Power distribution network operation and maintenance management method and system, electronic equipment and product

Abstract

The invention belongs to the technical field of power distribution network management, and aims to provide a power distribution network operation and maintenance management method and system, electronic equipment and a product. According to the method, the future health condition and fault risk of the power distribution equipment can be dynamically evaluated based on the multi-dimensional parameters, and the operation and maintenance priority of each power distribution equipment in the power distribution network is obtained by combining the health score prediction value and the fault risk prediction value of the power distribution equipment. And the operation and maintenance management scheme of the power distribution network is dynamically adjusted according to the operation and maintenance priority of each power distribution device. The operation and maintenance management scheme can be dynamically adjusted according to the actual state of the power distribution equipment, the purposes of improving the operation and maintenance efficiency of the power distribution network, reducing the maintenance cost and enhancing the reliability of the power distribution network can be achieved, and long-term stable operation of the power distribution network is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network management, and specifically relates to a distribution network operation and maintenance management method, system, electronic equipment and product. Background Art

[0002] The distribution network is a vital part of the power system, responsible for transmitting electricity from substations to end users. As the number and complexity of distribution equipment in the distribution network increase, how to ensure the efficient and reliable operation of distribution equipment has become a core issue in the operation and maintenance management of the distribution network.

[0003] In the prior art, the traditional distribution network operation and maintenance management scheme mainly relies on regular maintenance at fixed time intervals or post-maintenance after the distribution equipment fails. Among them, in the traditional fixed time interval maintenance scheme, the distribution equipment is usually inspected and maintained according to a predetermined cycle, regardless of the actual operating status of the distribution equipment, which can prevent potential failures of some distribution equipment. The post-maintenance method is to repair the distribution equipment after it fails, which can save some operating costs.

[0004] However, in the process of using the prior art, the inventors found that the prior art has at least the following problems: Existing fixed-time interval maintenance plans may cause distribution equipment in good health to be frequently overhauled, resulting in unnecessary waste of resources. At the same time, parts of distribution equipment in poor health may not be overhauled within the scheduled time, causing further deterioration of the distribution equipment, increasing the risk of distribution equipment failure. Post-maintenance plans often lead to unexpected shutdowns of distribution equipment, and even cause chain failures, resulting in more serious economic losses and social impacts. In addition, the above-mentioned existing operation and maintenance management plans are unable to adjust the operation and maintenance management plans in real time according to changes in the status of distribution equipment, making it impossible to dynamically adjust the operation and maintenance resources of the distribution network according to the actual needs of the distribution equipment, which is not conducive to the long-term stable operation of the distribution network. As the scale of the distribution network expands, the types of distribution equipment are diverse, and the operating environment is complex, making it difficult for existing operation and maintenance management methods to meet the needs of intelligent and refined management. Summary of the invention

[0005] The present invention aims to solve the above technical problems at least to a certain extent. The present invention provides a distribution network operation and maintenance management method, system, electronic equipment and product.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a distribution network operation and maintenance management method, comprising: Real-time collection of multi-dimensional real-time parameter sets of each distribution device in the specified distribution network in the current operation cycle, and pre-processing of the multi-dimensional real-time parameter sets of each distribution device to obtain multi-dimensional pre-processed parameter sets of each distribution device; According to the multi-dimensional pre-processed parameter set of each power distribution device, a multi-dimensional prediction parameter of each power distribution device at a specified time in the future is obtained; According to the multi-dimensional prediction parameters of each power distribution equipment, each power distribution equipment is evaluated for health, and the predicted health score value of each power distribution equipment at a specified time in the future is obtained; Obtain a multidimensional historical parameter set of each power distribution device, and perform fault risk prediction on each power distribution device based on the multidimensional historical parameter set, multidimensional real-time parameter set and multidimensional prediction parameters of each power distribution device, to obtain a fault risk prediction value of each power distribution device at a specified time in the future; According to the health score prediction value and fault risk prediction value of each power distribution equipment, the operation and maintenance priority of each power distribution equipment is obtained; According to the operation and maintenance priority of each power distribution device, an operation and maintenance priority sorting list of the specified power distribution network is constructed; wherein the operation and maintenance priority sorting list includes identification information of each power distribution device sorted from large to small according to the operation and maintenance priority; The operation and maintenance management plan of the designated distribution network is optimized according to the operation and maintenance priority sorting list to obtain an optimized operation and maintenance management plan, and the operation and maintenance management of the designated distribution network is performed according to the optimized operation and maintenance management plan.

