Power equipment state monitoring operation and maintenance control system based on data monitoring

Abstract

The invention discloses a power equipment state monitoring operation and maintenance control system based on data monitoring, relates to the technical field of power equipment operation and maintenance, and solves the technical problem that in the prior art, cooperative operation scene operation risk assessment cannot be performed according to power equipment with a low fault risk trend. Specifically, a historical fault analysis unit performs operation process data acquisition on power equipment, sets a top event and a middle event according to the acquired data, and constructs a fault tree; the fault feature recognition unit is used for extracting and recognizing collected data features of the power equipment in the fault high-risk trend, and a fault event and a non-fault event are obtained according to the feature extraction and recognition; and the parallel fault analysis unit performs multi-device parallel fault risk analysis on the power device in the fault low-risk trend, and deduces whether an operation fault risk exists during cooperative operation of the power device in the fault low-risk trend according to the analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment operation and maintenance, and in particular to a power equipment state monitoring and operation and maintenance control system based on data monitoring. Background Art

[0002] Power equipment mainly includes two categories: power generation equipment and power supply equipment; the operation and maintenance control of power equipment is the key link to ensure the safe, stable and efficient operation of the power system; data monitoring of power equipment is an important part of operation and maintenance control. Through the collection, analysis and processing of various data, the operating status of the equipment can be grasped in real time and potential problems can be discovered in time.

[0003] The patent with announcement number CN119205069A discloses an intelligent monitoring system for the operation and maintenance status of power equipment, which relates to the field of power equipment operation and maintenance technology; the system realizes fault prediction and remote control by real-time monitoring of the operating status of power equipment; the system effectively improves the operation and maintenance efficiency and reduces the equipment failure rate through functions such as real-time monitoring, intelligent operation and maintenance, and personalized configuration, bringing significant improvements and enhancements to the operation and maintenance management of power equipment.

[0004] However, in the prior art, it is impossible to collect top events and intermediate events according to the impact of events, it is impossible to build a fault tree for the operation process of power equipment, and it is impossible to perform fault risk trend analysis based on the collected data of each basic event in the fault tree; in addition, it is impossible to conduct risk assessment on power equipment with low fault risk trends, and it is impossible to eliminate the operational impact of various types of events, which reduces the operation and maintenance control efficiency of power equipment.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the above-mentioned problems and to propose an electric power equipment status monitoring and operation and maintenance control system based on data monitoring.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An electric power equipment condition monitoring and operation and maintenance control system based on data monitoring includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected with a historical fault analysis unit, a fault feature recognition unit, and a parallel fault analysis unit;

[0009] The historical fault analysis unit is used to collect the operation process data of the power equipment, set the top event and the intermediate event according to the collected data, and build a fault tree. According to the fault tree analysis, the power equipment fault risk trend is set, and it is set as a high-risk fault trend and a low-risk fault trend;

[0010] The fault feature recognition unit is used to extract and identify the features of the collected data of the power equipment with a high risk of failure, obtain the fault events and non-fault events based on the feature extraction and recognition, and perform operation and maintenance control on the collected data of the corresponding events;

[0011] The parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment with a low fault risk trend, and infer whether there is an operational failure risk when the power equipment with a low fault risk trend is operated in coordination based on the analysis.

[0012] As a preferred embodiment of the present invention, the process of the historical fault analysis unit is as follows:

[0013] The operating time of the power equipment is collected, and the data of the operating time is continuously monitored. The historical operating time is constructed based on the current status monitoring time and the starting time of the operating time. The fault time of the power equipment is obtained based on the historical operating time, and the fault type at the fault time is set as the top event of the fault tree. According to the cause of the top event corresponding to the fault type, the cause type is counted through the collected data of the historical operating time, and marked as an intermediate event.

