Detailed Implementation

[0018] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0019] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] like Figure 1 As shown in the figure, an online monitoring method for key components of electrical equipment according to an embodiment of this application includes: Step S101: Set multiple monitoring points according to the historical parameters of key components. Each monitoring point is equipped with several monitoring devices. Collect real-time monitoring data of the corresponding monitoring point based on the monitoring devices and the first-level monitoring instructions. Step S102: Construct and analyze multiple real-time monitoring data sequences based on real-time monitoring data from multiple monitoring points of the same key component, calculate the state coefficient of the corresponding key component based on the analysis results, and set the initial state identifier. Step S103: Set the secondary monitoring instructions for the corresponding key components according to the initial state identifier, obtain the monitoring data packets for the corresponding key components based on the secondary monitoring instructions, and calculate the compensation coefficient based on the monitoring data packets; Step S104: Generate corrected state coefficients based on the compensation coefficients and the state coefficients of the corresponding key components, and generate a key component monitoring report based on the corrected state coefficients.

[0023] The first-level monitoring instructions include several monitoring cycles and preset monitoring time intervals; the initial state identifiers include normal state, abnormal state, and unknown state; and the second-level monitoring instructions include a first correction strategy, a second correction strategy, and a maintenance strategy.

[0024] In this embodiment, the monitoring equipment includes, but is not limited to, a temperature sensor, a high-voltage bushing partial discharge sensor based on the principle of electromagnetic induction, and an acoustic fingerprint sensor.

[0025] In this embodiment, the real-time monitoring data sequence is constructed based on the real-time monitoring data of the same data category at the same time from multiple monitoring points of the same key component. For example, when the data category is temperature, the real-time monitoring data sequence includes the real-time temperature data at all monitoring points of the same key component.

[0026] In this embodiment, when the state is unknown, the preset time interval is corrected and the first monitoring data packet is obtained. An abnormal risk coefficient is generated based on the first monitoring data packet. When the state is normal, the preset time interval is corrected based on the real-time monitoring data change characteristics of the current monitoring period. The real-time monitoring data of the next monitoring period is obtained based on the corrected preset time interval. When the state is abnormal, an abnormal coefficient of the corresponding key component is generated.

[0027] In some embodiments of this application, multiple monitoring points are set based on historical parameters of key components, including: Obtain several historical operational requirements, and set multiple requirement categories based on these historical operational requirements; Based on the historical operating parameters of each key component for each demand category, the historical application coefficient and historical impact coefficient of the corresponding key component for each demand category are generated. Combined with the number of associated key components of the corresponding key component in the corresponding demand category, the historical importance coefficient of the corresponding key component for the corresponding demand category is generated. Based on the historical importance coefficient of each key component to the same demand category, construct a sequence of key components for the corresponding demand category; Obtain real-time operational requirements and determine the corresponding requirement categories; The number of monitoring points for each key component is set according to the historical importance coefficient of the key components in the key component sequence of the demand category corresponding to real-time operation requirements. Monitoring points are set based on the number of monitoring points and the preset key points of each key component.

[0028] In this embodiment, the sequence of key components for each demand category is obtained by sorting each key component in descending order of its historical importance coefficient for the corresponding demand category. By evaluating the application frequency, application degree, impact degree, and number of associated key components that affect the operating status of other key components for each key component in different demand categories, the corresponding historical importance coefficient is obtained, indicating that the greater the importance of the corresponding key component, the smaller the importance.

[0029] In this embodiment, the total number of monitoring points for each demand category is set in advance. The total number of monitoring points is allocated according to the historical importance coefficient of each key component in the key component sequence to obtain the number of monitoring points for each key component. The preset key points are pre-set and refer to the important or easily abnormal points in each key component.

[0030] In this embodiment, by determining the demand category corresponding to the real-time operation requirements, the number of monitoring points for each key component under the real-time operation requirements is determined, and the monitoring points are set, laying the foundation for subsequent online monitoring and improving the accuracy of monitoring and the accuracy of judging the operating status of each key component.