[0007] In a possible design, a health assessment is performed on any power distribution device according to the multi-dimensional prediction parameters of the power distribution device to obtain a predicted health score value of the power distribution device at a specified time in the future, including: Obtaining a multidimensional parameter threshold of any of the power distribution equipment, and adjusting the multidimensional parameter threshold of any of the power distribution equipment according to the multidimensional predicted parameters of any of the power distribution equipment, to obtain the multidimensional adjusted parameter threshold of any of the power distribution equipment at a specified future time; According to the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters of any power distribution device, a health score prediction value of any power distribution device at a specified future moment is obtained.

[0008] In a possible design, any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values ​​of any power distribution device at a future specified time t' is: X T (t')=X base + α [x(t')-X base )]; Where, X baseis the basic parameter threshold corresponding to any of the adjusted parameter thresholds, α is a preset adjustment coefficient, and x(t') is a prediction parameter corresponding to any of the adjusted parameter thresholds.

[0009] In a possible design, the multi-dimensional prediction parameters of any power distribution equipment at a future specified time t' include a temperature prediction parameter T(t'), a current prediction parameter I(t') and a load prediction parameter L(t'); the multi-dimensional adjusted parameter thresholds of any power distribution equipment at a future specified time t' include an adjusted temperature threshold T T (t'), adjusted current threshold I T (t') and the adjusted load threshold L T (t'); based on the preset SVR model, the predicted health score value of any power distribution equipment at the specified time t' in the future is obtained; correspondingly, the predicted health score value of any power distribution equipment at the specified time t' in the future is: H(t')= f SVR [T T (t'),I T (t'),L T (t'),T(t'),I(t'),L(t')]; In the formula, f SVR [·] represents the SVR model.

[0010] In a possible design, based on a multi-dimensional historical parameter set, a multi-dimensional real-time parameter set, and a multi-dimensional prediction parameter of any power distribution device, a fault risk prediction is performed on the any power distribution device to obtain a fault risk prediction value of the any power distribution device at a specified time in the future, including: Performing time series analysis on a multidimensional historical parameter set and a multidimensional real-time parameter set of any power distribution equipment to extract a multidimensional parameter change trend value; Comparing the multi-dimensional prediction parameters of any power distribution equipment with the multi-dimensional parameter change trend values ​​one by one to obtain the multi-dimensional parameter trend deviation; According to the multi-dimensional parameter trend deviation, a comprehensive failure risk index of any power distribution equipment at a specified time in the future is obtained; According to the comprehensive fault risk index, a predicted fault risk value of any power distribution equipment at a specified future moment is obtained.

[0011] In a possible design, the comprehensive failure risk index of any power distribution equipment at a specified time in the future is: ; In the formula, nis the parameter dimension of any power distribution device, For any of the above-mentioned power distribution equipment i The preset parameter weights of the dimension parameters, For any of the above-mentioned power distribution equipment i The parameter trend deviation of the dimension parameter.

[0012] In a possible design, the operation and maintenance priority of any power distribution equipment is: U = β · [1 - H (t')] + γ · P (t'); Wherein, H(t') is the predicted value of the health score of any power distribution equipment at the specified time t' in the future, β is the preset health score adjustment coefficient, P(t') is the predicted value of the fault risk of any power distribution equipment at the specified time t' in the future, and γ is the preset fault risk adjustment coefficient.

[0013] In a second aspect, the present invention provides a distribution network operation and maintenance management system, which is used to implement the distribution network operation and maintenance management method as described in any one of the above; the distribution network operation and maintenance management system comprises: The parameter acquisition module is used to collect in real time the multi-dimensional real-time parameter set of each distribution device in the specified distribution network in the current operation cycle, and pre-process the multi-dimensional real-time parameter set of each distribution device to obtain the multi-dimensional pre-processed parameter set of each distribution device; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of each power distribution device at a specified time in the future based on the multidimensional preprocessed parameter set of each power distribution device; A health assessment module, which is in communication with the parameter prediction module and is used to perform health assessment on each power distribution device according to the multi-dimensional prediction parameters of each power distribution device, and obtain a predicted health score value of each power distribution device at a specified time in the future; A fault risk prediction module is in communication with the parameter prediction module, and is used to obtain a multidimensional historical parameter set of each power distribution device, and perform fault risk prediction on each power distribution device according to the multidimensional historical parameter set, the multidimensional real-time parameter set and the multidimensional prediction parameters of each power distribution device, so as to obtain a fault risk prediction value of each power distribution device at a specified time in the future; An operation and maintenance priority calculation module, which is respectively connected to the health assessment module and the fault risk prediction module in communication, and is used to obtain the operation and maintenance priority of each power distribution device according to the health score prediction value and the fault risk prediction value of each power distribution device; An operation and maintenance management optimization module is communicatively connected to the operation and maintenance priority calculation module, and is used to construct an operation and maintenance priority sorting list of the designated distribution network according to the operation and maintenance priority of each distribution device; wherein the operation and maintenance priority sorting list includes identification information of each distribution device sorted from large to small according to the operation and maintenance priority; and is also used to optimize the operation and maintenance management plan of the designated distribution network according to the operation and maintenance priority sorting list to obtain an optimized operation and maintenance management plan, and perform operation and maintenance management on the designated distribution network according to the optimized operation and maintenance management plan.