[0014] As a preferred implementation mode of the present invention, according to the correlation analysis of the intermediate events, the occurrence relationship between the top event and different intermediate events is set; the currently constructed intermediate events are set at the same layer of the fault tree;

[0015] And build the next level of fault tree, that is, collect basic events for the current intermediate events;

[0016] The fault tree is continuously updated according to the event update of each layer of the fault tree at each moment in the historical operation period; and during the updating process, the sum of the number of types of increase of basic events corresponding to the intermediate events of the same layer and the number of types of increase of the corresponding top events is collected.

[0017] As a preferred embodiment of the present invention, if the sum of the quantities exceeds a set quantity threshold, or the sum of the quantities continues to increase at a rate that exceeds a set speed threshold, the trend is marked as a high-risk fault trend; if the sum of the quantities does not exceed the set quantity threshold, and the sum of the quantities continues to increase at a rate that does not exceed the set speed threshold, the trend is marked as a low-risk fault trend.

[0018] As a preferred embodiment of the present invention, the process of the fault feature identification unit is as follows:

[0019] The power equipment with high risk of failure is characterized and marked as identification equipment. The data collected at each level of the fault tree of the identification equipment is analyzed and counted. Before and after the top event occurs, the numerical floating waveform of the data corresponding to the basic event in the fault tree of the identification equipment is collected, and the floating waveform deviation area of ​​the current data is obtained based on the floating waveform comparison;

[0020] At the same time, the floating moments of basic events are counted, and the floating moment waveform is obtained according to the floating of each moment point in the running period. Specifically, the floating threshold of the Y-axis of the coordinate system is set, that is, if the current moment point floats, the value of the corresponding moment point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, there will be a horizontal line on the coordinate system, otherwise it will be 0 if there is no floating. The floating frequency is inferred according to the floating moment waveform.

[0021] As a preferred implementation mode of the present invention, after the top event occurs, the floating waveform deviation area of ​​the basic event is collected, and the floating frequency is obtained according to the floating moment waveform. If the floating waveform deviation area area of ​​the basic event during the period of occurrence of the top event exceeds the set area threshold, or the floating frequency obtained according to the floating moment waveform exceeds the set floating frequency threshold, the current basic event is regarded as a fault event with a high risk trend of fault; and the parameter corresponding to the basic event that exceeds the threshold floating is regarded as the operation and maintenance control trend of the power equipment;

[0022] If the area of ​​the floating waveform deviation region of the basic event during the period when the top event occurs does not exceed the set area threshold, and the floating frequency obtained according to the waveform at the floating time does not exceed the set floating frequency threshold, the current basic event will be regarded as a non-fault event with a high risk trend of fault; and the collected data of non-fault events will not be given priority as operation and maintenance control detection data.

[0023] As a preferred embodiment of the present invention, the process of the parallel fault analysis unit is as follows:

[0024] The power equipment with a low risk trend of failure is marked as cooperative monitoring equipment, and the cooperation type is determined according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, the top events of the corresponding cooperative monitoring equipment are collected, and intermediate event analysis is performed according to the top event type, that is, the same type of intermediate events of the same type of top events and non-same type of intermediate events are marked as same-top-same-middle events and same-top-non-same-middle events, respectively; the same type of intermediate events of non-same type of top events and non-same type of intermediate events are marked as non-same-top-same-middle events and non-same-top-non-same-middle events, respectively.

[0025] As a preferred embodiment of the present invention, the deviation of the set threshold of the collected data corresponding to the same-top but different-center events of the corresponding cooperative monitoring equipment is collected and the deviation of the real-time collected data value corresponds to the deviation numerical ratio. If the deviation numerical ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-top but different-center events affects the operating status of the cooperative monitoring equipment, and the current collected data type is sent to the operation and maintenance monitoring center.

