Power distribution equipment health assessment method and system, electronic equipment and product
By collecting and preprocessing multi-dimensional parameters of power distribution equipment in real time, dynamically adjusting the threshold and using Kalman filtering and SVR models to predict health scores, the problem of insufficient dynamic assessment ability and multi-factor correlation in the existing technology is solved, and health is improved Accuracy and intelligence of evaluation.
Patent Information
- Application Number
- CN202411592215.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-08
- Publication Date
- 2025-02-11
AI Technical Summary
The prior art has shortcomings in dynamic evaluation capabilities and multi-factor correlation, which leads to low accuracy of the results of distribution equipment health assessment, making it difficult to adapt to the dynamic changes in the equipment operating environment and the complex coupling relationship between multiple operating parameters.
By collecting the multi-dimensional real-time parameter set of distribution equipment in real time, pre-processing and prediction, dynamically adjusting the parameter threshold, and using Kalman filtering algorithm and SVR model to predict health scores.
It improves the accuracy of distribution equipment health assessment, can dynamically respond to changes in the equipment operating environment, comprehensively consider the correlation between multiple operating parameters, and provides more intelligent and timely maintenance decision support.
Smart Images
Figure CN119415889A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of power distribution network management, and specifically relates to a health assessment method, system, electronic equipment and product for power distribution equipment. Background Art
[0002] The health assessment of distribution equipment is one of the key links in the operation and maintenance management of distribution networks. Operation and maintenance management of distribution networks based on the health assessment results of distribution equipment can effectively avoid equipment failures and power outages and ensure the stable operation of distribution networks. At present, in the process of health assessment of distribution equipment, most of them rely on the historical operation data of distribution equipment to obtain the parameter thresholds of key parameters, and then monitor and predict the key parameters of distribution equipment. The health status assessment of distribution equipment is realized based on the technology of fixed thresholds. For example, when the predicted operating parameters of distribution equipment such as temperature, current and load exceed the preset parameter thresholds, it is determined that the distribution equipment has potential health risks.
[0003] However, in the process of using the prior art, the inventors found that the prior art has at least the following problems: The existing technology has obvious deficiencies in dynamic assessment capabilities and multi-factor correlation, which limits the accuracy of health assessment results. Specifically, the existing fixed threshold method does not take into account the dynamic changes in the operating conditions of the equipment and is difficult to adapt to the complexity of actual working conditions. For example, the operating performance of the equipment under different environmental conditions and load conditions varies. A single fixed threshold cannot fully reflect the health status of the equipment. The accuracy of health assessment based on fixed thresholds is low, which may lead to excessive maintenance or missed faults. Furthermore, the existing technology mostly evaluates the health status of the equipment based on a single parameter, ignoring the interaction between multiple operating parameters. For example, there are complex coupling relationships between operating parameters such as temperature, current and load. Simply relying on the change of a certain parameter cannot accurately reflect the true health status of the distribution equipment. This evaluation method that ignores the correlation of multiple factors is difficult to capture the nonlinear relationship between the parameters in the operation of the equipment, which in turn leads to the health assessment results being not comprehensive and affecting the accuracy of the health assessment results. Summary of the invention
[0004] The present invention aims to at least to some extent solve the technical problem that the prior art has obvious deficiencies in dynamic assessment capabilities and multi-factor correlation, which limits the accuracy of health assessment results. The present invention provides a health assessment method, system, electronic equipment and product for distribution equipment.
[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for health assessment of power distribution equipment, comprising: Real-time collection of multi-dimensional real-time parameter sets of the specified power distribution equipment in the current operation cycle, and pre-processing of the multi-dimensional real-time parameter sets to obtain multi-dimensional pre-processed parameter sets; Obtaining multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future according to the multidimensional preprocessed parameter set; Acquire the multidimensional parameter threshold of the designated power distribution equipment, and adjust the multidimensional parameter threshold of the designated power distribution equipment according to the multidimensional prediction parameter to obtain the multidimensional adjusted parameter threshold of the designated power distribution equipment at a designated time in the future; According to the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters, a health score prediction value of the designated power distribution equipment at a designated time in the future is obtained.
[0006] In a possible design, preprocessing any real-time parameter set in the multi-dimensional real-time parameter set to obtain a preprocessed parameter set of the any real-time parameter set includes: 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 the outlier in any real-time parameter set is: x outlier ={x||x-μ|>3σ}; Wherein, x is the data in any real-time parameter set, μ is the mean of any real-time parameter set, and σ is the standard deviation of any real-time parameter set; The outlier-removed parameter set is normalized to obtain a normalized parameter set; wherein, in the normalized parameter set, the normalized parameter corresponding to any parameter x' of 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; Perform data smoothing processing on the normalized parameter set to obtain a preprocessed parameter set of any real-time parameter set.