[0014] In a third aspect, the present invention provides an electronic device, comprising: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of the distribution network operation and maintenance management method as described in any one of the above.

[0015] In a fourth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the distribution network operation and maintenance management method as described in any one of the above.

[0016] The beneficial effects of the present invention are: The present invention discloses a distribution network operation and maintenance management method, system, electronic equipment and product, which can dynamically adjust the operation and maintenance management plan according to the actual state of the distribution equipment, so as to achieve the purpose of improving the operation and maintenance efficiency of the distribution network, reducing the maintenance cost and enhancing the reliability of the distribution network. Specifically, during the implementation of the present invention, the future health status and fault risk of the distribution equipment can be dynamically evaluated based on multi-dimensional parameters, and then the operation and maintenance priority of each distribution equipment in the distribution network can be obtained by combining the health score prediction value and the fault risk prediction value of the distribution equipment, and the operation and maintenance management plan of the distribution network can be dynamically adjusted according to the operation and maintenance priority of each distribution equipment. Based on this, the present invention can ensure that the operation and maintenance management plan can flexibly respond to the changes in the state of the distribution equipment, thereby optimizing the allocation of operation and maintenance resources, improving the pertinence and timeliness of the maintenance of the distribution equipment, avoiding the problems of excessive maintenance and insufficient maintenance of the distribution equipment in the prior art, improving the utilization efficiency of the operation and maintenance resources, and at the same time helping to reduce the failure rate of the distribution equipment, the safety and reliability of the distribution network operation are higher, and thus the long-term stable operation of the distribution network can be guaranteed.

[0017] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of a distribution network operation and maintenance management method in an embodiment; Figure 2 is a module block diagram of a distribution network operation and maintenance management system in an embodiment; Figure 3 It is a module block diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0020] Embodiment 1: This embodiment discloses a distribution network operation and maintenance management method, which can be but is not limited to being executed by a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or by a virtual machine.

[0021] like Figure 1 As shown, a distribution network operation and maintenance management method may include but is not limited to the following steps: S1. Real-time collection of multidimensional real-time parameter sets of each distribution equipment in the specified distribution network in the current operation cycle, and preprocessing of the multidimensional real-time parameter sets of each distribution equipment to obtain multidimensional preprocessed parameter sets of each distribution equipment; specifically, in this embodiment, when the distribution equipment is a transformer equipment, the corresponding multidimensional preprocessed parameter set may include but is not limited to temperature parameter sets, current parameter sets, load parameter sets, ambient temperature parameter sets, voltage parameter sets and other operating data that affect the health of the transformer equipment; real-time collection of multidimensional real-time parameter sets of distribution equipment in the current operation cycle, and preprocessing of the multidimensional real-time parameter sets can ensure the data quality of the input model.

[0022] Specifically, in this embodiment, preprocessing is performed on any real-time parameter set in the multi-dimensional real-time parameter set of any power distribution device to obtain a preprocessed parameter set of the any real-time parameter set, including: S101. Perform outlier detection and elimination processing on any real-time parameter set in the multi-dimensional real-time parameter set to obtain a parameter set after outlier elimination; wherein, as an example, in this embodiment, the triple standard deviation method is used to eliminate outliers from the real-time parameter sets of each dimension, and outliers can be screened out according to the mean and standard deviation of the real-time parameter sets of each dimension. The outliers in any real-time parameter set are: x outlier ={x||x-μ|>3σ}; Wherein, x is the data in any real-time parameter set, μ is the mean of any parameter set, and σ is the standard deviation of any parameter set; S102. Normalizing the outlier-removed parameter set to obtain a normalized parameter set; wherein, in the normalized parameter set, the normalized parameter corresponding to any parameter x' in the outlier-removed parameter set is: x norm =(x'-x min ) / (x max -x min ); In the formula, x max and x min are respectively the maximum and minimum values ​​in the parameter set after the outliers are removed; S103. Perform data smoothing processing on the normalized parameter set to obtain a preprocessed parameter set of any real-time parameter set.

[0023] Specifically, in this embodiment, the multi-dimensional pre-processed parameter set includes a temperature parameter set T(t), a current parameter set I(t) and a load parameter set L(t), and each pre-processed parameter set is time series data within the current operation cycle.

[0024] S2. According to the multidimensional preprocessed parameter set of each power distribution device, obtain the multidimensional prediction parameters of each power distribution device at a specified time in the future; Specifically, step S2 is implemented using a Kalman filter algorithm.