[0031] In some embodiments of this application, an initial state identifier for a corresponding key component is generated based on the analysis results, including: Pre-set several standard monitoring data ranges for each key component; Each real-time monitoring data in each real-time monitoring data sequence of the same monitoring point of the same key component is compared with the corresponding standard monitoring data interval. A first data set of the corresponding real-time monitoring data sequence is constructed based on the real-time monitoring data that is within the standard monitoring data interval, and a second data set of the corresponding real-time monitoring data sequence is constructed based on the real-time monitoring data that is not within the standard monitoring data interval. The sub-state coefficient of the corresponding monitoring point is calculated based on the first and second data sets of all real-time monitoring data sequences of the same monitoring point. The state coefficient is generated based on the sub-state coefficients of all monitoring points of the same key component; The initial state identifier of the corresponding key component is set according to the state coefficient.

[0032] In some embodiments of this application, the initial state identifier of the corresponding key component is set according to the state coefficient, including: The formula for calculating the state coefficient is as follows: ; Where T is the state coefficient, n is the number of monitoring points for the corresponding key components, t0i is the sub-state coefficient of the i-th monitoring point, and ai is the weight coefficient of the i-th monitoring point. The formula for calculating the sub-state coefficients is as follows: ; Where t0 is the sub-state coefficient, w is the number of real-time monitoring data sequences for the corresponding monitoring point, e1 is the state transition coefficient corresponding to the first data set, e2 is the state transition coefficient corresponding to the second data set, m1 is the number of data in the first data set, and m2 is the number of data in the second data set. Let be the median value of the standard monitoring data interval corresponding to the j-th real-time monitoring data sequence. For the j-th real-time monitoring data, the s1-th real-time monitoring data is in the first data set of the j-th real-time monitoring data sequence. The standard monitoring data threshold is the standard monitoring data range corresponding to the j-th real-time monitoring data sequence. The s2nd real-time monitoring data in the second data set of the j-th real-time monitoring data sequence; Preset the first preset state coefficient threshold Second preset state coefficient threshold ; when At that time, the initial status flag of the corresponding key component is set as an abnormal state; when At that time, the initial state flag of the corresponding key component is set to unknown; when At that time, the initial status flag of the corresponding key component is set to normal state.

[0033] In this embodiment, the standard monitoring data threshold is calculated based on the closest endpoint value between the s2th real-time monitoring data in the second data set and the standard monitoring data interval.

[0034] In this embodiment, the state transition coefficient refers to converting the absolute values ​​of the data differences in the first data set and the data differences in the second data set into values ​​with the same dimension as the state coefficient. When the absolute value of the data difference between the median value of the standard monitoring data interval and the s1th real-time monitoring data in the first data set is smaller, the corresponding sub-state coefficient is larger, and vice versa. When the absolute value of the data difference between the standard monitoring data threshold and the s2th real-time monitoring data in the second data set is smaller, the corresponding sub-state coefficient is larger, and vice versa.

[0035] In this embodiment, by calculating the sub-state coefficient of each monitoring point of the same key component, the state coefficient of the corresponding key component is generated, thereby generating an initial state identifier. Based on the initial state identifier, a secondary monitoring strategy is set to further improve the monitoring accuracy of the key component, promptly detect abnormal states of the key component, and formulate reasonable operation and maintenance strategies to ensure the operational stability of electrical equipment to meet the operational requirements.

[0036] In some embodiments of this application, secondary monitoring instructions for corresponding key components are set according to the initial state identifier, including: Randomly select a key component whose initial state is identified as unknown during the current monitoring period as the first key component of the target; Calculate the median of the preset state coefficients between the first preset state coefficient threshold and the second preset state coefficient threshold. ; when At that time, calculate the difference in the first state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the first state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the second state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the second state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the third state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the third state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the fourth state coefficients. And generate a first correction strategy, the first correction strategy including based on the fourth state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; The first correction strategy is generated sequentially for each critical component whose initial state is identified as unknown.