[0007] In a possible design, when obtaining the multi-dimensional prediction parameters of the designated power distribution equipment at a designated time in the future based on the multi-dimensional pre-processed parameter set, a Kalman filter algorithm is used.
[0008] In a possible design, any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values of the designated power distribution equipment at a future designated time t' is: XT (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.
[0009] In one possible design, the multi-dimensional prediction parameters of the designated power distribution equipment at the future designated 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 the designated power distribution equipment at the future designated 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 the specified power distribution equipment at the future specified time t' is obtained; correspondingly, the predicted health score value of the specified power distribution equipment 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.
[0010] In one possible design, the method further includes: Obtaining a historical health score of the designated power distribution equipment, and obtaining a remaining service life of the designated power distribution equipment based on the health score prediction value and the historical health score; Determine whether the remaining service life is less than a preset life threshold. If so, issue a fault warning to the designated power distribution equipment; if not, take no action.
[0011] In a second aspect, the present invention provides a power distribution equipment health assessment system for implementing any of the power distribution equipment health assessment methods described above; the power distribution equipment health assessment system comprises: The parameter acquisition module is used to acquire the multi-dimensional real-time parameter set of the specified power distribution equipment in the current operation cycle in real time, and pre-process the multi-dimensional real-time parameter set to obtain a multi-dimensional pre-processed parameter set; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future based on a multidimensional preprocessed parameter set; A threshold adjustment module, which is in communication with the parameter prediction module, and is used to obtain the multidimensional parameter thresholds of the designated power distribution equipment, and adjust the multidimensional parameter thresholds of the designated power distribution equipment according to the multidimensional prediction parameters, to obtain the multidimensional adjusted parameter thresholds of the designated power distribution equipment at a designated time in the future; The health assessment module is in communication with the threshold adjustment module and is used to obtain the health score prediction value of the designated power distribution equipment at a designated time in the future based on the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters.
[0012] 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 power distribution equipment health assessment method as described in any one of the above.
[0013] 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 a power distribution equipment health assessment method as described in any one of the above.
[0014] In a fifth aspect, the present invention provides a computer-readable storage product having instructions stored thereon. When the instructions are run on a computer, the power distribution equipment health assessment method as described in any one of the above is executed.
[0015] The beneficial effects of the present invention are: The present invention discloses a method, system, electronic device and product for health assessment of power distribution equipment, which can dynamically adjust the parameter threshold of the power distribution equipment, and take into account the correlation between multi-dimensional operating parameters, so that the accuracy of the health assessment result can be improved. In the implementation process of the present invention, first, a multi-dimensional real-time parameter set of a specified power distribution equipment in the current operation cycle is collected in real time, and the multi-dimensional real-time parameter set is preprocessed to obtain a multi-dimensional preprocessed parameter set; then, according to the multi-dimensional preprocessed parameter set, the multi-dimensional prediction parameters of the specified power distribution equipment at a specified time in the future are obtained; then, the multi-dimensional parameter threshold of the specified power distribution equipment is obtained, and according to the multi-dimensional prediction parameters, the multi-dimensional parameter threshold of the specified power distribution equipment is adjusted to obtain the multi-dimensional adjusted parameter threshold of the specified power distribution equipment at a specified time in the future; finally, according to the multi-dimensional adjusted parameter threshold and the multi-dimensional prediction parameters, the health score prediction value of the specified power distribution equipment at a specified time in the future is obtained. Based on this, the present invention can realize dynamic health status assessment of power distribution equipment, solving the problem in the prior art that fixed thresholds cannot cope with dynamic changes in the operating environment of power distribution equipment. At the same time, in the process of obtaining the predicted health score value of the power distribution equipment at a specified time in the future, the present invention can more accurately predict the health of the power distribution equipment through comprehensive analysis of multi-dimensional prediction parameters and corresponding adjustment parameter thresholds, which is conducive to providing more intelligent and timely decision-making support for the maintenance and management of power distribution equipment, thereby effectively extending the life of the equipment and reducing the occurrence of failures.
[0016] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 is a flow chart of a method for health assessment of power distribution equipment in an embodiment; Figure 2 is a module block diagram of a power distribution equipment health assessment system in an embodiment; Figure 3 It is a module block diagram of an electronic device in an embodiment. DETAILED DESCRIPTION
[0018] 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.
