Monitoring method and monitoring system based on power operation and maintenance big data

Abstract

The present invention relates to the field of power operation and maintenance management technology, and specifically discloses a method and system based on power operation and maintenance big data monitoring, including: data acquisition and transmission module, data storage and processing module, remote monitoring and alarm module, data analysis and prediction module and optimization decision and maintenance module. By setting up a power grid acquisition device at the power operation and maintenance site, the operating parameters, state parameters and environmental parameters of the power equipment are collected in real time, and the data transmission equipment and bus technology are used to efficiently and stably transmit the data to the server for storage and processing, thereby ensuring the comprehensiveness and real-time nature of the data. By calculating the fault prediction probability formula, the historical fault rate and regional importance score are comprehensively considered, the potential fault area is identified in advance, and preventive measures are taken in time. The energy efficiency index formula is used to quantitatively evaluate the energy consumption level of the power equipment, identify high-energy consumption equipment, guide energy-saving improvements, and help save energy and reduce consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power operation and maintenance management, and in particular to a method and system for monitoring electric power operation and maintenance big data. Background Art

[0002] With the continuous development of power systems and the expansion of power grids, the importance of power operation and maintenance has become increasingly prominent. The main task of power operation and maintenance is to ensure the safe, stable and efficient operation of the power system. However, traditional power operation and maintenance mainly relies on manual inspections and experience judgments, which has the following shortcomings:

[0003] 1. Incomplete data collection and poor real-time performance: Traditional methods are difficult to obtain key data such as the operating status and environmental parameters of power equipment in a timely and comprehensive manner, and cannot meet the needs of modern power systems for real-time monitoring;

[0004] 2. Insufficient data processing and analysis capabilities: Faced with a large amount of field data, there is a lack of effective processing and analysis methods, making it impossible to deeply explore the value of data and difficult to timely discover potential fault hazards and energy consumption problems;

[0005] 3. Weak fault prediction and early warning capabilities: Due to the lack of scientific evaluation indicators and prediction models, it is difficult to predict the occurrence of faults in a timely and accurate manner, resulting in delayed equipment maintenance and increasing the risk of unplanned downtime and equipment damage;

[0006] 4. Lack of scientific basis for maintenance decisions: The formulation of maintenance plans mainly relies on experience, lacks quantitative indicators and methods, and cannot prioritize according to equipment status and failure risks, resulting in unreasonable resource allocation and affecting operation and maintenance efficiency;

[0007] 5. Energy consumption management needs to be improved: With energy becoming increasingly scarce, traditional operation and maintenance methods are unable to detect high-energy consumption equipment in a timely manner and cannot effectively guide energy conservation and consumption reduction work;

[0008] To sum up, the existing power operation and maintenance methods can no longer meet the needs of modern power systems. There is an urgent need for a power operation and maintenance monitoring method and system based on big data that can realize real-time data collection, transmission, processing and analysis, have fault prediction and early warning capabilities, scientifically formulate maintenance plans, improve energy utilization efficiency, and ensure the safe, stable and efficient operation of the power system. Summary of the invention

[0009] The purpose of the present invention is to provide a method and system based on power operation and maintenance big data monitoring to solve the problems raised in the above-mentioned background technology. Through advanced data acquisition and transmission technology, intelligent data analysis and prediction algorithms, and optimized decision support mechanisms, the efficiency and reliability of power operation and maintenance are comprehensively improved. The present invention can not only monitor the operating status of power equipment in real time, but also comprehensively evaluate the overall performance and status of the power grid acquisition device, predict potential faults, and reasonably arrange maintenance plans, thereby ensuring the safe and stable operation of the power system.

