Method for evaluating influence of digital technology on retirement of power grid equipment

Abstract

According to the method for evaluating the influence of the digitization technology on the decommissioning of the power grid equipment, the decommissioning scoring model is constructed by selecting key features, the influence of different digitization technologies on the features is analyzed, the equipment decommissioning decision is optimized, and the scientificity and effectiveness of power grid equipment management are improved. The method comprises the following steps: firstly, selecting three key features of the aging degree, the failure rate and the cost of power grid equipment, performing weighted scoring on different equipment, synthesizing the features to calculate the total score of the equipment, and verifying the accuracy of a model by using historical data; secondly, analyzing the influence of different digital technologies on the key characteristic values of the power grid equipment, including predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management technologies; and finally, based on the selected key features and the influence of the digital technology, establishing system dynamics model analog simulation, setting different digital technology application scenarios, and simulating the influence of the digital technology application scenarios on the equipment features and decommissioning. The method is helpful for developing decision support for promoting popularization and application of a power grid equipment management digital technology.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid equipment management and optimization, and in particular to a method for evaluating the impact of digital technology on power grid equipment retirement, which is used to improve the management efficiency and life prediction accuracy of power grid equipment. Background Art

[0002] As the service life of power grid equipment increases, the problem of equipment aging becomes increasingly prominent. Therefore, scientifically evaluating and optimizing equipment retirement strategies has become a key challenge for power grid management. Most of the current equipment retirement methods are based on simple life prediction models, which fail to fully consider key factors such as equipment failure rate and cost. More importantly, related studies have failed to take into account the popularity of digital technology and its impact on power grid equipment retirement. Therefore, it is urgent to develop a retirement assessment model that comprehensively considers multiple factors such as equipment aging, failure rate, and cost, and use system dynamics methods to deeply analyze the impact of digital technology participating in equipment management on equipment retirement, so as to improve the scientificity and efficiency of power grid equipment retirement decisions. Summary of the invention

[0003] The present invention aims to provide a method for evaluating the impact of digital technology on the retirement of power grid equipment. By selecting key features to construct a retirement scoring model and analyzing the impact of different digital technologies on these features, the equipment retirement decision-making is optimized and the scientificity and effectiveness of power grid equipment management are improved.

[0004] In order to solve the above technical problems, the technical solution of the present invention is: a method for evaluating the impact of digital technology on power grid equipment retirement, the steps comprising:

[0005] Step 1: Select three key features of power grid equipment: aging degree, failure rate, and cost, give weighted scores to different equipment, calculate the total score of the equipment based on these features, and use historical data to verify the accuracy of the model;

[0006] Step 2: Analyze the impact of different digital technologies on key characteristic values ​​of power grid equipment, including predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management technology;

[0007] Step 3: Based on the selected key features and the impact of digital technology, a system dynamics model simulation is established, and different digital technology application scenarios are set to simulate their impact on equipment characteristics and retirement.

[0008] Preferably, it is characterized in that: the three key characteristics of the power grid equipment aging degree, failure rate and maintenance cost, the aging degree of the power grid equipment is determined by Weibull distribution, the Weibull distribution consists of a scale parameter α and a shape parameter β, and the parameter estimation method is as follows:

[0009] (1) Collect data on similar equipment under the same operating conditions, including retired equipment and in-service equipment. Statistics are kept on the year of commissioning and the year of retirement of retired equipment, and statistics are kept on in-service equipment, and data on the year of commissioning are collected.

[0010] (2) For retired equipment, its service time is the difference between the retirement year and the commissioning year. For in-service equipment, its service time is the difference between the current year and the commissioning year. The discrete failure probability of each year is the number of retired equipment in that year divided by the corresponding number of in-service equipment. The survival probability of each year is equal to the difference between 1 and the cumulative failure probability of that year.

[0011] (3) The reliability function of the Weibull distribution model is:

[0012]

[0013] Where α and β are scale parameter and shape parameter respectively, R is the survival probability, and t is age.

[0014] (4) When evaluating the aging degree of equipment, it is necessary to consider both the equipment's repairable failure and unavailability rate and the aging failure and unavailability rate caused by aging.

[0015] The repairable failure unavailability rate of the equipment is:

[0016]

[0017] Where: f is the average failure frequency (times / year), MTTR (mean time to repair) is the average repair time (h / time).

[0018] The aging failure unavailability rate of the equipment is:

[0019]

[0020] Where: T and t are the service age and subsequent time interval respectively, t is divided into N time periods, and the length of each time period is D; P is the failure probability in the i-th time period.

[0021] In principle, T in the model should preferably be the "functional age" that reflects the degree of wear and tear. This functional age is related to the equipment's usage history, maintenance status, and actual state. However, since the functional age is difficult to determine, in system planning, using the natural age of the equipment can generally produce acceptable results.