[0019] Embodiment 1: This embodiment discloses a method for health assessment of power distribution equipment, which can be executed by, but is not limited to, 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.
[0020] like Figure 1 As shown, a method for health assessment of power distribution equipment may include but is not limited to the following steps: S1. Real-time acquisition of a multi-dimensional real-time parameter set of the designated power distribution equipment in the current operation cycle, and pre-processing the multi-dimensional real-time parameter set to obtain a multi-dimensional pre-processed parameter set. Specifically, in this embodiment, when the designated power distribution equipment is a transformer device, the corresponding multi-dimensional pre-processed parameter set may include, but is not limited to, operating data that affects the health of the transformer equipment, such as a temperature parameter set, a current parameter set, a load parameter set, an ambient temperature parameter set, and a voltage parameter set; real-time acquisition of a multi-dimensional real-time parameter set of the designated power distribution equipment in the current operation cycle, and pre-processing the multi-dimensional real-time parameter set, can ensure the data quality of the input model.
[0021] Specifically, in this embodiment, preprocessing any real-time parameter set in the multidimensional real-time parameter set to obtain a preprocessed parameter set of the any real-time parameter set includes: 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 real-time parameter set, and σ is the standard deviation of any real-time 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.
[0022] 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.
[0023] S2. Obtain multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future based on the multidimensional preprocessed parameter set.
[0024] In step S2, when obtaining the multi-dimensional prediction parameters of the designated power distribution equipment at the future designated time t' according to the multi-dimensional pre-processed parameter set, the Kalman filter algorithm is used.
[0025] Specifically, in this embodiment, based on the multidimensional preprocessed parameter set, the multidimensional prediction parameters of the designated power distribution equipment at the future designated time t' are obtained, including: S201. According to the multidimensional preprocessed parameter set, predict the multidimensional initial prediction parameters of the designated power distribution equipment at the future designated time t'; wherein any initial prediction parameter of the multidimensional initial prediction parameters of the designated power distribution equipment at the future designated 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 the designated power distribution equipment at the current moment, and update the multidimensional initial prediction parameters according to the multidimensional current parameters to obtain the multidimensional prediction parameters of the designated power distribution equipment at the future designated moment t'. Among them, any prediction parameter of the multidimensional prediction parameters of the designated power distribution equipment at the future designated 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 the designated power distribution equipment at the future designated time t' include a temperature prediction parameter T(t'), a current prediction parameter I(t') and a load prediction parameter L(t').
[0027] S3. Obtain the multidimensional parameter thresholds of the designated power distribution equipment, and adjust the multidimensional parameter thresholds of the designated power distribution equipment according to the multidimensional prediction parameters to obtain the multidimensional adjusted parameter thresholds of the designated power distribution equipment at a designated time in the future. Specifically, in this embodiment, when the designated 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 management system can dynamically monitor the operating status of the designated power distribution equipment based on the multidimensional parameter thresholds and perform operation and maintenance control on it.
[0028] In step S3 of this embodiment, any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values of the designated power distribution equipment at the future designated 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.
[0029] Specifically, in step S3 of this embodiment, the multi-dimensional adjusted parameter threshold of the designated power distribution equipment at the future designated time t' includes an adjusted temperature threshold, an adjusted current threshold and an adjusted load threshold. As an example, the adjusted temperature threshold of the designated power distribution equipment at the future designated 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.
[0030] It should be noted that, in this embodiment, based on the above step S3, the multi-dimensional parameter threshold of the designated power distribution equipment 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 the designated power distribution equipment can also be adjusted based on the multi-dimensional adjusted parameter threshold, so that the operating parameters of the designated power distribution equipment remain within a safe range.
[0031] It should be understood that this embodiment can also perform operating status evaluation on the multi-dimensional current parameters of the designated power distribution equipment at the current moment based on the multi-dimensional parameter thresholds of the designated power distribution equipment, and the multi-dimensional parameter thresholds are adjusted at historical moments. Based on this, this embodiment can more accurately reflect the health status of the equipment and avoid excessive or insufficient maintenance.
[0032] S4. Obtain the health score prediction value of the designated power distribution equipment at a designated time in the future based on the multi-dimensional adjusted parameter threshold and the multi-dimensional prediction parameter. It should be noted that the health score prediction value is used to quantify the health status of the designated power distribution equipment. The larger the value, the better the health status of the equipment. The smaller the value, the worse the health status of the equipment and the presence of potential problems. In this embodiment, the maintenance requirements of the designated power distribution equipment can be determined based on the health score prediction value.