[0026] As a preferred embodiment of the present invention, the real-time peak value of the collected data of the non-same top and the same middle event and the data red line threshold set for the corresponding top event are collected, and the red line distance value of the corresponding matching monitoring equipment is obtained according to the difference calculation; the red line value ratio is obtained by calculating the ratio of the red line distance value and the data red line threshold;

[0027] The red line value ratio of each coordinated monitoring device and the corresponding collected data are counted at each moment, and the floating speed of the red line value ratio and the numerical floating speed of the collected data are obtained;

[0028] When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device increases, or the red line value ratio of the cooperative monitoring device increases accordingly, the corresponding cooperative monitoring device will be marked as a high-risk cooperative device;

[0029] When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device does not increase, and the red line value ratio of the cooperative monitoring device does not increase accordingly, the corresponding cooperative monitoring device will be marked as a low-risk cooperative device;

[0030] When the red line value of the current cooperative monitoring device does not exceed the floating speed threshold compared to the floating speed, the floating speed monitoring will continue.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the present invention, parallel fault analysis of power equipment is performed based on the collected data combined with fault tree technology. The impact of events in each link of the power equipment is collected through the fault tree technology. The probability of occurrence of parallel faults and events affecting the operating status are determined according to the event type, which improves the accuracy of power equipment data monitoring, effectively improves the availability of collected data, and can improve the detection efficiency of power equipment, ensure the accuracy of fault prediction within the operating cycle, and improve the work efficiency of operation and maintenance control.

[0033] 2. In the present invention, the impact of collected data is accurately evaluated by extracting and identifying features, and the impact of the collected data itself and the impact on the power equipment are combined to improve the pertinence of the operation and maintenance control of the power equipment. When a fault occurs, it can be quickly traced according to the fault tree, and targeted control can be performed according to the tracing results, which fundamentally reduces the impact of the fault and improves the operating efficiency of the power equipment.

[0034] 3. In the present invention, multi-device parallel failure risk analysis is performed on power equipment with a low failure risk trend, and it is inferred whether the power equipment with a current low failure risk trend has a high failure risk when operating in coordination with each other, so as to avoid the situation where the power equipment cannot be guaranteed to be in a low failure risk trend in the coordinated operation scenario during operation and maintenance control. The accuracy of state monitoring of power equipment is effectively improved through coordinated operation monitoring, the efficiency of monitoring and operation and maintenance control is improved, the failure rate of power equipment is minimized, and the occurrence of failures is effectively avoided through timely operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0036] Figure 1 It is a principle block diagram of the present invention;

[0037] Figure 2 A flowchart of the steps for implementing the historical fault analysis unit in the present invention;

[0038] Figure 3 This is a flowchart of the steps for implementing the fault feature identification unit in the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0041] See also Figure 1As shown, the power equipment status monitoring and operation and maintenance control system based on data monitoring includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected with a historical fault analysis unit, a fault feature recognition unit and a parallel fault analysis unit;

[0042] The historical fault analysis unit is used to collect operation process data of power equipment, conduct parallel fault analysis of power equipment based on the collected data combined with fault tree technology, collect the impact of events in each link of power equipment through fault tree technology, determine the probability of parallel faults and the impact of events on the operation status according to the event type, improve the accuracy of power equipment data monitoring, effectively improve the availability of collected data, and can improve the detection efficiency of power equipment, ensure the accuracy of fault prediction within the operation cycle, and improve the work efficiency of operation and maintenance control; it needs to be explained that the data monitoring of the fault tree is to collect the operation data related to the power equipment, such as voltage, current, temperature and other related data; and the number of power equipment in the data monitoring process is not unique, but the fault tree only matches a single power equipment;

[0043] It is necessary to further explain that the historical fault analysis unit implementation steps flow chart is as follows Figure 2 As shown;

[0044] Collect the operation time of the power equipment, and continuously monitor the operation time, and construct the historical operation time with the current state monitoring time and the start time of the operation time; according to the historical operation time, get the fault time of the power equipment, and set it as the top event of the fault tree according to the fault type at the fault time; it should be explained that the top event is also an intermediate event, because the power equipment failure will affect other matching equipment, but this scenario is the state monitoring of the power equipment, so it is marked as the top event; specifically, the fault type is the motor power failure;

[0045] According to the cause of the top event corresponding to the fault type, the cause type is counted through the collected data of the historical operation period and marked as an intermediate event; for example, the motor runs but is not powered or the motor does not run;