[0037] In this embodiment, based on the first state coefficient difference When setting the correction factor for the preset monitoring time interval for the remaining monitoring period, when The smaller the value, the larger the corresponding correction factor, and vice versa. The range of the corresponding correction factor is (0.55, 0.7).

[0038] In this embodiment, based on the difference in the second state coefficients When setting the correction factor for the preset monitoring time interval for the remaining monitoring period, when The larger the value, the larger the corresponding correction factor, and vice versa. The range of the corresponding correction factor is (0.4, 0.55).

[0039] In this embodiment, based on the difference in the third state coefficients When setting the correction factor for the preset monitoring time interval for the remaining monitoring period, when The larger the value, the larger the corresponding correction factor, and vice versa. The range of the corresponding correction factor is (0.7, 0.85).

[0040] In this embodiment, based on the fourth state coefficient difference When setting the correction factor for the preset monitoring time interval for the remaining monitoring period, when The larger the value, the smaller the corresponding correction factor, and vice versa. The range of the corresponding correction factor is (0.85, 1).

[0041] In this embodiment, the state coefficients of key components in unknown states are compared with the first preset state coefficient threshold and the second preset state coefficient threshold. The comparison results are refined, and the value range of the corresponding correction coefficients is configured. This ensures that the preset monitoring time interval for the remaining monitoring period of the current monitoring cycle is adjusted, thereby improving the monitoring accuracy of key components. This lays the foundation for the subsequent calculation of the risk assessment value of the corresponding key components and ensures the timeliness of the operation and maintenance of key components.

[0042] In this embodiment, by setting a first correction strategy to correct the monitoring time interval of key components in unknown states, it is possible to further determine whether there are any abnormalities or potential fault hazards, thereby formulating reasonable operation and maintenance strategies or repair strategies, improving the monitoring accuracy of key components and the operational stability of electrical equipment.

[0043] In some embodiments of this application, setting secondary monitoring instructions for corresponding key components based on initial state identifiers further includes: Randomly select a key component whose initial state is marked as normal in the current monitoring cycle as the second key component of the target; Acquire the data change characteristics of each monitoring point of the target's second key component in the current monitoring cycle, wherein the data change characteristics include data change trend, data change rate, and data change magnitude. Generate a data change evaluation value for the second key component of the target based on the data change characteristics of all monitoring points of the target's second key component in the current monitoring period; Pre-set a first preset data change evaluation value threshold and a second preset data change evaluation value threshold; If the data change evaluation value is less than the first preset data change evaluation value threshold, the difference of the first data change evaluation value is calculated. The second correction strategy includes correcting the preset monitoring time interval of the next monitoring cycle based on the difference of the first data change evaluation value (increasing the preset monitoring time interval). If the data change evaluation value is between the first preset data change evaluation value threshold and the second preset data change evaluation value threshold, calculate the median of the data change evaluation value threshold, and generate the second data change evaluation value difference based on the data change evaluation value and the median of the data change evaluation value threshold. The second correction strategy includes correcting the preset monitoring time interval for the next monitoring cycle based on the difference in the evaluation value of the second data change; If the data change evaluation value is greater than the second preset data change evaluation value threshold, the third data change evaluation value difference is calculated. The second correction strategy includes correcting the preset monitoring time interval of the remaining monitoring period of the current monitoring cycle based on the third data change evaluation value difference. A second correction strategy is generated sequentially for each critical component whose initial state is identified as normal.

[0044] In this embodiment, the more stable the data change trend, the slower the data change rate, and the smaller the data change value, the smaller the corresponding data change evaluation value, which means that the corresponding monitoring data is more stable and the probability of anomalies is smaller.

[0045] In this embodiment, the first preset data change evaluation value threshold is less than the second preset data change evaluation value threshold.

[0046] In this embodiment, the difference in the first data change evaluation value = the first preset data change evaluation value threshold - the data change evaluation value. When the difference in the first data change evaluation value is smaller, the corresponding correction coefficient is larger, and vice versa. The range of the corresponding correction coefficient is (1, 1.25).

