High-voltage electric energy measurement optimization method and system

Abstract

The invention discloses a high-voltage electric energy measurement optimization method and system. The method comprises the following steps: acquiring voltage and current data after preliminary filtering; extracting characteristic parameters from the voltage and current data, inputting the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generating an isolation strategy in a high-voltage environment according to the fault diagnosis result; acquiring operation state data of the electric energy measuring device, judging an aging trend and a potential fault risk of the device by adopting a prediction model based on a neural network, and generating a maintenance strategy; and integrating the isolation strategy and the maintenance strategy to generate a comprehensive management optimization scheme in the high-voltage environment. According to the invention, by combining adaptive filtering, fuzzy logic control, neural network prediction and dynamic optimization technologies, the measurement precision is improved, the fault risk is reduced, and the problems of measurement precision, aging maintenance and fault isolation of the device in a high-voltage environment are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and particularly to a method and system for optimizing high-voltage electric energy measurement. Background Art

[0002] In the practice of power monitoring and fault isolation on the user side, a technical problem is indeed faced, which is mainly reflected in the application of high-precision measurement devices and the influence of the high-voltage environment on the stability and reliability of the devices. In practical applications, in order to effectively monitor the electric energy on the user side, high-precision electric energy measurement devices must be used to ensure the accurate acquisition and analysis of parameters such as power consumption, quality, and fluctuation. However, these high-precision electric energy measurement devices often need to operate in a high-voltage environment, which poses unprecedented challenges to the design and application of the devices. The particularity of the high-voltage environment itself has a great impact on the stability, measurement accuracy, service life, etc. of the measurement devices.

[0003] Firstly, electromagnetic interference and temperature fluctuations in the high-voltage environment will directly affect the performance of the sensors of the measurement devices, thereby leading to a decrease in measurement accuracy or even data loss. Electrical devices are often vulnerable to strong electromagnetic wave interference in a high-voltage electric field, which will make the acquisition of measurement signals unstable, especially in occasions with high-precision measurement requirements for current, voltage, etc. Factors such as voltage fluctuations and temperature fluctuations will cause the output signals of the sensors to deviate and cannot accurately reflect the actual power consumption situation. Such measurement errors not only affect the effectiveness of power monitoring but may also mislead fault judgment, thus affecting the response of the entire fault isolation system.

[0004] Secondly, with the long-term operation of the measurement devices in the high-voltage environment, the problem of aging of insulating materials gradually emerges. Under high voltage, the insulating materials are under long-term electric field pressure and are prone to aging, cracking, or breakdown, resulting in a decline in the performance of the devices and even device failures. Especially in a harsh working environment, the deterioration process of the insulating layer accelerates, the stability of the device becomes worse, and the failure rate increases, which will seriously affect the operation safety and reliability of the entire power system. Once the device fails, the frequent maintenance and replacement processes not only increase the operation cost but also reduce the availability of the power grid and even affect the normal operation of the system.

[0005] Furthermore, the response speed and reliability requirements of the fault isolation system conflict with the design of the measurement device in a high-voltage environment. The impact of the high-voltage environment on the device is not limited to performance degradation, but also causes a delay in the response speed of the device. When a fault occurs, quickly and accurately determining the fault point and promptly cutting off the power supply are the keys to ensuring the safe and stable operation of the power system on the user side. However, in a high-voltage environment, existing power measurement devices suffer from delays in information transmission due to electromagnetic interference or unstable sensors, and even lose key signals, thus delaying the identification and handling of faults and affecting the timeliness of isolation operations. Summary of the Invention

[0006] The present invention provides an optimization method and system for high-voltage power measurement, which solves the problems of aging maintenance and fault isolation of power measurement devices in a high-voltage environment.

[0007] According to one aspect of the present invention, there is provided an optimization method for high-voltage power measurement, including: acquiring real-time voltage and current data, using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on measurement accuracy, and obtaining preliminarily filtered voltage and current data; extracting characteristic parameters from the voltage and current data, inputting the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generating an isolation strategy in a high-voltage environment according to the fault diagnosis result, where the isolation strategy uses a control algorithm based on fuzzy logic to determine the priority and execution order of fault isolation; acquiring the operating state data of the power measurement device, using a prediction model based on a neural network to judge the aging trend and potential fault risks of the device, and generating a maintenance strategy for the device in a high-voltage environment according to the aging trend and the potential fault risks; integrating the isolation strategy and the maintenance strategy to generate a comprehensive management optimization plan in a high-voltage environment.