[0025] Specifically, in this embodiment, according to the multidimensional preprocessed parameter set of any power distribution device, the multidimensional prediction parameters of any power distribution device at a future specified time t' are obtained, including: S201. According to the multidimensional preprocessed parameter set, predict the multidimensional initial prediction parameters of any power distribution device at a future specified time t'; wherein any initial prediction parameter of the multidimensional initial prediction parameters of any power distribution device at a future specified time t' is: ; In the formula, is a preset state transfer matrix used to describe the change of operating parameters from the current operating cycle to the future specified time t'. is a set of preprocessed parameters corresponding to any of the initial prediction parameters, is the preset input control matrix, is the preset control variable; S202. Obtain the multidimensional current parameters of any power distribution device at the current moment, and update the multidimensional initial prediction parameters according to the multidimensional current parameters to obtain the multidimensional prediction parameters of any power distribution device at a future specified moment t'. Among them, any prediction parameter of the multidimensional prediction parameters of any power distribution device at a future specified moment t' is: ; In the formula, is the preset Kalman gain matrix, is the current parameter corresponding to any of the prediction parameters, is the preset observation matrix.

[0026] Specifically, in this embodiment, the multi-dimensional prediction parameters of each power distribution device at a specified future time t' include a temperature prediction parameter T(t'), a current prediction parameter I(t') and a load prediction parameter L(t').

[0027] S3. Perform health assessment on each power distribution equipment according to the multi-dimensional prediction parameters of each power distribution equipment, and obtain the predicted health score value of each power distribution equipment at a specified time in the future; it should be noted that the health score prediction value is used to quantify the health status of any of the power distribution equipment, and the larger the value, the better the health status of the equipment, and the smaller the value, the worse the health status of the equipment and the existence of potential problems; in this embodiment, the maintenance requirements of any of the power distribution equipment can be determined based on the health score prediction value.

[0028] In step S3, a health assessment is performed on any power distribution device according to the multi-dimensional prediction parameters of the power distribution device to obtain a predicted health score value of the power distribution device at a specified time in the future, including: S301. Obtain multidimensional parameter thresholds of any of the power distribution equipment, and adjust the multidimensional parameter thresholds of any of the power distribution equipment according to the multidimensional predicted parameters of any of the power distribution equipment, to obtain the multidimensional adjusted parameter thresholds of any of the power distribution equipment at a specified future moment; specifically, in this embodiment, when any of the power distribution equipment is a transformer device, the corresponding multidimensional parameter thresholds include the maximum allowable temperature value, current limit value, load tolerance, etc. The preset distribution network operation and management system can dynamically monitor the operating status of any distribution equipment based on the multidimensional parameter thresholds and perform operation and maintenance control on it.

[0029] In step S301 of this embodiment, any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values ​​of any power distribution device at a future specified time t' is: X T (t')=X base + α [x(t')-X base )]; Where, X base is the basic parameter threshold corresponding to any of the adjusted parameter thresholds, α is a preset adjustment coefficient used to control the threshold adjustment amplitude, and x(t') is a prediction parameter corresponding to any of the adjusted parameter thresholds.

[0030] Specifically, in step S3 of this embodiment, the multi-dimensional adjusted parameter threshold of any power distribution device at the future specified time t' includes an adjusted temperature threshold, an adjusted current threshold and an adjusted load threshold. As an example, the adjusted temperature threshold of any power distribution device at the future specified time t' is: T T (t') = T base + α [T(t')-T base )]; Where, T base is the basic temperature threshold, and T(t') is the temperature prediction parameter.

[0031] It should be noted that, in this embodiment, based on the above step S301, the multi-dimensional parameter threshold of any power distribution device can adopt a dynamic threshold, which is adjusted according to the change of the predicted parameter. During the implementation of this embodiment, after obtaining the multi-dimensional adjusted parameter threshold, the real-time operating status of any power distribution device can also be adjusted based on the multi-dimensional adjusted parameter threshold, so that the operating parameters of any power distribution device are kept within a safe range.

[0032] It should be understood that the present embodiment can also perform an operating status evaluation on the multi-dimensional current parameters of any of the power distribution equipment at the current moment based on the multi-dimensional parameter thresholds of any of the power distribution equipment, and the multi-dimensional parameter thresholds are adjusted at historical moments. Based on this, the present embodiment can more accurately reflect the health status of the equipment and avoid excessive or insufficient maintenance.

[0033] S302. Obtain a predicted health score value of any power distribution device at a specified future moment based on the multi-dimensional adjusted parameter thresholds and multi-dimensional prediction parameters of any power distribution device.