[0022] Applying the concept of union, the aging degree including repairable summer failure and aging failure is:

[0023] U t =U r +U a -U r Ua

[0024] Preferably, the three key characteristics of the power grid equipment are the degree of aging, failure rate and cost. The failure rate of the power grid equipment is determined by referring to the state maintenance decision management field in the PMS system of the State Grid Corporation of China and Q / GDW645-2011 "Guidelines for the Status Evaluation of Distribution Equipment". The two-parameter Weibull distribution is used to express the relationship between the equipment failure rate and time:

[0025]

[0026] Where β is the shape parameter, which characterizes the shape of the distribution curve. When β<1, the failure rate shows a downward trend; when B=1, the failure rate is a constant; when β>1, the failure rate shows an upward trend; 0 is the scale parameter, which characterizes the coordinate scale; b is the random failure coefficient.

[0027] Preferably, the three key characteristics of the power grid equipment are aging degree, failure rate and cost, and the cost includes operation and maintenance cost, failure cost, repair cost and depreciation loss:

[0028] C=C1+C2+C3+C4

[0029] In the formula, C is the cost, C1 is the operation and maintenance cost, C2 is the failure cost, C3 is the maintenance cost, and C4 is the depreciation loss.

[0030] Preferably, the system dynamics model includes determining the model boundary, drawing a cause-effect relationship diagram, drawing an inventory and flow diagram, and scenario analysis, including determining the main variables and boundary conditions involved, including equipment life, failure rate, maintenance cost, degree of application of digital technology, etc.; drawing a cause-effect relationship diagram to show the mutual influence and feedback relationship between the variables; drawing an inventory and flow diagram covering the application of different digital technologies; setting different scenarios to simulate the impact of digital technology application on equipment retirement.

[0031] The present invention has the following beneficial effects: The present invention constructs a retirement scoring model by selecting three key characteristics of power grid equipment: degree of aging, failure rate, and maintenance cost, and uses historical data to verify the accuracy of the model to ensure that the scoring criteria are in line with the actual retirement situation. Based on system dynamics, the impact of different digital technologies (such as predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management) on equipment life, failure rate, and maintenance cost is analyzed, a system dynamics model is established, simulations are performed, and different digital technology application scenarios are set to simulate their impact on equipment characteristics and retirement. By optimizing and verifying the model, it is applied to actual equipment retirement decisions, which improves the scientificity and effectiveness of power grid equipment retirement decisions. The present invention comprehensively considers the application of system dynamics in power grid equipment management and the improvement of equipment management by digital technology. It can solve the shortcomings of traditional retirement methods, optimize the management and maintenance strategies of power grid equipment, and improve the economy and accuracy of equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The flowchart of the retirement scoring model of the present invention is as follows;

[0033] Figure 2 System dynamics diagram considering the impact of digital technology for the present invention;

[0034] Figure 3 Flow chart of the decommissioning method for equipment evaluation of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] Please refer to Figure 1 , Figure 2 and Figure 3 The present invention selects three key features of power grid equipment aging, failure rate, and maintenance cost to construct a retirement scoring model, and uses historical data to verify the accuracy of the model. Based on system dynamics, the impact of different digital technologies on equipment aging, failure rate, and maintenance cost is analyzed, and a system dynamics model is established to perform simulations, set different digital technology application scenarios, and simulate their impact on equipment characteristics and retirement. The steps include:

[0037] Step 1: Select the three key characteristics of power grid equipment: aging degree, failure rate and cost, give weighted scores to different equipment, calculate the total score of the equipment based on these characteristics, and use historical data to verify the accuracy of the model. The specific analysis steps are as follows:

[0038] (1) The aging degree includes the equipment repairable failure unavailability rate and the aging failure rate. The equipment repairable failure unavailability rate is calculated as follows:

[0039]

[0040] Where: f is the average failure frequency (times / year), MTTR (mean time to repair) is the average repair time (h / time).

[0041] The aging failure rate of the equipment is calculated as follows:

[0042]

[0043] Where: T and t are the service age and subsequent time interval respectively, t is divided into N time periods, and the length of each time period is D; P is the failure probability in the i-th time period.

[0044] In principle, T in the model should preferably be the "functional age" that reflects the degree of wear and tear. This functional age is related to the equipment's usage history, maintenance status, and actual state. However, since the functional age is difficult to determine, in system planning, using the natural age of the equipment can generally produce acceptable results.