[0047] In this embodiment, the difference in the second data change evaluation value = the data change evaluation value - the median of the preset data change evaluation value threshold. When the difference in the second data change evaluation value is larger, the corresponding correction coefficient is smaller, and vice versa. The range of the corresponding correction coefficient is (0.9, 1).

[0048] In this embodiment, the difference in the third data change evaluation value = the data change evaluation value - the second preset data change evaluation value threshold. When the third data change evaluation value is larger, the corresponding correction coefficient is smaller, and vice versa. The value range of the corresponding correction coefficient is (0.8, 1).

[0049] In this embodiment, the remaining time period of the current monitoring cycle or the preset monitoring time interval of the next monitoring cycle of the target second key component is corrected by calculating the data change evaluation value. The duration of the monitoring cycle can also be adjusted to improve the accuracy and timeliness of monitoring the state changes of key components under normal conditions and to promptly detect potential fault hazards.

[0050] In this embodiment, the larger the data change evaluation value, the greater the fluctuation and instability of the real-time monitoring data in the current monitoring period, thus increasing the probability of potential fault risks. By setting a second correction strategy, the monitoring accuracy of key components in normal state is improved, potential fault hazards are detected in time, and the reliability and safety of the power system are improved.

[0051] In some embodiments of this application, setting secondary monitoring instructions for corresponding key components based on initial state identifiers further includes: Randomly select a key component whose initial state is marked as abnormal in the current monitoring period as the target's third key component; Based on the abnormal component reference library, a similarity analysis is performed on the state coefficients and corresponding abnormal types of the target's third key component to obtain the similarity score. The abnormal component reference library includes several preset abnormal types of key components. Each preset abnormal type is mapped to several preset state coefficients, and each preset state coefficient is associated with several preset influence characteristic parameters and preset maintenance strategies. Extract the preset maintenance strategy corresponding to the maximum similarity and set it as the maintenance strategy for the target third key component; Maintenance strategies are generated sequentially for each critical component whose initial state is marked as abnormal.

[0052] In this embodiment, the preset impact characteristic parameters include, but are not limited to, the number and extent of the related key components affected by the abnormal state, and the degree of impact on real-time operation requirements.

[0053] In this embodiment, the maintenance strategy refers to the timely maintenance of critical components in case of abnormal conditions, in order to prevent the fault from escalating, reduce maintenance costs, and ensure the safe operation of electrical equipment.

[0054] In some embodiments of this application, the compensation coefficient is calculated based on the monitoring data packet, including: Real-time monitoring data for the remaining monitoring period is collected according to the preset monitoring time interval after correction according to the first correction strategy, and a monitoring data package for key components in unknown state is generated. Several risk assessment indicators are pre-defined; The monitoring data packets of each key component in an unknown state are evaluated based on several risk assessment indicators to generate an abnormal risk assessment value. The compensation coefficients for key components in the unknown state are set according to the abnormal risk assessment values; If the second correction strategy is to correct the preset monitoring time interval for the remaining monitoring period of the current monitoring cycle, real-time monitoring data for the remaining monitoring period is collected according to the preset monitoring time interval corrected by the second correction strategy, and a monitoring data packet for key components in normal state is generated. Several deviation evaluation indicators are pre-defined; The monitoring data packets of each key component in normal condition are evaluated based on several deviation evaluation indicators to generate data deviation evaluation values. Set compensation coefficients for key components in normal condition based on data deviation evaluation values; If the second correction strategy is to correct the preset monitoring time interval for the next monitoring period and extrapolate the curve trend of the current monitoring period to obtain the predicted data change curve for the next monitoring period; The predicted data change curve includes predicted monitoring data at several preset monitoring time intervals after correction by the second correction strategy; Based on several predictive monitoring data points from the predicted data change curve, a monitoring data package for key components in normal condition is generated. The monitoring data packets of key components in normal condition are evaluated based on several deviation evaluation indicators to generate predicted data deviation evaluation values. The prediction data deviation evaluation value is corrected based on the degree of fluctuation in the prediction data change curve, and the compensation coefficient of the key components corresponding to the normal state is set based on the corrected prediction data deviation evaluation value. Based on the preset impact features mapped by the preset maintenance strategy corresponding to the maximum similarity, a monitoring data packet for key components in abnormal state is generated. Several impact assessment indicators are pre-defined; The monitoring data packets of key components in abnormal states are evaluated based on several impact evaluation indicators to generate impact evaluation values. The compensation coefficients for key components in the corresponding abnormal state are set according to the impact evaluation values.