[0010] To achieve the above object, the present invention provides the following technical solution: a method for monitoring power operation and maintenance big data, comprising the following steps:

[0011] S1. Set up a power grid collection device at the power operation and maintenance site to collect the operation and status parameters of the power equipment and the environmental parameters. The collection device transmits the data to the data transmission device. The data transmission device collects the data through the bus and transmits the data to the server through the Internet for storage and processing;

[0012] S2. The server receives and stores data transmitted by the data transmission device, and performs statistics on the data by day, month, year and peak power consumption period;

[0013] S3, remote monitoring equipment connects to the server to display data in real time, and can set alarm thresholds. When data exceeds the threshold, it will automatically trigger an alarm to remind the monitoring personnel;

[0014] S4. Analyze on-site data to evaluate the operating status and performance of the power system, predict faults through the fault prediction probability formula to provide early warnings, and use the energy efficiency index formula to evaluate the potential for energy efficiency improvement of power equipment to help save energy and reduce consumption;

[0015] S5. Operation and maintenance personnel make optimization decisions based on the analysis and prediction results, adjust the operating parameters of power-consuming equipment, optimize the grid structure, and use the maintenance priority formula to determine the equipment maintenance priority and arrange maintenance plans to ensure reliable operation of the equipment.

[0016] Preferably, in S1, the overall performance and status of the power grid collection device are comprehensively evaluated:

[0017] S11. Calculate the data transmission balance value of each power grid collection device to evaluate the stability and reliability of data transmission. The data transmission balance value calculation formula is:

[0018] ;in, Indicates The data transmission balance value of each power grid collection device, Indicates The highest amplitude in the time domain of each power grid collection device, Indicates The lowest amplitude in the time domain of each power grid collection device;

[0019] S12. Calculate the performance stability value of each power grid collection device to evaluate the performance stability and power consumption efficiency of the device. The performance stability value calculation formula is:

[0020] ;in, Indicates Performance stability value of each power grid collection device; Indicates The power consumption efficiency index of a power grid collection device is calculated as follows:

[0021] ;in, For the The actual average power consumption of each device, is the reference power consumption value; Indicates The operation stability index of a power grid collection device is calculated as follows:

[0022] ;in, For the The actual data sampling rate of each device, is the reference data sampling rate of the device; Indicates The response performance index of a power grid collection device is calculated as follows:

[0023] ;in, For the The average response time of each device, is the expected response time reference value; , , are the weight coefficients of power consumption efficiency, operation stability and response performance, and satisfy + + =1;

[0024] S13. Calculate the power balance value of each power grid collection device to monitor whether the power output of the equipment is balanced. The power balance value calculation formula is:

[0025] ;in, Indicates The power balance value of each power grid collection device, Indicates The highest reference active power of each grid collection device, Indicates The highest reference reactive power of each grid collection device;

[0026] S14. Comprehensively evaluate the monitoring evaluation value of each power grid collection device to provide a basis for equipment maintenance and management. The monitoring evaluation value calculation formula is:

[0027] ;in, Indicates The monitoring evaluation value of each power grid collection device, , , Respectively represent the weight coefficients of data transmission balance value, performance stability value and power balance value, and .

[0028] Preferably, in S3, an alarm threshold is set to ensure that an alarm signal can be issued in time when the monitoring parameters are abnormal. The alarm threshold setting formula is:

[0029] ;in, Indicates the alarm threshold, Indicates the historical average value of a monitoring parameter. Indicates the historical standard deviation of the monitoring parameter. Represents the critical value of the standard normal distribution selected according to the required alarm sensitivity.

[0030] Preferably, in S4, the fault prediction probability of each operation and maintenance area is calculated to identify potential fault areas in advance. The fault prediction probability calculation formula is:

[0031] ;in, represents the failure prediction probability, Indicates The historical failure rate of each operation and maintenance area, Indicates The importance score of each operation and maintenance area, Indicates the total number of operation areas.

[0032] Preferably, in S4, the energy efficiency index of each electrical device is calculated to evaluate the energy consumption level of the device. The energy efficiency index formula is:

[0033] ;in, represents the energy efficiency index, Indicates The power of electrical equipment, Indicates The actual operating time of each electrical device, Indicates the total number of electrical devices.

[0034] Preferably, in S5, the maintenance priority of the equipment is calculated, and a maintenance plan is arranged according to the failure prediction probability, the energy efficiency index and the equipment age. The maintenance priority calculation formula is:

[0035] ;in, Indicates the maintenance priority, , , represent the weight coefficients of failure prediction probability, energy efficiency index and equipment age, respectively, and , Indicates the operating age or aging degree of the equipment.