[0008] Furthermore, the acquiring real-time voltage and current data, using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on measurement accuracy, and obtaining preliminarily filtered voltage and current data is specifically: acquiring real-time voltage and current data in a high-voltage environment, using multi-sensor fusion technology to synchronously collect and preprocess the voltage and current data; according to the preprocessed voltage and current data, using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference on measurement accuracy, and using a temperature compensation algorithm to eliminate the influence of temperature fluctuations on measurement accuracy; fusing the voltage and current data after adaptive filtering and temperature compensation to obtain preliminarily filtered voltage and current data.

[0009] Furthermore, extract characteristic parameters from the voltage and current data, input the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generate an isolation strategy in a high-voltage environment according to the fault diagnosis result. The isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation. Specifically: according to the preliminarily filtered voltage and current data, extract the characteristic parameters in the high-voltage environment, and the characteristic parameters include voltage fluctuation frequency and current harmonic components; generate a characteristic data set according to the characteristic parameters, input the characteristic data set into a preset fault diagnosis model, and the fault diagnosis model uses a support vector machine algorithm to judge the fault type and location and output the fault diagnosis result; generate an isolation strategy in a high-voltage environment according to the fault diagnosis result, and the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; use a distributed control algorithm to execute the fault isolation operation of the isolation strategy.

[0010] Furthermore, obtain the operation status data of the power measurement device, use a neural network-based prediction model to judge the aging trend and potential fault risk of the device, and generate a maintenance strategy for the device in a high-voltage environment according to the aging trend and the potential fault risk. Specifically: obtain the operation status data of the power measurement device, and the operation status data includes temperature and insulation resistance value; input the operation status data into a pre-trained neural network model to judge the aging trend and the potential fault risk of the device, and obtain an aging trend prediction result and a fault risk level; generate the maintenance strategy for the high-voltage power measurement device according to the aging trend prediction result and the fault risk level; use a dynamic programming algorithm to optimize the maintenance frequency and resource allocation to obtain the optimized maintenance strategy.

[0011] Furthermore, integrate the isolation strategy and the maintenance strategy to generate an integrated management optimization plan in a high-voltage environment. Specifically: integrate the isolation strategy of the fault diagnosis result and the maintenance strategy of the high-voltage power measurement device to generate the integrated management optimization plan in a high-voltage environment; according to the integrated management optimization plan, use a fuzzy logic control algorithm to determine the specific steps and execution order of the maintenance operation to obtain an integrated management instruction; according to the integrated management instruction, use a distributed control algorithm to execute the integrated management operation.

[0012] Further, after the comprehensive management operation is executed, the operation status data of the high-voltage power measurement device after execution is monitored in real time; the operation status data monitored in real time is input into a neural network model for a second aging trend prediction to determine whether the aging trend accelerates or the potential failure risk escalates; if the aging trend accelerates or the potential failure risk escalates, the comprehensive management strategy is updated and corresponding operations are executed; according to the updated comprehensive management strategy, the dynamic programming algorithm is used to re-optimize the maintenance frequency and resource allocation to obtain the final comprehensive management optimization execution plan.

[0013] Further, the dynamic programming algorithm is optimized by using a comprehensive management optimization objective function, and the comprehensive management optimization objective function is:

[0014]

[0015] where F(t) represents the comprehensive management optimization objective function at time t, Mi(t) represents the state of the i-th maintenance item at time t, αi is the weight coefficient, R(t) represents the resource usage at time t, β is the resource weight, C(t) represents the cost at time t, and γ is the cost weight.

[0016] According to another aspect of the present invention, a high-voltage power measurement system is provided, including: a data acquisition module, which is used to acquire real-time voltage and current data, and uses an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on the measurement accuracy to obtain preliminarily filtered voltage and current data; an isolation strategy module, which is used to extract characteristic parameters from the voltage and current data, input the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generate an isolation strategy in a high-voltage environment according to the fault diagnosis result, and the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; a maintenance strategy module, which is used to acquire the operation status data of the power measurement device, use a neural network-based prediction model to judge the aging trend and potential failure risk of the device, and generate a maintenance strategy for the device in a high-voltage environment according to the aging trend and the potential failure risk; a management optimization module, which is used to integrate the isolation strategy and the maintenance strategy to generate a comprehensive management optimization plan in a high-voltage environment.

[0017] According to another aspect of the present invention, an electronic device is provided, including: at least one processor, and a memory communicatively connected to the at least one processor;

[0018] wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any high-voltage power measurement optimization method in the embodiments of the present invention.

[0019] According to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute any one of the high-voltage power measurement optimization methods in the embodiments of the present invention.