[0045] Applying the concept of union, the aging degree including repairable summer failure and aging failure is:

[0046] U t =U r +U a -U r U a

[0047] (2) The failure rate is expressed using a two-parameter Weibull distribution:

[0048]

[0049] Where β is the shape parameter, which characterizes the shape of the distribution curve. When β<1, the failure rate shows a downward trend; when B=1, the failure rate is a constant; when β>1, the failure rate shows an upward trend; θ is the scale parameter, which characterizes the coordinate scale; b is the random failure coefficient.

[0050] (3) Costs include operation and maintenance costs, failure costs, repair costs, and depreciation losses.

[0051] C=C1+C2+C3+C4

[0052] In the formula, C is the cost, C1 is the operation and maintenance cost, C2 is the failure cost, C3 is the maintenance cost, and C4 is the depreciation loss.

[0053] (4) The weighted scoring method for comprehensive feature equipment is shown in the following formula:

[0054]

[0055] V=ω1X1+ω2X2+ω3X3

[0056] Where V is the score of the power grid equipment, X1, X2, and X3 are the normalized values ​​of aging degree, failure rate, and cost characteristics, respectively, and ω1, ω2, and ω3 are the weight coefficients of the characteristics.

[0057] According to the comprehensive score V, the devices are divided into different equipment health status levels, as shown in Table 1.

[0058] Table 1 Equipment health status level

[0059]

[0060] The above is the process of weighted scoring of different devices in step 1.

[0061] Step 2: Analyze the impact of different digital technologies on the key characteristic values ​​of power grid equipment, including predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management technology. The impact of specific digital technologies on the key characteristic values ​​of power grid equipment is as follows:

[0062] (1) Predictive maintenance: By installing high-precision sensors on power grid equipment to collect parameters such as current, voltage, temperature, and vibration, machine learning algorithms are used to analyze sensor data, identify abnormal conditions and potential failures of equipment, and formulate maintenance plans in advance. Through early prediction and maintenance, the number of unplanned downtime of equipment is reduced, and the failure rate is significantly reduced; early maintenance measures avoid long-term operation of equipment under high load, extend the service life of equipment, and slow down the degree of aging; through targeted maintenance plans, the number and cost of emergency repairs are reduced, reducing overall costs.

[0063] (2) Intelligent dispatching uses big data and AI technology to monitor and predict the load in the power grid in real time. Based on the prediction results, the dispatching and operation strategies of power grid equipment are optimized to avoid equipment overload and frequent start and stop. By optimizing dispatching, equipment overload and frequent start and stop are avoided, and the wear and failure rate of equipment are reduced; the optimized dispatching strategy reduces the stress and loss of equipment and slows down the aging of equipment.

[0064] (3) Remote monitoring and control Through the Internet of Things technology, power grid equipment is connected to the central monitoring system to achieve remote monitoring and control. When an abnormality occurs in the equipment, the monitoring system automatically issues an alarm and performs fault diagnosis and troubleshooting through remote control. Through remote monitoring and diagnosis, the number and cost of on-site maintenance are reduced; rapid response and timely maintenance reduce equipment damage, extend the service life of equipment, and slow down aging.

[0065] (4) Digital file management: A digital file is established for each power grid device to record the equipment model, specifications, service life, maintenance records and other information. The historical maintenance and operation data recorded in the digital file is used to evaluate the equipment status and predict faults. Accurate maintenance decisions can reduce unnecessary maintenance and replacement, and reduce costs; scientific maintenance decisions and timely maintenance measures can improve equipment reliability and slow down equipment aging.

[0066] Step 3: Based on the selected key features and the impact of digital technology, a system dynamics model simulation is established, and different digital technology application scenarios are set to simulate their impact on equipment characteristics and retirement.

[0067] Step 3: The specific process is as follows:

[0068] Step 3.1: Select the number of equipment and comprehensive classification as inventory variables (Levels), select the equipment retirement rate as the flow variable (Rates), select the equipment aging degree, equipment failure rate, equipment cost, operation and maintenance cost, failure cost, maintenance cost, depreciation loss, predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management as auxiliary variables (Auxiliary Variables);

[0069] Step 3.2: Establish connections and feedback loops. Use the Arrow tool to connect auxiliary variables to corresponding inventory and flow variables to establish feedback loops. For example, the degree of equipment aging, failure rate, and cost jointly determine the comprehensive score of the equipment. Changes in the comprehensive score of the equipment affect the retirement rate of the equipment, which in turn affects the number of equipment.

[0070] Step 3.3: Set different digital application scenarios, including low coverage, medium coverage, and high coverage of digital technology. By running the system dynamics model under different scenarios, we can obtain the dynamic changes of the comprehensive score of the equipment and how digital technology affects the retirement rate of equipment. By comparing and analyzing these results, we can clearly understand the impact of digital technology on power grid equipment management under different coverage or popularity, thereby providing strong support for optimizing the application strategy of digital technology.