[0036] Based on the power operation and maintenance big data monitoring system, including:

[0037] The data acquisition and transmission module includes data acquisition equipment and data transmission equipment, which are used to collect data from the power operation and maintenance site in real time and transmit the data to the server for storage and processing. The data acquisition and transmission module also has the function of comprehensively evaluating the overall performance and status of the power grid acquisition device, including calculating the data transmission balance value, performance stability value and power balance value, providing a basis for equipment maintenance and management;

[0038] Data storage and processing module, used to receive and store field data, and to process and statistically analyze the data;

[0039] Remote monitoring and alarm module, used to display field data in real time, set alarm thresholds, and send alarm signals when monitoring parameters are abnormal;

[0040] The data analysis and prediction module is used to analyze field data, evaluate the operating status of the power system, predict possible failures, and evaluate the energy efficiency of power equipment. The data analysis and prediction module can also calculate the failure prediction probability of each operation and maintenance area and the energy efficiency index of each power equipment to gain a more comprehensive understanding of the operating status of the power system;

[0041] The optimization decision and maintenance module is used to make optimization decisions, adjust the operating parameters of electrical equipment, and reasonably arrange maintenance plans based on the analysis and prediction results. The optimization decision and maintenance module uses the maintenance priority calculation formula to determine the maintenance priority of the equipment.

[0042] Preferably, the data acquisition equipment includes multiple monitoring terminals, the data transmission equipment adopts a communication management machine, and the multiple monitoring terminals are connected to the communication management machine through a bus; the monitoring terminals are arranged at the electrical equipment on site, and are used to connect with sensors and data acquisition circuits to collect operating data or status data of the electrical equipment, and the communication management machine has a communication module for communicating with a server or a remote monitoring device.

[0043] Preferably, different operation and maintenance areas are set up according to geographical locations, and each operation and maintenance area is provided with the data collection device and data transmission equipment.

[0044] Preferably, the overall health status of the power grid collection device is comprehensively evaluated, and the comprehensive equipment health index formula is:

[0045] ;in: Indicates Comprehensive equipment health index of each power grid collection device; Indicates the historical failure rate, ranging from 0 to 1, where 1 indicates a high failure rate; Respectively represent the weight coefficients of each indicator, and .

[0046] The power operation and maintenance big data monitoring method and system proposed in the present invention have the following beneficial effects:

[0047] 1. By setting up power grid collection devices at the power operation and maintenance site, the operating parameters, status parameters and environmental parameters of power-consuming equipment are collected in real time. By using data transmission equipment and bus technology, the data is efficiently and stably transmitted to the server for storage and processing, ensuring the comprehensiveness and real-time nature of the data.

[0048] 2. The server conducts statistical analysis on the received data by day, month, year and peak electricity consumption period, providing a solid data foundation for subsequent data mining and decision-making.

[0049] 3. By calculating the fault prediction probability formula, taking into account the historical failure rate and regional importance score, potential fault areas can be identified in advance and preventive measures can be taken in time, reducing the probability of unplanned downtime and equipment failure.

[0050] 4. Use the energy efficiency index formula to quantitatively evaluate the energy consumption level of electrical equipment, identify high-energy consumption equipment, guide energy-saving improvements, and help save energy and reduce consumption.

[0051] 5. Calculate the maintenance priority of the equipment based on the failure prediction probability, energy efficiency index and equipment age, formulate a scientific maintenance plan, and reasonably allocate maintenance resources to ensure the reliable operation of the equipment.

[0052] 6. By calculating the data transmission balance value, performance stability value and power balance value, the overall performance and status of the power grid collection device are comprehensively evaluated to ensure the stability of data transmission, the reliability of equipment operation and power consumption efficiency, providing a scientific basis for equipment maintenance and management.

[0053] 7. Using the alarm threshold setting formula based on statistical analysis, the alarm sensitivity can be flexibly adjusted according to different environments and conditions, improving the accuracy of anomaly detection and reducing false alarms and missed alarms.