[0046] According to the correlation analysis of intermediate events, the occurrence relationship between the top event and different intermediate events is set, specifically, an OR relationship; if multiple intermediate events correspond to a single intermediate event affecting the generation of the top event and cannot exist at the same time, the corresponding relationship is OR; for example, if the motor runs but is not powered and the motor does not run, it is an OR relationship;

[0047] Set the intermediate event currently being constructed to the same level of the fault tree;

[0048] And build the next layer of fault tree, that is, collect basic events for the current intermediate event; it needs to be explained that at this time, the intermediate event and the basic event are in an AND relationship, that is, the generation of the basic event will affect the generation of the intermediate event; it needs to be explained that the number of intermediate events between the top event and the basic event is not unique according to the actual scenario, and the fault tree is jointly constructed;

[0049] The fault tree is continuously updated according to the event update of each layer of the fault tree at each moment in the historical operation period; and during the update process, the sum of the number of types of basic events corresponding to the intermediate events of the same layer and the number of types of top events corresponding to the same layer are collected. If the sum of the numbers exceeds the set number threshold, or the continuous increase rate of the sum of the numbers exceeds the set speed threshold, the operating status trend of the power equipment in the current historical operation period is marked as a high-risk fault trend; if the sum of the numbers does not exceed the set number threshold, and the continuous increase rate of the sum of the numbers does not exceed the set speed threshold, the operating status trend of the power equipment in the current historical operation period is marked as a low-risk fault trend;

[0050] When the power equipment is at a high risk of failure, the operation and maintenance monitoring center will perform operation and maintenance on the power equipment; conversely, when the power equipment is at a low risk of failure, the operation process will continue to be monitored;

[0051] The fault feature recognition unit is used to extract and identify the features of the collected data of the power equipment, accurately evaluate the impact of the collected data by extracting and identifying the features, and improve the pertinence of the operation and maintenance control of the power equipment by combining the impact of the collected data itself and the impact on the power equipment. When a fault occurs, it can quickly trace the source according to the fault tree, and can also carry out targeted control according to the traceability results, fundamentally reducing the impact of the fault and improving the operating efficiency of the power equipment;

[0052] It is necessary to further explain that the flow chart of the implementation steps of the fault feature identification unit is as follows: Figure 3 As shown;

[0053] The power equipment with a high risk of failure is characterized and marked as an identification device. The data collected at each level of the fault tree of the identification device is analyzed and counted. Before and after the top event occurs, the numerical floating waveform of the data corresponding to the basic event in the fault tree of the identification device is collected, and the floating waveform deviation area of ​​the current data is obtained based on the floating waveform comparison. It should be explained that the area of ​​the waveform deviation area corresponding to the numerical floating waveform can reflect the magnitude of the floating influence of the numerical value. Specifically, when the current waveform is counted, the basic event is the current floating. If the current floating span is different before and after the top event occurs, the corresponding waveform will deviate.

[0054] At the same time, the floating time of the basic event is counted, and the floating time waveform is obtained according to the floating of each time point in the running period. Specifically, the Y-axis floating threshold of the coordinate system is set, that is, if the current time point floats, the value of the corresponding time point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, it will be a horizontal line on the coordinate system, otherwise it will be 0 if there is no floating; the floating frequency is inferred according to the floating time waveform;

[0055] After the top event occurs, the floating waveform deviation area of ​​the basic event is collected, and the floating frequency is obtained according to the floating moment waveform. If the floating waveform deviation area of ​​the basic event during the period of the top event exceeds the set area threshold, or the floating frequency obtained according to the floating moment waveform exceeds the set floating frequency threshold, the current basic event is regarded as a fault event with a high risk trend of fault; and the parameter corresponding to the basic event exceeding the threshold floating is used as the operation and maintenance control trend of the power equipment;

[0056] If the area of ​​the floating waveform deviation region of the basic event during the period of the top event does not exceed the set area threshold, and the floating frequency obtained according to the floating time waveform does not exceed the set floating frequency threshold, the current basic event is regarded as a non-fault event with a high risk trend of fault; and the collected data of the non-fault event is not given priority as the operation and maintenance control detection data;