[0055] In this embodiment, the risk assessment index is constructed based on the historical monitoring data of potential failure risks of key components and the corresponding historical monitoring data change characteristics. By setting multiple potential failure risks and performing similarity analysis with the real-time monitoring data collected in the remaining monitoring period of the current monitoring period and the real-time monitoring data change characteristics, the risk assessment value is obtained. That is, the greater the similarity with the historical monitoring data of potential failure risks, the greater the risk assessment value, and the smaller the set compensation coefficient, and vice versa. The corresponding compensation coefficient ranges from (0.85 to 1.15).

[0056] In this embodiment, the deviation evaluation index refers to the data deviation value, data deviation degree, and deviation rate between the real-time monitoring data of the remaining monitoring period of the current monitoring cycle and the corresponding standard monitoring data interval. That is, the larger the data deviation value, data deviation degree, and deviation rate, the larger the corresponding deviation evaluation value, and the smaller the set compensation coefficient, and vice versa. The corresponding compensation coefficient ranges from (0.9 to 1.1).

[0057] In this embodiment, the impact evaluation index refers to the degree of abnormality of the key components under abnormal conditions and the degree of impact of the abnormality type on other key components, the degree of impact on the current operational requirements, and the degree of impact on the component lifespan. When the degree of impact is greater, the corresponding impact evaluation value is greater and the set compensation coefficient is smaller, and vice versa. The corresponding compensation coefficient ranges from 0.8 to 1.

[0058] In this embodiment, by setting compensation coefficients for key components with different status indicators, the significance of potential fault risks and the rationality of operation and maintenance of faults that have occurred are improved, thereby enhancing the operational stability of electrical equipment and the online monitoring efficiency of various key components.

[0059] In some embodiments of this application, a critical component monitoring report is generated based on a modified state coefficient, including: The state coefficients of the corresponding key components are corrected based on the compensation coefficients to obtain the corrected state coefficients. The final status identifier of the corresponding key component is determined based on the corrected status coefficient. The final status identifier includes normal status and abnormal status. A critical component monitoring report for the current monitoring cycle is generated based on the final status identifier of each critical component. The critical component monitoring report includes several critical components in abnormal states, the corresponding maintenance strategies, and the maintenance sequence.

[0060] In this embodiment, the maintenance sequence is set according to the correction state coefficient and the weight coefficient of the corresponding key component, and arranged from large to small.

[0061] In this embodiment, by calculating the corrected state coefficient and determining the final state identifier, the abnormal state includes the state that is already abnormal in the current monitoring cycle and the state that has potential fault hazards and will cause an abnormality in the current monitoring cycle.

[0062] In this embodiment, by generating a key component monitoring report, key components with abnormal states and potential fault hazards in the current monitoring cycle are screened out, and corresponding maintenance strategies are configured and maintenance sequences are set to ensure the timeliness and reliability of operation and maintenance, thereby improving the reliability and safety of the power system.