[0054] 8. The present invention builds a complete monitoring system, realizing full process coverage from data collection, transmission, storage, processing, to data analysis, prediction, optimization decision-making and maintenance management, greatly improving the efficiency and intelligence level of power operation and maintenance.

[0055] In summary, the present invention effectively solves the problems existing in the prior art, such as incomplete data collection, insufficient fault prediction capability, lack of scientific basis for maintenance decisions, etc., improves the reliability and safety of the power system, reduces operating costs, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the monitoring method based on power operation and maintenance;

[0057] Figure 2 A flow chart for comprehensively evaluating the overall performance and status of the power grid collection device;

[0058] Figure 3 This is the principle block diagram of the monitoring system based on power operation and maintenance;

[0059] Figure 4 This is the principle block diagram of the data acquisition and transmission module. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0061] like Figure 1 As shown, the present invention provides a technical solution: a method for monitoring power operation and maintenance big data, comprising the following steps:

[0062] S1. Set up a power grid collection device at the power operation and maintenance site to collect the operation and status parameters of the power equipment and the environmental parameters. The collection device transmits the data to the data transmission device. The data transmission device collects the data through the bus and transmits the data to the server through the Internet for storage and processing;

[0063] S2. The server receives and stores data transmitted by the data transmission device, and performs statistics on the data by day, month, year and peak power consumption period;

[0064] S3, remote monitoring equipment connects to the server to display data in real time, and can set alarm thresholds. When data exceeds the threshold, it will automatically trigger an alarm to remind the monitoring personnel;

[0065] S4. Analyze on-site data to evaluate the operating status and performance of the power system, predict faults through the fault prediction probability formula to provide early warnings, and use the energy efficiency index formula to evaluate the potential for energy efficiency improvement of power equipment to help save energy and reduce consumption;

[0066] S5. Operation and maintenance personnel make optimization decisions based on the analysis and prediction results, adjust the operating parameters of power-consuming equipment, optimize the grid structure, and use the maintenance priority formula to determine the equipment maintenance priority and arrange maintenance plans to ensure reliable operation of the equipment.

[0067] like Figure 3 As shown, the power operation and maintenance big data monitoring system includes: data acquisition and transmission module, data storage and processing module, remote monitoring and alarm module, data analysis and prediction module and optimization decision and maintenance module.

[0068] The data acquisition and transmission module includes data acquisition equipment and data transmission equipment, which are used to collect data from the power operation and maintenance site in real time and transmit the data to the server for storage and processing. The data acquisition and transmission module also has the function of comprehensively evaluating the overall performance and status of the power grid acquisition device, including calculating the data transmission balance value, performance stability value and power balance value, providing a basis for equipment maintenance and management.

[0069] like Figure 4 As shown, the data acquisition equipment includes multiple monitoring terminals, and the data transmission equipment adopts a communication management machine. The multiple monitoring terminals are connected to the communication management machine through a bus; the monitoring terminals are arranged at the electrical equipment on site, and are used to connect with sensors and data acquisition circuits to collect operating data or status data of the electrical equipment. The communication management machine has a communication module for communicating with a server or a remote monitoring device, and different operation and maintenance areas are set according to geographical locations, and each operation and maintenance area is provided with the data acquisition device and the data transmission equipment.

[0070] like Figure 2 As shown, in order to predict potential problems and take preventive measures in time, it is necessary to comprehensively evaluate the overall performance and status of the power grid collection device, as follows:

[0071] S11. Calculate the data transmission balance value of each power grid collection device to evaluate the stability and reliability of data transmission. The data transmission balance value calculation formula is:

[0072] ;in, Indicates The data transmission balance value of each power grid collection device, Indicates The highest amplitude in the time domain of each power grid collection device, Indicates The lowest amplitude in the time domain of each power grid collection device can be calculated. By calculating the average of the highest and lowest amplitudes in the time domain, the fluctuation range of data transmission can be understood, and then its stability can be evaluated, which helps to identify and solve the unstable factors that may exist in the data transmission process, ensure the accuracy and reliability of the data, and thus improve the monitoring efficiency of the overall system;