[0057] After the fault feature identification is completed, the data type of the corresponding fault event and the numerical floating trajectory of the corresponding type of data in the corresponding operation and maintenance fault are synchronized to the operation and maintenance monitoring center. The operation and maintenance monitoring center stores and issues early warnings based on the data floating trajectory of the fault event, and adjusts and updates the set threshold according to the floating value when necessary;

[0058] It should be explained that the set thresholds are all threshold values ​​manually set by personnel in the field of power equipment operation and maintenance based on historical operation failure scenarios; if the set threshold is not suitable for the current scenario, the threshold is updated to ensure accurate data detection in the current scenario;

[0059] The parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment with a low fault risk trend, infer whether the power equipment with a current low fault risk trend has a high fault risk when operating in coordination with each other, and avoid the failure to ensure that the power equipment is still in a low fault risk trend in the coordinated operation scenario during operation and maintenance control. The accuracy of state monitoring of power equipment is effectively improved through coordinated operation monitoring, the efficiency of monitoring and operation and maintenance control is improved, the failure rate of power equipment is reduced to the greatest extent, and the occurrence of failures is effectively avoided through timely operation and maintenance;

[0060] The power equipment with a low risk trend of failure is marked as a cooperative monitoring equipment, and the cooperation type is determined according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, the top events of the corresponding cooperative monitoring equipment are collected, and the intermediate events are analyzed according to the top event type, that is, the same type of intermediate events of the same type of top events and the non-same type of intermediate events are marked as same-top-same-middle events and same-top-non-same-middle events respectively; the same type of intermediate events of non-same type of top events and the non-same type of intermediate events are marked as non-same-top-same-middle events and non-same-top-non-same-middle events respectively; it should be explained that the same-top-same-middle events refer to the same top events and the same intermediate events of the collected cooperative monitoring equipment; the same-top-non-same-middle events refer to the same top events of the collected cooperative monitoring equipment but the same intermediate events; the non-same-top-same-middle events refer to the different top events of the collected cooperative monitoring equipment but the same intermediate events; the non-same-top-same-middle events refer to the different top events of the collected cooperative monitoring equipment but the same intermediate events; the non-same-top-non-same-middle events refer to the different top events of the collected cooperative monitoring equipment and the different intermediate events;

[0061] Collect the deviation of the set threshold of the collected data corresponding to the same-top but different-center event of the corresponding monitoring equipment and the deviation of the real-time collected data value. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-top but different-center event affects the running state of the cooperative monitoring equipment. Send the current collected data type to the operation and maintenance monitoring center. After receiving it, the operation and maintenance monitoring center monitors the current collected data of the same-top but different-center event and the corresponding value fluctuation, and adjusts it in time. At the same time, the same-top event is used as the corresponding top event of the intermediate event for monitoring and early warning.

[0062] Collect the real-time peak value of the collected data of the non-same top and same center events and the data red line threshold set for the corresponding top event, and calculate the red line distance value of the corresponding monitoring equipment according to the difference; calculate the red line value ratio according to the ratio of the red line distance value and the data red line threshold;

[0063] The red line value ratio of each cooperating monitoring device and the corresponding collected data are counted at each moment, and the floating speed of the red line value ratio and the numerical floating speed of the collected data are obtained. When the floating speed of the red line value ratio of the current cooperating monitoring device exceeds the floating speed threshold, if the numerical floating speed of the collected data of the intermediate event corresponding to the cooperating monitoring device increases, or the red line value ratio of the cooperating monitoring device increases accordingly, it is inferred that the current cooperating monitoring device has a risk of cooperating operation, and the corresponding cooperating monitoring device is marked as a high-risk cooperating device, and the collected data of the intermediate event is monitored when the high-risk cooperating device is running, so as to avoid the generation of collected data corresponding to the same intermediate event by different cooperating monitoring devices;

[0064] When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device does not increase, and the red line value ratio of the cooperative monitoring device does not increase accordingly, it is inferred that there is no cooperative operation risk for the current cooperative monitoring device, and the corresponding cooperative monitoring device is marked as a low-risk cooperative device;