[0020] According to the technology of the present invention, by integrating the fault isolation strategy and the maintenance strategy into a comprehensive management optimization scheme, the intelligent management of the device can be realized in a high-voltage environment, the operation efficiency of the system can be comprehensively improved, the operation risk can be reduced, and the stability and safety of the device during long-term operation can be ensured. By combining adaptive filtering, fuzzy logic control, neural network prediction and dynamic optimization technology, not only the accuracy, response speed and operation safety of the high-voltage power measurement device are improved, but also the fault risk and maintenance cost are reduced through intelligent management, effectively solving the problems of measurement accuracy, aging maintenance and fault isolation of the power measurement device in a high-voltage environment.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings are used to better understand the solution and do not constitute a limitation to the present invention. Among them:

[0023] Figure 1 is a flowchart of the high-voltage power measurement optimization method provided by the embodiment of the present invention;

[0024] Figure 2 is a schematic structural diagram of the high-voltage power measurement system provided by the embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the electronic device and the storage medium provided by the embodiment of the present invention.

[0026] In the figure, 100 is the high-voltage power measurement system; 11 is the data acquisition module; 12 is the isolation strategy module; 13 is the maintenance strategy module; 14 is the management optimization module; 200 is the electronic device; 201 is the computing unit; 202 is the ROM; 203 is the RAM; 204 is the bus; 205 is the I / O interface; 206 is the input unit; 207 is the output unit; 208 is the storage unit; 209 is the communication unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following describes exemplary embodiments of the present invention in conjunction with the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present invention. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0028] As Figure 1 shown, an embodiment of the present invention discloses a method for optimizing high-voltage power measurement, including: S1, obtaining real-time voltage and current data, using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on the measurement accuracy, and obtaining the preliminarily filtered voltage and current data; S2, extracting characteristic parameters from the voltage and current data, inputting the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, generating an isolation strategy in a high-voltage environment according to the fault diagnosis result, and the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; S3, obtaining the operation status data of the power measurement device, using a neural network-based prediction model to judge the aging trend and potential fault risk of the device, and generating a maintenance strategy for the device in a high-voltage environment according to the aging trend and potential fault risk; S4, integrating the isolation strategy and the maintenance strategy to generate a comprehensive management optimization plan in a high-voltage environment.

[0029] The high-voltage power measurement optimization method of this application can significantly improve the measurement accuracy and stability of the device in a complex environment, ensure the accuracy of real-time data, and provide a reliable basis for subsequent processing by using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on voltage and current data. By extracting the characteristic parameters of voltage and current data and analyzing them in combination with a preset fault diagnosis model, potential faults of the device can be quickly identified. The fault isolation strategy generated based on the fuzzy logic control algorithm can give priority to dealing with serious faults, optimize the execution order of fault isolation, and improve the speed and accuracy of fault emergency response. The prediction model of the neural network can accurately predict the aging trend and potential fault risk of the power measurement device according to the operation status data of the device, providing a scientific basis for formulating a reasonable maintenance strategy, thus effectively avoiding the occurrence of device faults, extending the service life of the device, and reducing maintenance costs.

[0030] By integrating the fault isolation strategy with the maintenance strategy into a comprehensive management optimization solution, intelligent management of the device can be achieved in a high-voltage environment. Through this optimization solution, the operating efficiency of the system can be comprehensively improved, the operation risk can be reduced, and the stability and safety of the device during long-term operation can be ensured. Through intelligent fault diagnosis and maintenance prediction, the present invention significantly reduces the incidence rate of device failures and the maintenance frequency, reduces the impact of sudden failures on production, thereby reducing the system maintenance cost and improving the overall safety. By combining technologies such as adaptive filtering, fuzzy logic control, neural network prediction, and dynamic optimization, the present invention not only improves the accuracy, response speed, and operation safety of high-voltage power measurement devices, but also reduces the fault risk and maintenance cost through intelligent management, effectively solving the problems of measurement accuracy, aging maintenance, and fault isolation of power measurement devices in high-voltage environments.

[0031] In an alternative embodiment of the present invention, step S1 is specifically as follows: Obtain real-time voltage and current data in a high-voltage environment, and use multi-sensor fusion technology to synchronously collect and preprocess the voltage and current data; According to the preprocessed voltage and current data, use an adaptive filtering algorithm to eliminate the influence of electromagnetic interference on the measurement accuracy, and use a temperature compensation algorithm to eliminate the influence of temperature fluctuations on the measurement accuracy; Fuse the voltage and current data after adaptive filtering and temperature compensation to obtain preliminarily filtered voltage and current data.

[0032] Specifically, the acquisition of voltage and current data in a high-voltage environment is a complex project that requires the comprehensive application of various technologies to ensure the accuracy of measurement. By using multi-sensor fusion technology to synchronously collect data from different sensors, the reliability and comprehensiveness of the data can be improved. In a high-voltage substation, various devices such as voltage transformers, current transformers, and optical fiber sensors can be used simultaneously for data acquisition, which can verify and complement each other and reduce the errors caused by a single sensor. The data preprocessing stage mainly solves the problems of outliers and noise interference. Taking current measurement as an example, sudden spike data caused by device failures or external interference may occur, and these data deviate significantly from the normal range. By setting reasonable thresholds, these outliers can be identified and eliminated. At the same time, methods such as median filtering can be used to effectively remove random noise and retain the effective signal.