[0073] S12. Calculate the performance stability value of each power grid collection device to evaluate the performance stability and power consumption efficiency of the device. The performance stability value calculation formula is:

[0074] ;in, Indicates Performance stability value of each power grid collection device; Indicates The power consumption efficiency index of a power grid collection device is calculated as follows:

[0075] ;in, For the The actual average power consumption of each device, is the reference power consumption value; Indicates The operation stability index of a power grid collection device is calculated as follows:

[0076] ;in, For the The actual data sampling rate of each device, is the reference data sampling rate of the device; Indicates The response performance index of a power grid collection device is calculated as follows:

[0077] ;in, For the The average response time of each device, is the expected response time reference value; , , are the weight coefficients of power consumption efficiency, operation stability and response performance, and satisfy + + =1;

[0078] Power efficiency index :By dividing the reference power consumption The actual power consumption The ratio of is used to measure the energy efficiency of the equipment; the higher A value of indicates that the device has a high power consumption efficiency, i.e., the actual power consumption is low;

[0079] Operation stability index : Through the actual data sampling rate With reference data sampling rate The closer the value is to 1, the better the sampling rate stability of the device.

[0080] Response performance indicators : By expected response time The actual response time The ratio of the device to measure the response speed; the higher the Indicates that the device has good response performance;

[0081] By combining the above three indicators and setting the weight coefficient according to actual needs , , , can comprehensively evaluate the performance stability and power consumption efficiency of each power grid collection device, and provide a basis for equipment maintenance and optimization;

[0082] S13. Calculate the power balance value of each power grid collection device to monitor whether the power output of the equipment is balanced. The power balance value calculation formula is:

[0083] ;in, Indicates The power balance value of each power grid collection device, Indicates The highest reference active power of each grid collection device, Indicates The highest reference reactive power of each power collection device. By calculating the average value of active power and reactive power, the balance of equipment power output can be evaluated. This monitoring function helps to ensure the stable operation of the power collection device under different load conditions and avoid equipment damage or grid instability caused by unbalanced power output;

[0084] S14. Comprehensively evaluate the monitoring evaluation value of each power grid collection device to provide a basis for equipment maintenance and management. The monitoring evaluation value calculation formula is:

[0085] ;in, Indicates The monitoring evaluation value of each power grid collection device, , , Respectively represent the weight coefficients of data transmission balance value, performance stability value and power balance value, and Through comprehensive weighted calculation of data transmission, performance stability and power balance, the monitoring evaluation value is obtained. This value can be used as an important reference for equipment maintenance and management, helping decision makers to prioritize equipment maintenance and allocate resources. By comprehensively monitoring and evaluating various performance indicators of power grid collection devices, the system can identify potential problems in advance and take preventive measures in time, thereby reducing the probability of unplanned downtime and equipment failure, and improving the reliability and safety of the entire power system.

[0086] The data storage and processing module is used to receive and store field data, and to process and perform statistical analysis on the data.

[0087] The remote monitoring and alarm module is used to display the field data in real time, set the alarm threshold, and send an alarm signal when the monitoring parameters are abnormal, ensuring that the alarm signal can be sent in time when the monitoring parameters are abnormal. The alarm threshold setting formula is:

[0088] ;in, Indicates the alarm threshold, Indicates the historical average value of a monitoring parameter. Indicates the historical standard deviation of the monitoring parameter. It represents the critical value of the standard normal distribution selected according to the required alarm sensitivity. Through real-time monitoring and automatic alarm, the system can identify and respond to abnormal situations in the first time to ensure the safety and stability of the site. The setting of the alarm threshold is based on the statistical analysis of historical data and can adapt to the monitoring needs of different environments and conditions. The system can flexibly adapt to different alarm sensitivity requirements and use statistical methods to set thresholds, which can effectively reduce false alarms and missed alarms and improve the accuracy and reliability of alarms.

[0089] The data analysis and prediction module is used to analyze field data, evaluate the operating status of the power system, predict possible failures, and evaluate the energy efficiency of electrical equipment. The data analysis and prediction module can also calculate the failure prediction probability of each operation and maintenance area and the energy efficiency index of each electrical equipment to gain a more comprehensive understanding of the operating status of the power system.