[0063] In some embodiments of this application, such as Figure 2 As shown, it also includes an online monitoring system for key components of electrical equipment: The setting module is used to set multiple monitoring points based on the historical parameters of key components. Each monitoring point is equipped with several monitoring devices, and real-time monitoring data of the corresponding monitoring point is collected based on the monitoring devices and the first-level monitoring instructions. The analysis module is used to construct and analyze multiple real-time monitoring data sequences based on real-time monitoring data from multiple monitoring points of the same key component, calculate the state coefficient of the corresponding key component based on the analysis results, and set the initial state identifier. The calculation module is used to set the secondary monitoring instructions for the corresponding key components according to the initial state identifier, obtain the monitoring data packets of the corresponding key components based on the secondary monitoring instructions, and calculate the compensation coefficient based on the monitoring data packets. The generation module is used to generate corrected state coefficients based on the compensation coefficients and the state coefficients of the corresponding key components, and to generate key component monitoring reports based on the corrected state coefficients.

[0064] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for online monitoring of key components of electrical equipment, characterized in that, include: Multiple monitoring points are set according to the historical parameters of key components. Each monitoring point is equipped with several monitoring devices. Real-time monitoring data of the corresponding monitoring point is collected based on the monitoring devices and the first-level monitoring instructions. Multiple real-time monitoring data sequences are constructed and analyzed based on real-time monitoring data from multiple monitoring points of the same key component. The state coefficients of the corresponding key components are calculated based on the analysis results, and initial state identifiers are set. The secondary monitoring instructions for the corresponding key components are set according to the initial state identifier. The monitoring data packets for the corresponding key components are obtained based on the secondary monitoring instructions. The compensation coefficient is calculated based on the monitoring data packets. A corrected state coefficient is generated based on the compensation coefficient and the state coefficient of the corresponding key component; a key component monitoring report is then generated based on the corrected state coefficient. The first-level monitoring instructions include several monitoring cycles and preset monitoring time intervals, while the second-level monitoring instructions include a first correction strategy, a second correction strategy, and a maintenance strategy.

2. The online monitoring method for key components of electrical equipment as described in claim 1, characterized in that, Multiple monitoring points are set based on the historical parameters of key components, including: Obtain several historical operational requirements, and set multiple requirement categories based on these historical operational requirements; Based on the historical operating parameters of each key component for each demand category, the historical application coefficient and historical impact coefficient of the corresponding key component for each demand category are generated. Combined with the number of associated key components of the corresponding key component in the corresponding demand category, the historical importance coefficient of the corresponding key component for the corresponding demand category is generated. Based on the historical importance coefficient of each key component to the same demand category, construct a sequence of key components for the corresponding demand category; Obtain real-time operational requirements and determine the corresponding requirement categories; The number of monitoring points for each key component is set according to the historical importance coefficient of the key components in the key component sequence of the demand category corresponding to real-time operation requirements. Monitoring points are set based on the number of monitoring points and the preset key points of each key component.

3. The online monitoring method for key components of electrical equipment as described in claim 2, characterized in that, Based on the analysis results, initial state identifiers for corresponding key components are generated, including: Pre-set several standard monitoring data ranges for each key component; Each real-time monitoring data in each real-time monitoring data sequence of the same monitoring point of the same key component is compared with the corresponding standard monitoring data interval. A first data set of the corresponding real-time monitoring data sequence is constructed based on the real-time monitoring data that is within the standard monitoring data interval, and a second data set of the corresponding real-time monitoring data sequence is constructed based on the real-time monitoring data that is not within the standard monitoring data interval. The sub-state coefficient of the corresponding monitoring point is calculated based on the first and second data sets of all real-time monitoring data sequences of the same monitoring point. The state coefficient is generated based on the sub-state coefficients of all monitoring points of the same key component; The initial state identifier of the corresponding key component is set according to the state coefficient.