[0034] In step S2 of this embodiment, the multi-dimensional prediction parameters of any power distribution equipment at the future specified time t' include temperature prediction parameters T(t'), current prediction parameters I(t') and load prediction parameters L(t'); in step S301, the multi-dimensional adjusted parameter thresholds of any power distribution equipment at the future specified time t' include the adjusted temperature threshold T T (t'), adjusted current threshold I T (t') and the adjusted load threshold L T(t'); In step S302 of this embodiment, the health score prediction value of any power distribution device at the future specified time t' is obtained based on the preset SVR (Support Vector Regression) model, that is, in this embodiment, the SVR model is used as the health prediction model; correspondingly, the health score prediction value of any power distribution device at the future specified time t' is: H(t')= f SVR [T T (t'),I T (t'),L T (t'),T(t'),I(t'),L(t')]; In the formula, f SVR [·] represents the SVR model.

[0035] It should be noted that, in this embodiment, when predicting the health score of any of the power distribution equipment, the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters are input into the preset SVR model, thereby obtaining the health score prediction value of any of the power distribution equipment at a specified time in the future. In this embodiment, the multi-dimensional adjusted parameter thresholds are used as part of the input data of the SVR model to assist in health status prediction, which can ensure that the health assessment results (i.e., the health score prediction values) can take into account the safe operation boundaries of any of the power distribution equipment in a dynamic environment.

[0036] In addition, in this embodiment, the SVR model can be trained based on the multi-dimensional historical parameter thresholds, the relationship between the multi-dimensional historical operating parameters and the historical health scores of any of the power distribution equipment. The correlation between multiple factors is modeled by the SVR model, so that the health assessment results of the power distribution equipment in this embodiment can capture the correlation between multiple operating parameters, and the accuracy of the health score prediction value can be improved.

[0037] It should be noted that this embodiment implements a dynamic health status assessment of the distribution equipment based on the above steps S301 and S302. In this process, the correlation between multi-dimensional operating parameters is taken into account, so that the accuracy of the health assessment results is improved, which solves the problem that the fixed threshold in the prior art cannot cope with the dynamic changes in the operating environment of the distribution equipment. At the same time, in the process of obtaining the predicted health score value of the distribution equipment at a specified time in the future, this embodiment can more accurately predict the health of the distribution equipment through a comprehensive analysis of the multi-dimensional prediction parameters and the corresponding adjustment parameter thresholds, which is conducive to providing more intelligent and timely decision support for the operation and maintenance management of the distribution network.

[0038] S4. Obtain a multidimensional historical parameter set for each distribution device, and perform fault risk prediction on each distribution device based on the multidimensional historical parameter set, multidimensional real-time parameter set and multidimensional prediction parameters of each distribution device, to obtain a fault risk prediction value for each distribution device at a specified future moment; it should be noted that the fault risk prediction value is usually a value between 0 and 1, which represents the probability of a distribution device failing at a specified future moment, and the larger the value, the greater the possibility of a device failure.

[0039] Specifically, statistical analysis or machine learning models can be used to predict the fault risks of power distribution equipment. In order to simplify the workload of fault risk prediction and improve the efficiency of fault analysis and prediction, in step S4 of this embodiment, trend analysis is used to predict the fault risks of power distribution equipment. In this process, the changing patterns of historical, real-time and predicted values ​​of multi-dimensional parameters are analyzed to predict the possibility of future failures of power distribution equipment.

[0040] In step S4 of this embodiment, based on the multidimensional historical parameter set, the multidimensional real-time parameter set and the multidimensional prediction parameter of any power distribution device, a fault risk prediction is performed on the any power distribution device to obtain a fault risk prediction value of the any power distribution device at a specified time in the future, including: S401. Perform time series analysis on the multidimensional historical parameter set and the multidimensional real-time parameter set of any of the power distribution equipment to extract the multidimensional parameter change trend value; specifically, in this embodiment, moving average, exponential smoothing and other methods can be used to extract the parameter change trend value of each dimensional parameter to characterize the rise, fall or fluctuation of different types of parameters.

[0041] S402. Compare the multidimensional prediction parameters of any power distribution equipment with the multidimensional parameter change trend values ​​one by one to obtain the multidimensional parameter trend deviation; it should be noted that the larger the parameter trend deviation of any dimensional parameter, the higher the abnormality of the parameter. Based on the parameter trend deviation of each dimensional parameter, the fault risk of any power distribution equipment can be predicted.