[0065] When the red line value of the current cooperative monitoring device does not exceed the floating speed threshold, the floating speed monitoring will continue;

[0066] For the coordinated monitoring equipment of the events with the same top and the same center, the collected data of the intermediate events are synchronously identified and monitored; for the events with different tops and different centers, the coordinated monitoring equipment performs impact analysis on each type of intermediate events to infer whether there is a floating impact on the corresponding collected data of the intermediate events. If the floating between the collected data does not affect each other, the collected data of different intermediate events are monitored accordingly. If there is an impact, the coordinated monitoring process of the events with the same top and different centers is used for monitoring;

[0067] When the present invention is in use, the historical fault analysis unit collects operation process data of the power equipment, sets the top event and the intermediate event according to the collected data, and constructs a fault tree, and sets the fault risk trend of the power equipment according to the fault tree analysis, and sets it as a high-risk fault trend and a low-risk fault trend; the fault feature recognition unit extracts and identifies features of the collected data of the power equipment with a high-risk fault trend, obtains fault events and non-fault events according to the feature extraction and recognition, and performs operation and maintenance control on the collected data of the corresponding events; the parallel fault analysis unit performs multi-device parallel fault risk analysis on the power equipment with a low-risk fault trend, and infers whether there is an operation failure risk when the power equipment with a low-risk fault trend cooperates in operation according to the analysis.

[0068] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The power equipment status monitoring and operation and maintenance control system based on data monitoring is characterized by: It includes an operation and maintenance monitoring center, and the operation and maintenance monitoring center is communicatively connected with a historical fault analysis unit, a fault feature identification unit, and a parallel fault analysis unit; The historical fault analysis unit is used to collect the operation process data of the power equipment, set the top event and the intermediate event according to the collected data, and build a fault tree. According to the fault tree analysis, the power equipment fault risk trend is set, and it is set as a high-risk fault trend and a low-risk fault trend; The fault feature recognition unit is used to extract and identify the features of the collected data of the power equipment with a high risk of failure, obtain the fault events and non-fault events based on the feature extraction and recognition, and perform operation and maintenance control on the collected data of the corresponding events; The parallel fault analysis unit is used to perform multi-device parallel fault risk analysis on power equipment with a low fault risk trend, and infer whether there is an operational failure risk when the power equipment with a low fault risk trend is operated in coordination based on the analysis.

2. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 1 is characterized in that: The process of the historical fault analysis unit is as follows: Collect the operating time of power equipment and continuously monitor the operating time, and build the historical operating time based on the current status monitoring time and the starting time of the operating time; According to the historical operation period, the fault time of the power equipment is obtained, and the fault type at the fault time is set as the top event of the fault tree; according to the cause of the top event corresponding to the fault type, the cause type is counted through the collected data of the historical operation period and marked as an intermediate event.

3. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 2 is characterized in that: According to the correlation analysis of intermediate events, the occurrence relationship between the top event and different intermediate events is set; the currently constructed intermediate events are set at the same level of the fault tree; And build the next level of fault tree, that is, collect basic events for the current intermediate events; The fault tree is continuously updated according to the event updates of each layer of the fault tree at each moment in the historical operation period; During the updating process, the sum of the number of added basic event types corresponding to the intermediate events at the same layer and the number of added top event types corresponding to the intermediate events at the same layer is collected.

4. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 3 is characterized in that: If the sum of the quantities exceeds the set quantity threshold, or the sum of the quantities continues to increase at a rate exceeding the set speed threshold, the trend is marked as a high-risk trend for failure; If the sum of the quantities does not exceed the set quantity threshold, and the continuous increase rate of the sum of the quantities does not exceed the set speed threshold, the trend is marked as a low risk trend for failure.

5. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 1 is characterized in that: The process of the fault feature recognition unit is as follows: The power equipment with high risk of failure is characterized and marked as identification equipment. The data collected at each level of the fault tree of the identification equipment is analyzed and counted. Before and after the top event occurs, the numerical floating waveform of the data corresponding to the basic event in the fault tree of the identification equipment is collected, and the floating waveform deviation area of ​​the current data is obtained based on the floating waveform comparison; At the same time, the floating moments of basic events are counted, and the floating moment waveform is obtained according to the floating of each moment point in the running period. Specifically, the floating threshold of the Y-axis of the coordinate system is set, that is, if the current moment point floats, the value of the corresponding moment point on the Y-axis of the coordinate system is the set floating threshold. If it continues to float, there will be a horizontal line on the coordinate system, otherwise it will be 0 if there is no floating. The floating frequency is inferred according to the floating moment waveform.

6. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 5 is characterized in that: After the top event occurs, the floating waveform deviation area of ​​the basic event is collected, and the floating frequency is obtained according to the floating moment waveform. If the floating waveform deviation area of ​​the basic event during the period of the top event exceeds the set area threshold, or the floating frequency obtained according to the floating moment waveform exceeds the set floating frequency threshold, the current basic event is regarded as a fault event with a high risk trend of fault; and the parameter corresponding to the basic event exceeding the threshold floating is used as the operation and maintenance control trend of the power equipment; If the area of ​​the floating waveform deviation area corresponding to the basic event during the time period when the top event occurs does not exceed the set area threshold, and the floating frequency obtained according to the floating moment waveform does not exceed the set floating frequency threshold, then the current basic event will be regarded as a non-fault event with a high risk trend of fault; and the collected data of non-fault events will not be given priority as operation and maintenance control detection data.

7. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 1 is characterized in that: The process of the parallel fault analysis unit is as follows: The power equipment with a low risk trend of failure is marked as cooperative monitoring equipment, and the cooperation type is determined according to the power equipment required by the power process flow. During the cooperative operation of the cooperative monitoring equipment, the top events of the corresponding cooperative monitoring equipment are collected, and intermediate event analysis is performed according to the top event type, that is, the same type of intermediate events of the same type of top events and non-same type of intermediate events are marked as same-top-same-middle events and same-top-non-same-middle events, respectively; the same type of intermediate events of non-same type of top events and non-same type of intermediate events are marked as non-same-top-same-middle events and non-same-top-non-same-middle events, respectively.

8. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 7 is characterized in that: The deviation of the collected data corresponding to the same-top but different-center events of the corresponding monitoring equipment is collected, and the deviation of the real-time collected data value corresponds to the deviation of the set threshold. If the deviation value ratio exceeds the set threshold and continues to increase, it is inferred that the collected data corresponding to the same-top but different-center events affects the operating status of the corresponding monitoring equipment, and the current collected data type is sent to the operation and maintenance monitoring center.

9. The data-based monitoring and control system for power equipment status monitoring and operation according to claim 8 is characterized in that: Collect the real-time peak value of the collected data of the non-same top and same center events and the data red line threshold set for the corresponding top event, and calculate the red line distance value of the corresponding monitoring equipment according to the difference; calculate the red line value ratio according to the ratio of the red line distance value and the data red line threshold; The red line value ratios of each coordinated monitoring device and the corresponding collected data are statistically analyzed at each moment, and the floating speed of the red line value ratio and the numerical floating speed of the collected data are obtained.

10. The power equipment status monitoring and operation and maintenance control system based on data monitoring according to claim 9 is characterized in that: When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device increases, or the red line value ratio of the cooperative monitoring device increases accordingly, the corresponding cooperative monitoring device will be marked as a high-risk cooperative device; When the floating speed of the red line value ratio of the current cooperative monitoring device exceeds the floating speed threshold, if the floating speed of the collected data value of the corresponding intermediate event of the cooperative monitoring device does not increase, and the red line value ratio of the cooperative monitoring device does not increase accordingly, the corresponding cooperative monitoring device will be marked as a low-risk cooperative device; When the red line value of the current cooperative monitoring device does not exceed the floating speed threshold compared to the floating speed, the floating speed monitoring will continue.

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