[0033] In step S2 of this embodiment, the multi-dimensional prediction parameters of the designated power distribution equipment at the future designated time t' include temperature prediction parameters T(t'), current prediction parameters I(t') and load prediction parameters L(t'); in step S3, the multi-dimensional adjusted parameter thresholds of the designated power distribution equipment at the future designated 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 S4 of this embodiment, the health score prediction value of the specified power distribution equipment 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 the specified power distribution equipment 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.
[0034] It should be noted that, in this embodiment, when predicting the health score of the designated 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 the designated 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 the designated power distribution equipment in a dynamic environment.
[0035] In addition, in this embodiment, the SVR model can be trained based on the multi-dimensional historical parameter thresholds of the specified power distribution equipment, the relationship between the multi-dimensional historical operating parameters and the historical health scores. 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.
[0036] After step S4, the method further comprises: S5. Obtain the historical health score of the designated power distribution equipment, and obtain the remaining service life of the designated power distribution equipment based on the health score prediction value and the historical health score; it should be noted that, in this embodiment, the remaining service life of the designated power distribution equipment is predicted by utilizing the historical change trend information of the health score obtained by the health score prediction value and the historical health score.
[0037] S6. Determine whether the remaining service life is less than the preset service life threshold. If so, issue a fault warning to the designated power distribution equipment. If not, do nothing. It should be noted that the historical health score is a health score calculated based on the most recent historical operation cycle of this embodiment. Of course, the historical health score may also be a health score manually marked by the operator based on the operating status of the designated power distribution equipment, which is not limited here.
[0038] This embodiment can dynamically adjust the parameter thresholds of the power distribution equipment, and considers the correlation between the multi-dimensional operating parameters, so that the accuracy of the health assessment results can be improved. In the implementation process of this embodiment, first, the multi-dimensional real-time parameter set of the specified power distribution equipment in the current operation cycle is collected in real time, and the multi-dimensional real-time parameter set is preprocessed to obtain a multi-dimensional preprocessed parameter set; then, based on the multi-dimensional preprocessed parameter set, the multi-dimensional prediction parameters of the specified power distribution equipment at a specified time in the future are obtained; then, the multi-dimensional parameter thresholds of the specified power distribution equipment are obtained, and based on the multi-dimensional prediction parameters, the multi-dimensional parameter thresholds of the specified power distribution equipment are adjusted to obtain the multi-dimensional adjusted parameter thresholds of the specified power distribution equipment at a specified time in the future; finally, based on the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters, the health score prediction value of the specified power distribution equipment at a specified time in the future is obtained. Based on this, this embodiment can realize the dynamic health status assessment of the distribution equipment, solving 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 multi-dimensional prediction parameters and corresponding adjustment parameter thresholds, which is conducive to providing more intelligent and timely decision-making support for the maintenance and management of the distribution equipment, thereby effectively extending the equipment life and reducing the occurrence of failures.
[0039] Embodiment 2: This embodiment discloses a power distribution equipment health assessment system for implementing the power distribution equipment health assessment method in Embodiment 1; Figure 2 As shown, the power distribution equipment health assessment system includes: The parameter acquisition module is used to acquire the multi-dimensional real-time parameter set of the specified power distribution equipment in the current operation cycle in real time, and pre-process the multi-dimensional real-time parameter set to obtain a multi-dimensional pre-processed parameter set; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future based on a multidimensional preprocessed parameter set; A threshold adjustment module, which is in communication with the parameter prediction module, and is used to obtain the multidimensional parameter thresholds of the designated power distribution equipment, and adjust the multidimensional parameter thresholds of the designated power distribution equipment according to the multidimensional prediction parameters, to obtain the multidimensional adjusted parameter thresholds of the designated power distribution equipment at a designated time in the future; The health assessment module is in communication with the threshold adjustment module and is used to obtain the health score prediction value of the designated power distribution equipment at a designated time in the future based on the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters.
[0040] It should be noted that the working process, working details and technical effects of the power distribution equipment health assessment system provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.
[0041] 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 power distribution equipment health assessment method as described in any one of Example 1.
[0042] 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.
[0043] 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 power distribution equipment health assessment method provided in Example 1 of the present application.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof.
[0048] The power supply 306 is used to supply power to various components in the electronic device.
[0049] Embodiment 4: Based on any one of Embodiments 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements the health assessment method for power distribution equipment as described in any one of Embodiments 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
[0050] Embodiment 5: Based on any one of embodiments 1 to 3, this embodiment discloses a computer-readable storage product, on which instructions are stored, and when the instructions are run on a computer, the power distribution equipment health assessment method described in any one of embodiment 1 is executed. The computer-readable storage product refers to a carrier for storing data, which may include but is not limited to computer-readable storage media such as floppy disks, optical disks, hard disks, flash memories, USB flash drives, and / or memory sticks, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
[0051] 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.