[0071] To sum up, the implementation principle of the present invention is to establish an equipment health assessment model based on the degree of equipment aging, equipment failure rate and cost key factors, and use the system dynamics model to evaluate the changes in the comprehensive score of equipment under different digital application scenarios, so as to realize the health assessment and retirement planning of power grid equipment. This method is reasonable in calculation, respects facts in design, and is suitable for promotion.

[0072] The parts not involved in the present invention are the same as the prior art or are implemented by using the prior art.

[0073] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for evaluating the impact of digital technology on power grid equipment retirement, characterized in that: include: Step 1: Select three key features of power grid equipment: aging degree, failure rate, and cost, give weighted scores to different equipment, calculate the total score of the equipment based on these features, and use historical data to verify the accuracy of the model; Step 2: Analyze the impact of different digital technologies on key characteristic values ​​of power grid equipment, including predictive maintenance, intelligent scheduling, remote monitoring and control, and digital archive management technology; Step 3: Based on the selected key features and the impact of digital technology, a system dynamics model simulation is established, and different digital technology application scenarios are set to simulate their impact on equipment characteristics and retirement.

2. The method for evaluating the impact of digital technology on power grid equipment retirement according to claim 1 is characterized in that: The aging degree of the power grid equipment is determined by Weibull distribution, and the parameter estimation method is as follows: (1) Collect data on similar equipment under the same operating conditions, including retired equipment and in-service equipment. Statistics on the year of commissioning and the year of retirement of retired equipment, statistics on in-service equipment, and data on the year of commissioning; (2) For retired equipment, its service time is the difference between the retirement year and the commissioning year. For in-service equipment, its service time is the difference between the current year and the commissioning year. The discrete failure probability of each year is the number of retired equipment in that year divided by the corresponding number of in-service equipment. The survival probability of each year is equal to the difference between 1 and the cumulative failure probability of that year; (3) The reliability function of the Weibull distribution model is: Where: α and β are scale parameter and shape parameter respectively, R is the survival probability, t is age; (4) When evaluating the aging degree of equipment, it is necessary to consider the equipment’s repairable failure unavailability rate u r , and the aging failure unavailability rate u caused by aging should also be considered a . Equipment repairable failure unavailability rate U r for: Where: f is the average failure frequency (times / year), MTTR is the average repair time (h / time); Equipment aging failure unavailability rate U a for: Where: T and t are the service age and follow-up time interval respectively, t is divided into N time periods, and the length of each time period is D; P is the failure probability in the i-th time period; Applying the concept of union, the total aging degree including repairable failures and aging failures is: IN t =U r +in a -in r IN a 。 3. The method for evaluating the impact of digital technology on power grid equipment retirement according to claim 2 is characterized in that: The three key characteristics of the power grid equipment are aging failure rate, failure rate and cost. The two-parameter Weibull distribution is used to express the relationship between equipment failure rate and time: Where β is the shape parameter, which characterizes the shape of the distribution curve. When β<1, the failure rate shows a downward trend; when B=1, the failure rate is a constant; when β>1, the failure rate shows an upward trend; θ is the scale parameter, which characterizes the coordinate scale; and b is the random failure coefficient.

4. The method for evaluating the impact of digital technology on power grid equipment retirement according to claim 3 is characterized in that: The three key characteristics of the power grid equipment are the degree of aging, failure rate and cost. The cost includes operation and maintenance cost, failure cost, maintenance cost and depreciation loss. The cost includes the following: C=C1+C2+C3+C4 In the formula, C is the cost, C1 is the operation and maintenance cost, C2 is the failure cost, C3 is the maintenance cost, and C4 is the depreciation loss.

5. The method for evaluating the impact of digital technology on power grid equipment retirement according to claim 4 is characterized in that: The comprehensive feature calculation device scoring method is shown in the following formula: V=ω1X1+ω2X2+ω3X3 Where V is the score of the power grid equipment, X1, X2, and X3 are the normalized values ​​of aging degree, failure rate, and cost characteristics, respectively, and ω1, ω2, and ω3 are the weight coefficients of the characteristics.

6. The method for evaluating the impact of digital technology on power grid equipment retirement according to claim 6 is characterized in that: System dynamics models include determining model boundaries, drawing cause-effect diagrams, drawing stock and flow diagrams, and scenario analysis. (1) Determine the model boundaries: determine the main variables and boundary conditions involved, including equipment life, failure rate, maintenance cost, and degree of digital technology application; (2) Cause-effect diagram: Draw a cause-effect diagram to show the mutual influence and feedback relationship between variables; (3) Flow and inventory diagrams: Draw inventory and flow diagrams, covering the application of different digital technologies; (4) Scenario analysis: Set different scenarios to simulate the impact of digital technology application on equipment retirement.

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