[0090] Calculate the failure prediction probability of each operation and maintenance area to identify potential failure areas in advance. The failure prediction probability calculation formula is:

[0091] ;in, represents the failure prediction probability, Indicates The historical failure rate of each operation and maintenance area, Indicates The importance score of each operation and maintenance area, Represents the total number of operation and maintenance areas. By taking a weighted average of the historical failure rate and the importance of the area, the failure prediction probability of each area is obtained. This can identify which areas are more likely to fail, so that these areas can be monitored and maintained in a focused manner.

[0092] Historical failure rate : Indicates the frequency of failures in a certain operation and maintenance area over the past period of time; by analyzing historical failure data, areas with high failure rates can be identified;

[0093] Importance Rating : Indicates the importance of the operation and maintenance area in the entire power system; the score can be based on multiple factors, such as the load of the area, the number of key equipment, the impact on the overall system, etc.; the importance score helps determine which areas need priority attention;

[0094] A comprehensive failure prediction probability is obtained by taking a weighted average of the historical failure rate and importance score; ensuring that high-importance areas have a greater weight in the calculation, thereby more accurately reflecting the risks of these areas;

[0095] The calculation results can help identify areas with a higher risk of failure; these areas require enhanced monitoring and maintenance to reduce the possibility of failure; based on the predicted probability of failure, the operation and maintenance team can reasonably allocate resources and concentrate more manpower and material resources on high-risk areas; this helps to improve operation and maintenance efficiency and reduce overall operation and maintenance costs; by identifying potential failure areas in advance, the operation and maintenance team can implement preventive maintenance strategies; this can not only reduce the risk of power outages and equipment damage, but also extend the service life of the equipment.

[0096] Calculate the energy efficiency index of each electrical device and evaluate the energy consumption level of the device. The energy efficiency index formula is:

[0097] ;in, represents the energy efficiency index, Indicates The power of electrical equipment, Indicates The actual operating time of each electrical device, Represents the total number of electrical devices. The energy efficiency index is calculated by taking a weighted average of the power and operating time of each device. This index can help identify high-energy-consuming devices and take measures to improve their energy efficiency.

[0098] By calculating the probability of fault prediction, potential fault areas can be identified in advance, targeted preventive maintenance can be carried out, the occurrence of sudden failures can be reduced, and the reliability of the system can be improved; the energy efficiency index provides a quantitative indicator for evaluating the energy consumption level of equipment, which helps to identify and optimize high-energy consumption equipment, reduce energy consumption, and improve economic benefits; through the analysis of fault prediction and energy efficiency, the data analysis and prediction module can fully understand the operating status of the power system and provide strong support for decision-making.

[0099] The optimization decision and maintenance module is used to make optimization decisions, adjust the operating parameters of electrical equipment, and reasonably arrange maintenance plans based on the analysis and prediction results. The optimization decision and maintenance module uses the maintenance priority calculation formula to determine the maintenance priority of the equipment.

[0100] Calculate the maintenance priority of the equipment and arrange the maintenance plan according to the failure prediction probability, energy efficiency index and equipment age. The maintenance priority calculation formula is:

[0101] ;in, Indicates the maintenance priority, , , represent the weight coefficients of failure prediction probability, energy efficiency index and equipment age, respectively, and , Indicates the operating age or aging degree of the equipment.

[0102] Reflects the possibility of equipment failure in the future. Equipment with a high predicted failure probability requires a higher maintenance priority to prevent sudden failures. Reflects the energy efficiency of the equipment. Equipment with low energy efficiency may need to be optimized or maintained to reduce energy consumption and improve the overall energy efficiency of the system; Reflects the operating age or aging degree of the equipment. The longer the equipment is in operation, the higher the aging degree is. Usually, a higher maintenance priority is required to ensure its normal operation. By comprehensively considering the fault prediction probability, energy efficiency index and equipment age, the system can scientifically determine the maintenance priority of the equipment and ensure the rational allocation of maintenance resources. High-priority equipment can be maintained in a timely manner to prevent the occurrence of potential failures, reduce unplanned downtime and maintenance costs, and improve system reliability. By adjusting the operating parameters of electrical equipment and optimizing maintenance plans, the system can improve energy utilization efficiency, reduce operating costs, and enhance overall economic benefits.