4. The online monitoring method for key components of electrical equipment as described in claim 3, characterized in that, The initial state flags of the corresponding key components are set according to the state coefficients, including: The formula for calculating the state coefficient is as follows: ； Where T is the state coefficient, n is the number of monitoring points for the corresponding key components, t0i is the sub-state coefficient of the i-th monitoring point, and ai is the weight coefficient of the i-th monitoring point. The formula for calculating the sub-state coefficients is as follows: ； Where t0 is the sub-state coefficient, w is the number of real-time monitoring data sequences for the corresponding monitoring point, e1 is the state transition coefficient corresponding to the first data set, e2 is the state transition coefficient corresponding to the second data set, m1 is the number of data in the first data set, and m2 is the number of data in the second data set. Let be the median value of the standard monitoring data interval corresponding to the j-th real-time monitoring data sequence. For the j-th real-time monitoring data, the s1-th real-time monitoring data is in the first data set of the j-th real-time monitoring data sequence. The standard monitoring data threshold is the standard monitoring data range corresponding to the j-th real-time monitoring data sequence. The s2nd real-time monitoring data in the second data set of the j-th real-time monitoring data sequence; Preset the first preset state coefficient threshold Second preset state coefficient threshold ; when At that time, the initial status flag of the corresponding key component is set as an abnormal state; when At that time, the initial state flag of the corresponding key component is set to unknown; when At that time, the initial status flag of the corresponding key component is set to normal state.

5. The online monitoring method for key components of electrical equipment as described in claim 4, characterized in that, Based on the initial state identifier, set the secondary monitoring instructions for the corresponding key components, including: Randomly select a key component whose initial state is identified as unknown during the current monitoring period as the first key component of the target; Calculate the median of the preset state coefficients between the first preset state coefficient threshold and the second preset state coefficient threshold. ; when At that time, calculate the difference in the first state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the first state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the second state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the second state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the third state coefficients. And generate a first correction strategy, the first correction strategy including adjusting the third state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; when At that time, calculate the difference in the fourth state coefficients. And generate a first correction strategy, the first correction strategy including based on the fourth state coefficient difference. The preset monitoring time interval for the remaining monitoring period of the current monitoring cycle of the target's first critical component is corrected, wherein... ; The first correction strategy is generated sequentially for each critical component whose initial state is identified as unknown.

6. The online monitoring method for key components of electrical equipment as described in claim 5, characterized in that, The system also includes setting secondary monitoring instructions for key components based on the initial state identifier, and further includes: Randomly select a key component whose initial state is marked as normal in the current monitoring cycle as the second key component of the target; Acquire the data change characteristics of each monitoring point of the target's second key component in the current monitoring cycle, wherein the data change characteristics include data change trend, data change rate, and data change magnitude. Generate a data change evaluation value for the second key component of the target based on the data change characteristics of all monitoring points of the target's second key component in the current monitoring period; Pre-set a first preset data change evaluation value threshold and a second preset data change evaluation value threshold; If the data change evaluation value is less than the first preset data change evaluation value threshold, the difference of the first data change evaluation value is calculated. The second correction strategy includes correcting the preset monitoring time interval of the next monitoring cycle based on the difference of the first data change evaluation value (increasing the preset monitoring time interval). If the data change evaluation value is between the first preset data change evaluation value threshold and the second preset data change evaluation value threshold, calculate the median of the data change evaluation value threshold, and generate the second data change evaluation value difference based on the data change evaluation value and the median of the data change evaluation value threshold. The second correction strategy includes correcting the preset monitoring time interval for the next monitoring cycle based on the difference in the evaluation value of the second data change; If the data change evaluation value is greater than the second preset data change evaluation value threshold, the third data change evaluation value difference is calculated. The second correction strategy includes correcting the preset monitoring time interval of the remaining monitoring period of the current monitoring cycle based on the third data change evaluation value difference. A second correction strategy is generated sequentially for each critical component whose initial state is identified as normal.

7. The online monitoring method for key components of electrical equipment as described in claim 6, characterized in that, The system also includes setting secondary monitoring instructions for key components based on the initial state identifier, and further includes: Randomly select a key component whose initial state is marked as abnormal in the current monitoring period as the target's third key component; Based on the abnormal component reference library, a similarity analysis is performed on the state coefficients and corresponding abnormal types of the target's third key component to obtain the similarity score. The abnormal component reference library includes several preset abnormal types of key components. Each preset abnormal type is mapped to several preset state coefficients, and each preset state coefficient is associated with several preset influence characteristic parameters and preset maintenance strategies. Extract the preset maintenance strategy corresponding to the maximum similarity and set it as the maintenance strategy for the target third key component; Maintenance strategies are generated sequentially for each critical component whose initial state is marked as abnormal.