[0042] S403. Obtaining a comprehensive failure risk index of any power distribution equipment at a specified time in the future according to the multi-dimensional parameter trend deviation; Specifically, in step S403 of this embodiment, the comprehensive failure risk index of any power distribution equipment at a specified time in the future is: ; In the formula, n is a parameter dimension of any power distribution device, that is, a parameter dimension in a multidimensional historical parameter set, a multidimensional real-time parameter set, and a multidimensional predicted parameter set, representing a parameter dimension in a multidimensional historical parameter set, a multidimensional real-time parameter set, and a multidimensional predicted parameter set. nTypes of parameters, in this embodiment, n The value of is 3; For any of the above-mentioned power distribution equipment i The preset parameter weight of the dimension parameter reflects the influence of the corresponding type parameter on the failure of any distribution equipment. For any of the above-mentioned power distribution equipment i The parameter trend deviation of the dimension parameter.

[0043] S404. Obtain the predicted value of the failure risk of any power distribution device at a future specified time according to the comprehensive failure risk index. It should be noted that in this embodiment, the quantitative evaluation of the failure risk of any power distribution device at a future specified time can be achieved according to the size of the comprehensive failure risk index.

[0044] S5. Obtain the operation and maintenance priority of each power distribution device according to the health score prediction value and fault risk prediction value of each power distribution device; In step S5, the operation and maintenance priority of any power distribution equipment is: U = β · [1 - H (t')] + γ · P (t'); In the formula, H(t') is the predicted health score value of any power distribution equipment at a specified time t' in the future, β is the preset health score adjustment coefficient, P(t') is the predicted fault risk value of any power distribution equipment at a specified time t' in the future, and γ is the preset fault risk adjustment coefficient. It should be noted that β and γ are used to balance the importance of the predicted health score value and the predicted fault risk value. The larger the value of the operation and maintenance priority U of any power distribution equipment, the more priority maintenance of any power distribution equipment is needed.

[0045] S6. According to the operation and maintenance priority of each distribution device, construct an operation and maintenance priority sorting list of the designated distribution network; wherein the operation and maintenance priority sorting list includes the identification information of each distribution device sorted from large to small according to the operation and maintenance priority; it should be noted that, in this embodiment, by sorting each distribution device according to the operation and maintenance priority, the distribution equipment that needs to be prioritized for operation and maintenance in the designated distribution network can be determined.

[0046] S7. Optimize the operation and maintenance management plan of the designated distribution network according to the operation and maintenance priority list to obtain an optimized operation and maintenance management plan, and perform operation and maintenance management on the designated distribution network according to the optimized operation and maintenance management plan.

[0047] Specifically, in this embodiment, the operation and maintenance management plan of the designated distribution network includes a maintenance plan for the distribution equipment in the designated distribution network. During implementation, the maintenance sequence of each distribution equipment in the operation and maintenance management plan can be adjusted according to the ranking of each distribution equipment in the operation and maintenance priority ranking list, so as to achieve optimization of the operation and maintenance management plan.

[0048] In addition, this embodiment may further consider the geographical distribution of each distribution equipment, available resources (such as maintenance personnel, equipment accessories) and the overall operation requirements of the distribution network to further optimize the operation and maintenance management plan, which is not limited here.

[0049] This embodiment can dynamically adjust the operation and maintenance management plan according to the actual state of the distribution equipment, so as to achieve the purpose of improving the operation and maintenance efficiency of the distribution network, reducing maintenance costs and enhancing the reliability of the distribution network. Specifically, during the implementation of this embodiment, the future health status and failure risk of the distribution equipment can be dynamically evaluated based on multi-dimensional parameters, and then the operation and maintenance priority of each distribution equipment in the distribution network can be obtained by combining the health score prediction value and the failure risk prediction value of the distribution equipment, and the operation and maintenance management plan of the distribution network can be dynamically adjusted according to the operation and maintenance priority of each distribution equipment. Based on this, this embodiment can ensure that the operation and maintenance management plan can flexibly respond to changes in the state of the distribution equipment, thereby optimizing the allocation of operation and maintenance resources, improving the pertinence and timeliness of the maintenance of the distribution equipment, avoiding the problems of excessive maintenance and insufficient maintenance of the distribution equipment in the prior art, improving the utilization efficiency of the operation and maintenance resources, and at the same time helping to reduce the failure rate of the distribution equipment, and the safety and reliability of the distribution network operation are higher, thereby ensuring the long-term stable operation of the distribution network.