[0103] Comprehensively evaluate the overall health status of the power grid collection device, the comprehensive equipment health index formula is:

[0104] ;in: Indicates Comprehensive equipment health index of each power grid collection device; Indicates the historical failure rate, ranging from 0 to 1, where 1 indicates a high failure rate; Respectively represent the weight coefficients of each indicator, and , which can help operation and maintenance personnel quickly evaluate the health status of each device and give priority to devices with lower health indexes to improve the reliability and efficiency of the overall system.

[0105] This solution covers the entire process from data collection, transmission, storage, processing to data analysis, prediction, optimization decision-making and maintenance management by building a complete monitoring system. It uses a variety of calculation formulas and indicators to quantitatively evaluate key factors such as the operating status of the power system, equipment performance, fault prediction, energy efficiency, etc., to help operation and maintenance personnel promptly identify potential problems, reasonably formulate maintenance plans, optimize the operating parameters of the power system, improve energy utilization efficiency, and reduce operating costs. It improves the automation and intelligence level of power operation and maintenance, and ensures the safe, stable and efficient operation of the power system.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A monitoring method based on power operation and maintenance big data, characterized in that: The steps include: S1. Set up a power grid collection device at the power operation and maintenance site to collect the operation, status parameters and environmental parameters of the power equipment. The power grid collection device transmits the data to the data transmission device. The data transmission device collects the data through the bus and transmits the data to the server through the Internet for storage and processing; Comprehensively evaluate the overall performance and status of the power grid collection device: S11. Calculate the data transmission balance value of each power grid collection device to evaluate the stability and reliability of data transmission. The data transmission balance value calculation formula is: ;in, Indicates The data transmission balance value of each power grid collection device, Indicates The highest amplitude in the time domain of each power grid collection device, Indicates The lowest amplitude in the time domain of each power grid collection device; S12. Calculate the performance stability value of each power grid collection device to evaluate the performance stability and power consumption efficiency of the device. The performance stability value calculation formula is: ;in, Indicates Performance stability value of each power grid collection device; Indicates The power consumption efficiency index of a power grid collection device is calculated as follows: ;in, For the The actual average power consumption of each device, is the reference power consumption value; Indicates The operation stability index of a power grid collection device is calculated as follows: ;in, For the The actual data sampling rate of each device, is the reference data sampling rate of the device; Indicates The response performance index of a power grid collection device is calculated as follows: ;in, For the The average response time of each device, is the expected response time reference value; , , are the weight coefficients of power consumption efficiency, operation stability and response performance, and satisfy + + =1; S13. Calculate the power balance value of each power grid collection device to monitor whether the power output of the equipment is balanced. The power balance value calculation formula is: ;in, Indicates The power balance value of each power grid collection device, Indicates The highest reference active power of each grid collection device, Indicates The highest reference reactive power of each grid collection device; S14. Comprehensively evaluate the monitoring evaluation value of each power grid collection device to provide a basis for equipment maintenance and management. The monitoring evaluation value calculation formula is: ;in, Indicates The monitoring evaluation value of each power grid collection device, , , Respectively represent the weight coefficients of data transmission balance value, performance stability value and power balance value, and ; S2. The server receives and stores data transmitted by the data transmission device, and performs statistics on the data by day, month, year and peak power consumption period; S3, remote monitoring equipment connects to the server to display data in real time, and can set alarm thresholds. When data exceeds the threshold, it will automatically trigger an alarm to remind the monitoring personnel; S4. Analyze on-site data to evaluate the operating status and performance of the power system, predict faults through the fault prediction probability formula to provide early warnings, and use the energy efficiency index formula to evaluate the potential for energy efficiency improvement of power equipment to help save energy and reduce consumption; S5. Operation and maintenance personnel make optimization decisions based on analysis and prediction results, adjust the operating parameters of power-consuming equipment, optimize the grid structure, and use the maintenance priority formula to determine the equipment maintenance priority and arrange maintenance plans to ensure reliable operation of equipment; In S4, the fault prediction probability of each operation and maintenance area is calculated to identify potential fault areas in advance. The fault prediction probability calculation formula is: ;in, represents the failure prediction probability, Indicates The historical failure rate of each operation and maintenance area, Indicates The importance score of each operation and maintenance area, Indicates the total number of operation and maintenance areas; In S4, the energy efficiency index of each electrical device is calculated to evaluate the energy consumption level of the device. The energy efficiency index formula is: ;in, represents the energy efficiency index, Indicates The power of electrical equipment, Indicates The actual operating time of each electrical device, Indicates the total number of electrical equipment; In S5, the maintenance priority of the equipment is calculated, and the maintenance plan is arranged according to the failure prediction probability, energy efficiency index and equipment age. The maintenance priority calculation formula is: ;in, Indicates the maintenance priority, , , represent the weight coefficients of failure prediction probability, energy efficiency index and equipment age, respectively, and , Indicates the operating age or aging degree of the equipment.