8. The online monitoring method for key components of electrical equipment as described in claim 7, characterized in that, The compensation coefficient is calculated based on the monitoring data packets, including: Real-time monitoring data for the remaining monitoring period is collected according to the preset monitoring time interval after correction according to the first correction strategy, and a monitoring data package for key components in unknown state is generated. Several risk assessment indicators are pre-defined; The monitoring data packets of each key component in an unknown state are evaluated based on several risk assessment indicators to generate an abnormal risk assessment value. The compensation coefficients for key components in the unknown state are set according to the abnormal risk assessment values; If the second correction strategy is to correct the preset monitoring time interval for the remaining monitoring period of the current monitoring cycle, real-time monitoring data for the remaining monitoring period is collected according to the preset monitoring time interval corrected by the second correction strategy, and a monitoring data packet for key components in normal state is generated. Several deviation evaluation indicators are pre-defined; The monitoring data packets of each key component in normal condition are evaluated based on several deviation evaluation indicators to generate data deviation evaluation values. Set compensation coefficients for key components in normal condition based on data deviation evaluation values; If the second correction strategy is to correct the preset monitoring time interval for the next monitoring period and extrapolate the curve trend of the current monitoring period to obtain the predicted data change curve for the next monitoring period; The predicted data change curve includes predicted monitoring data at several preset monitoring time intervals after correction by the second correction strategy; Based on several predictive monitoring data points from the predicted data change curve, a monitoring data package for key components in normal condition is generated. The monitoring data packets of key components in normal condition are evaluated based on several deviation evaluation indicators to generate predicted data deviation evaluation values. The prediction data deviation evaluation value is corrected based on the degree of fluctuation in the prediction data change curve, and the compensation coefficient of the key components corresponding to the normal state is set based on the corrected prediction data deviation evaluation value. Based on the preset impact features mapped by the preset maintenance strategy corresponding to the maximum similarity, a monitoring data packet for key components in abnormal state is generated. Several impact assessment indicators are pre-defined; The monitoring data packets of key components in abnormal states are evaluated based on several impact evaluation indicators to generate impact evaluation values. The compensation coefficients for key components in the corresponding abnormal state are set according to the impact evaluation values.

9. The online monitoring method for key components of electrical equipment as described in claim 8, characterized in that, A critical component monitoring report is generated based on the corrected state coefficient, including: The state coefficients of the corresponding key components are corrected based on the compensation coefficients to obtain the corrected state coefficients. The final status identifier of the corresponding key component is determined based on the corrected status coefficient. The final status identifier includes normal status and abnormal status. A critical component monitoring report for the current monitoring cycle is generated based on the final status identifier of each critical component. The critical component monitoring report includes several critical components in abnormal states, the corresponding maintenance strategies, and the maintenance sequence.

10. An online monitoring system for key components of electrical equipment, characterized in that, include: The setting module is used to set multiple monitoring points based on the historical parameters of key components. Each monitoring point is equipped with several monitoring devices, and real-time monitoring data of the corresponding monitoring point is collected based on the monitoring devices and the first-level monitoring instructions. The analysis module is used to construct and analyze multiple real-time monitoring data sequences based on real-time monitoring data from multiple monitoring points of the same key component, calculate the state coefficient of the corresponding key component based on the analysis results, and set the initial state identifier. The calculation module is used to set the secondary monitoring instructions for the corresponding key components according to the initial state identifier, obtain the monitoring data packets of the corresponding key components based on the secondary monitoring instructions, and calculate the compensation coefficient based on the monitoring data packets. The generation module is used to generate corrected state coefficients based on the compensation coefficients and the state coefficients of the corresponding key components, and to generate key component monitoring reports based on the corrected state coefficients.

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