[0050] Embodiment 2: This embodiment discloses a distribution network operation and maintenance management system, which is used to implement the distribution network operation and maintenance management method in Embodiment 1; Figure 2 As shown, the distribution network operation and maintenance management system includes: The parameter acquisition module is used to collect in real time the multi-dimensional real-time parameter set of each distribution device in the specified distribution network in the current operation cycle, and pre-process the multi-dimensional real-time parameter set of each distribution device to obtain the multi-dimensional pre-processed parameter set of each distribution device; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of each power distribution device at a specified time in the future based on the multidimensional preprocessed parameter set of each power distribution device; A health assessment module, which is in communication with the parameter prediction module and is used to perform health assessment on each power distribution device according to the multi-dimensional prediction parameters of each power distribution device, and obtain a predicted health score value of each power distribution device at a specified time in the future; A fault risk prediction module is in communication with the parameter prediction module, and is used to obtain a multidimensional historical parameter set of each power distribution device, and perform fault risk prediction on each power distribution device according to the multidimensional historical parameter set, the multidimensional real-time parameter set and the multidimensional prediction parameters of each power distribution device, so as to obtain a fault risk prediction value of each power distribution device at a specified time in the future; An operation and maintenance priority calculation module, which is respectively connected to the health assessment module and the fault risk prediction module in communication, and is used to obtain the operation and maintenance priority of each power distribution device according to the health score prediction value and the fault risk prediction value of each power distribution device; An operation and maintenance management optimization module is communicatively connected to the operation and maintenance priority calculation module, and is used to construct an operation and maintenance priority sorting list of the designated distribution network according to the operation and maintenance priority of each distribution device; wherein the operation and maintenance priority sorting list includes identification information of each distribution device sorted from large to small according to the operation and maintenance priority; and is also used to optimize the operation and maintenance management plan of the designated distribution network according to the operation and maintenance priority sorting list to obtain an optimized operation and maintenance management plan, and perform operation and maintenance management on the designated distribution network according to the optimized operation and maintenance management plan.

[0051] It should be noted that the working process, working details and technical effects of the distribution network operation and maintenance management system provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0052] Embodiment 3: Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. Figure 3 As shown, the electronic equipment includes: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of the distribution network operation and maintenance management method as described in any one of Example 1.

[0053] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0054] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the distribution network operation and maintenance management method provided in Example 1 of the present application.

[0055] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0056] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0057] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0058] The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof.

[0059] The power supply 306 is used to supply power to various components in the electronic device.

[0060] Embodiment 4: Based on any one of Embodiments 1 to 3, this embodiment discloses a computer program product, including a computer program or an instruction, wherein when the computer program or the instruction is executed by a computer, the distribution network operation and maintenance management method as described in any one of Embodiment 1 is implemented. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0061] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network operation and maintenance management method, characterized in that: include: Real-time collection of multi-dimensional real-time parameter sets of each distribution device in the specified distribution network in the current operation cycle, and pre-processing of the multi-dimensional real-time parameter sets of each distribution device to obtain multi-dimensional pre-processed parameter sets of each distribution device; According to the multi-dimensional pre-processed parameter set of each power distribution device, a multi-dimensional prediction parameter of each power distribution device at a specified time in the future is obtained; According to the multi-dimensional prediction parameters of each power distribution equipment, each power distribution equipment is evaluated for health, and the predicted health score value of each power distribution equipment at a specified time in the future is obtained; Obtain a multidimensional historical parameter set of each power distribution device, and perform fault risk prediction on each power distribution device based on the multidimensional historical parameter set, multidimensional real-time parameter set and multidimensional prediction parameters of each power distribution device, to obtain a fault risk prediction value of each power distribution device at a specified time in the future; According to the health score prediction value and fault risk prediction value of each power distribution equipment, the operation and maintenance priority of each power distribution equipment is obtained; According to the operation and maintenance priority of each power distribution device, an operation and maintenance priority sorting list of the specified power distribution network is constructed; wherein the operation and maintenance priority sorting list includes identification information of each power distribution device sorted from large to small according to the operation and maintenance priority; The operation and maintenance management plan of the designated distribution network is optimized according to the operation and maintenance priority sorting list to obtain an optimized operation and maintenance management plan, and the operation and maintenance management of the designated distribution network is performed according to the optimized operation and maintenance management plan.

2. A distribution network operation and maintenance management method according to claim 1, characterized in that: According to the multi-dimensional prediction parameters of any power distribution equipment, a health assessment is performed on the power distribution equipment to obtain a predicted health score value of the power distribution equipment at a specified time in the future, including: Obtaining a multidimensional parameter threshold of any of the power distribution equipment, and adjusting the multidimensional parameter threshold of any of the power distribution equipment according to the multidimensional predicted parameters of any of the power distribution equipment, to obtain the multidimensional adjusted parameter threshold of any of the power distribution equipment at a specified future time; According to the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters of any power distribution device, a health score prediction value of any power distribution device at a specified future moment is obtained.

3. A distribution network operation and maintenance management method according to claim 2, characterized in that: Any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values ​​of any power distribution equipment at a future specified time t' is: X T (t’)=X base + α [x(t’)-X base )]; Where, X base is the basic parameter threshold corresponding to any of the adjusted parameter thresholds, α is a preset adjustment coefficient, and x(t') is a prediction parameter corresponding to any of the adjusted parameter thresholds.