[0052] 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 method for health assessment of power distribution equipment, characterized in that: include: Real-time collection of multi-dimensional real-time parameter sets of the specified power distribution equipment in the current operation cycle, and pre-processing of the multi-dimensional real-time parameter sets to obtain multi-dimensional pre-processed parameter sets; Obtaining multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future according to the multidimensional preprocessed parameter set; Acquire the multidimensional parameter threshold of the designated power distribution equipment, and adjust the multidimensional parameter threshold of the designated power distribution equipment according to the multidimensional prediction parameter to obtain the multidimensional adjusted parameter threshold of the designated power distribution equipment at a designated time in the future; According to the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters, a health score prediction value of the designated power distribution equipment at a designated time in the future is obtained.
2. A method for health assessment of power distribution equipment according to claim 1, characterized in that: Preprocessing any real-time parameter set in the multidimensional real-time parameter set to obtain a preprocessed parameter set of the any real-time parameter set includes: 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 the outlier in any real-time parameter set is: x outlier ={x||x-μ|>3σ}; Wherein, x is the data in any real-time parameter set, μ is the mean of any real-time parameter set, and σ is the standard deviation of any real-time parameter set; The outlier-removed parameter set is normalized to obtain a normalized parameter set; wherein, in the normalized parameter set, the normalized parameter corresponding to any parameter x' of 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; Perform data smoothing processing on the normalized parameter set to obtain a preprocessed parameter set of any real-time parameter set.
3. A method for health assessment of power distribution equipment according to claim 1, characterized in that: When obtaining the multi-dimensional prediction parameters of the designated power distribution equipment at a designated time in the future according to the multi-dimensional pre-processed parameter set, the Kalman filtering algorithm is adopted.
4. A method for health assessment of power distribution equipment according to claim 1, characterized in that: Any adjusted parameter threshold value among the multi-dimensional adjusted parameter threshold values of the designated power distribution equipment at a future designated 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.
5. A method for health assessment of power distribution equipment according to claim 1, characterized in that: The multi-dimensional prediction parameters of the designated power distribution equipment at the future designated time t' include temperature prediction parameters T(t'), current prediction parameters I(t') and load prediction parameters L(t'); the multi-dimensional adjusted parameter thresholds of the designated power distribution equipment at the future designated time t' include the 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 the specified power distribution equipment at the future specified time t' is obtained; correspondingly, the predicted health score value of the specified power distribution equipment 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.
6. A method for health assessment of power distribution equipment according to claim 1, characterized in that: The method further comprises: Obtaining a historical health score of the designated power distribution equipment, and obtaining a remaining service life of the designated power distribution equipment based on the health score prediction value and the historical health score; Determine whether the remaining service life is less than a preset life threshold. If so, issue a fault warning to the designated power distribution equipment; if not, take no action.
7. A power distribution equipment health assessment system, characterized in that: Used to implement the health assessment method for power distribution equipment as described in any one of claims 1 to 6; The power distribution equipment health assessment system comprises: The parameter acquisition module is used to collect the multi-dimensional real-time parameter set of the specified power distribution equipment in the current operation cycle in real time, and pre-process the multi-dimensional real-time parameter set to obtain a multi-dimensional pre-processed parameter set; A parameter prediction module, which is in communication with the parameter acquisition module and is used to obtain multidimensional prediction parameters of the designated power distribution equipment at a designated time in the future based on a multidimensional preprocessed parameter set; A threshold adjustment module, which is in communication with the parameter prediction module, and is used to obtain the multidimensional parameter thresholds of the designated power distribution equipment, and adjust the multidimensional parameter thresholds of the designated power distribution equipment according to the multidimensional prediction parameters, to obtain the multidimensional adjusted parameter thresholds of the designated power distribution equipment at a designated time in the future; The health assessment module is in communication with the threshold adjustment module and is used to obtain the health score prediction value of the designated power distribution equipment at a designated time in the future based on the multi-dimensional adjusted parameter thresholds and the multi-dimensional prediction parameters.
8. 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 power distribution equipment health assessment method as described in any one of claims 1 to 6.
9. 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 method for health assessment of power distribution equipment as described in any one of claims 1 to 6 is implemented.
10. A computer readable storage product, characterized in that: The computer-readable storage product stores instructions, and when the instructions are executed on a computer, the power distribution equipment health assessment method according to any one of claims 1 to 6 is executed.