2. The method for monitoring power operation and maintenance based on big data according to claim 1 is characterized in that: In S3, the alarm threshold is set to ensure that an alarm signal can be issued in time when the monitoring parameters are abnormal. The alarm threshold setting formula is: ;in, Indicates the alarm threshold, Indicates the historical average value of a monitoring parameter. Indicates the historical standard deviation of the monitoring parameter. Represents the critical value of the standard normal distribution selected according to the required alarm sensitivity.

3. A power operation and maintenance big data monitoring system, which is applied to the power operation and maintenance big data monitoring method as claimed in claim 2, characterized in that: include: The data collection and transmission module includes a power grid collection device and a data transmission device, which is used to collect data from the power operation and maintenance site in real time and transmit the data to the server for storage and processing. The data collection and transmission module also has the function of comprehensively evaluating the overall performance and status of the power grid collection device, including calculating the data transmission balance value, performance stability value and power balance value, providing a basis for equipment maintenance and management; Data storage and processing module, used to receive and store field data, and to process and statistically analyze the data; Remote monitoring and alarm module, used to display field data in real time, set alarm thresholds, and send alarm signals when monitoring parameters are abnormal; The data analysis and prediction module is used to analyze field data, evaluate the operating status of the power system, predict possible failures, and evaluate the energy efficiency of power equipment. The data analysis and prediction module can also calculate the failure prediction probability of each operation and maintenance area and the energy efficiency index of each power equipment to gain a more comprehensive understanding of the operating status of the power system; The optimization decision and maintenance module is used to make optimization decisions, adjust the operating parameters of electrical equipment, and reasonably arrange maintenance plans based on the analysis and prediction results. The optimization decision and maintenance module uses the maintenance priority calculation formula to determine the maintenance priority of the equipment.

4. The power operation and maintenance big data monitoring system according to claim 3 is characterized in that: The power grid collection device includes multiple monitoring terminals, and the data transmission equipment adopts a communication management machine. The multiple monitoring terminals are connected to the communication management machine through a bus; the monitoring terminals are arranged at the electrical equipment on site, and are used to connect with sensors and data acquisition circuits to collect operating data or status data of the electrical equipment. The communication management machine has a communication module for communicating with a server or a remote monitoring device.

5. The power operation and maintenance big data monitoring system according to claim 4 is characterized in that: Different operation and maintenance areas are set up according to geographical locations, and each operation and maintenance area is provided with the power grid collection device and data transmission equipment.

6. The power operation and maintenance big data monitoring system according to claim 5 is characterized in that: Comprehensively evaluate the overall health status of the power grid collection device, the comprehensive equipment health index formula is: ;in: Indicates Comprehensive equipment health index of each power grid collection device; Indicates the historical failure rate, ranging from 0 to 1, where 1 indicates a high failure rate; Respectively represent the weight coefficients of each indicator, and .

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