4. A distribution network operation and maintenance management method according to claim 1, characterized in that: The multidimensional prediction parameters of any power distribution equipment at a future specified time t' include a temperature prediction parameter T(t'), a current prediction parameter I(t') and a load prediction parameter L(t'); the multidimensional adjusted parameter thresholds of any power distribution equipment at a future specified time t' include an adjusted temperature threshold T T (t'), adjusted current threshold I T (t') and the adjusted load threshold L T (t'); based on the preset SVR model, the predicted health score value of any power distribution equipment at the specified time t' in the future is obtained; correspondingly, the predicted health score value of any power distribution equipment at the specified time t' in the future is: H(t’)= f SVR [T T (t’),I T (t’),L T (t’),T(t’),I(t’),L(t’)]; In the formula, f SVR [·] represents the SVR model.

5. A distribution network operation and maintenance management method according to claim 1, characterized in that: According to a multi-dimensional historical parameter set, a multi-dimensional real-time parameter set and a multi-dimensional prediction parameter of any power distribution device, a fault risk prediction is performed on any power distribution device to obtain a fault risk prediction value of any power distribution device at a specified time in the future, including: Performing time series analysis on a multidimensional historical parameter set and a multidimensional real-time parameter set of any power distribution equipment to extract a multidimensional parameter change trend value; Comparing the multi-dimensional prediction parameters of any power distribution equipment with the multi-dimensional parameter change trend values ​​one by one to obtain the multi-dimensional parameter trend deviation; According to the multi-dimensional parameter trend deviation, a comprehensive failure risk index of any power distribution equipment at a specified time in the future is obtained; According to the comprehensive fault risk index, a predicted fault risk value of any power distribution equipment at a specified future moment is obtained.

6. A distribution network operation and maintenance management method according to claim 5, characterized in that: The comprehensive failure risk index of any power distribution equipment at a specified time in the future is: ； In the formula, n is the parameter dimension of any power distribution device, For any of the above-mentioned power distribution equipment i The preset parameter weights of the dimension parameters, For any of the above-mentioned power distribution equipment i The parameter trend deviation of the dimension parameter.

7. A distribution network operation and maintenance management method according to claim 1, characterized in that: The operation and maintenance priority of any power distribution equipment is: U = β · [1 - H (t')] + γ · P (t'); Wherein, H(t') is the predicted value of the health score of any power distribution equipment at the specified time t' in the future, β is the preset health score adjustment coefficient, P(t') is the predicted value of the fault risk of any power distribution equipment at the specified time t' in the future, and γ is the preset fault risk adjustment coefficient.

8. A distribution network operation and maintenance management system, characterized in that: Used to implement the distribution network operation and maintenance management method as described in any one of claims 1 to 7; The distribution network operation and maintenance management system comprises: The parameter acquisition module is used to collect in real time the multi-dimensional real-time parameter set of each distribution device in the specified distribution network in the current operation cycle, and pre-process the multi-dimensional real-time parameter set of each distribution device to obtain the multi-dimensional pre-processed parameter set of each distribution device; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of each power distribution device at a specified time in the future based on the multidimensional preprocessed parameter set of each power distribution device; A health assessment module, which is in communication with the parameter prediction module and is used to perform health assessment on each power distribution device according to the multi-dimensional prediction parameters of each power distribution device, and obtain a predicted health score value of each power distribution device at a specified time in the future; A fault risk prediction module is in communication with the parameter prediction module, and is used to obtain a multidimensional historical parameter set of each power distribution device, and perform fault risk prediction on each power distribution device according to the multidimensional historical parameter set, the multidimensional real-time parameter set and the multidimensional prediction parameters of each power distribution device, so as to obtain a fault risk prediction value of each power distribution device at a specified time in the future; An operation and maintenance priority calculation module, which is respectively connected to the health assessment module and the fault risk prediction module in communication, and is used to obtain the operation and maintenance priority of each power distribution device according to the health score prediction value and the fault risk prediction value of each power distribution device; An operation and maintenance management optimization module is communicatively connected to the operation and maintenance priority calculation module, and is used to construct an operation and maintenance priority sorting list of the designated distribution network according to the operation and maintenance priority of each distribution device; wherein the operation and maintenance priority sorting list includes identification information of each distribution device sorted from large to small according to the operation and maintenance priority; and is also used to optimize the operation and maintenance management plan of the designated distribution network according to the operation and maintenance priority sorting list to obtain an optimized operation and maintenance management plan, and perform operation and maintenance management on the designated distribution network according to the optimized operation and maintenance management plan.

9. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of the distribution network operation and maintenance management method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the distribution network operation and maintenance management method as described in any one of claims 1 to 7 is implemented.

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