[0033] By adopting an adaptive filtering algorithm to cope with electromagnetic interference, in a high-voltage environment, strong electromagnetic fields will have a significant impact on the measurement device. For example, by using a least mean square (LMS) adaptive filter, the filtering parameters can be adjusted in real time according to environmental changes, effectively suppressing electromagnetic interference. This method is particularly suitable for scenarios where the characteristics of the interference source are constantly changing, such as the transient electromagnetic interference generated during the operation of high-voltage switches. A large amount of heat is generated during the operation of high-voltage devices, resulting in temperature changes, which in turn affect the measurement accuracy. By temperature compensation, the measurement accuracy can be improved. By integrating a temperature sensor in the measurement device, the ambient temperature can be monitored in real time. According to the temperature characteristic curve of the device, a relationship model between temperature and measurement error can be established, and then the original data can be corrected. The measurement error of a certain type of current transformer is 0.1% at 20°C, while the error increases to 0.3% at 60°C. Through the temperature compensation algorithm, the measurement results at high temperatures can be corrected to be closer to the actual value.

[0034] By integrating multi-source information through data fusion, the data that has undergone adaptive filtering and temperature compensation can be fused, which can make full use of the advantages of each sensor and improve the overall measurement accuracy. Common fusion methods include the weighted average method and the Kalman filtering method. For example, for voltage measurement, the data of capacitive voltage transformers and resistive voltage transformers can be weighted and fused, and weights are assigned according to their performance characteristics in different frequency ranges, so as to obtain a more accurate voltage value. As the final processing step, the Kalman filtering algorithm can effectively eliminate the remaining random noise. This algorithm continuously optimizes the estimation result through two stages: prediction and correction. In high-voltage measurement, the state equation can be established by using the physical relationship between voltage and current, such as the impedance model based on Ohm's law, so as to further optimize the measurement result. Through the above series of processing steps, the voltage and current measurement results in a high-voltage environment will finally have high accuracy and reliability. The comprehensive application of these technologies not only improves the measurement accuracy, but also enhances the system's adaptability to various interferences and environmental changes, providing an important guarantee for the safe and stable operation of the power system.

[0035] In an alternative embodiment of the present invention, step S2 is specifically: according to the preliminarily filtered voltage and current data, extract the characteristic parameters in the high-voltage environment, and the characteristic parameters include the voltage fluctuation frequency and the current harmonic component; generate a characteristic data set according to the characteristic parameters, input the characteristic data set into a preset fault diagnosis model, and the fault diagnosis model uses a support vector machine algorithm to judge the fault type and location and output the fault diagnosis result; generate an isolation strategy in the high-voltage environment according to the fault diagnosis result, and the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; use a distributed control algorithm to execute the fault isolation operation of the isolation strategy.

[0036] Specifically, the safe and stable operation of the power system is ensured through fault diagnosis and isolation under high-voltage environments. By performing fast Fourier transform processing on the preliminarily filtered voltage and current data, the fundamental wave and harmonic components in the system can be effectively extracted. In a certain 110 kV substation, after performing fast Fourier transform analysis on the phase A voltage, obvious 3rd, 5th, and 7th harmonics are found, among which the amplitude of the 5th harmonic reaches 3% of the fundamental wave, indicating the existence of non-linear loads or harmonic sources in the system. Classification is carried out in fault diagnosis through a support vector machine model. Through a pre-trained support vector machine model, the fault type and location can be quickly determined based on the extracted feature data. Suppose on a certain 330 kV transmission line, the support vector machine model diagnoses a single-phase grounding fault based on the voltage drop characteristics and current mutation characteristics, and locates the fault position 32 kilometers away from the substation. This precise positioning greatly shortens the fault finding time and improves the system recovery efficiency.

[0037] Complex and highly uncertain decision-making problems are processed through fuzzy logic control algorithms. In determining the fault isolation priority and execution order, fuzzy logic can comprehensively consider multiple factors, such as fault severity, influence range, system stability, etc. For example, for a ring network structure with multiple lines, when a three-phase short-circuit fault occurs on line B, the fuzzy controller will, based on the current load distribution and system stability assessment, decide to isolate line A first and then line B to minimize the impact on user power supply. The distributed control algorithm can achieve fast and coordinated responses when performing fault isolation operations. In a large substation, multiple intelligent terminal devices can simultaneously receive isolation instructions and independently execute corresponding operations according to preset strategies. For example, when an internal fault occurs in the main transformer, the relevant circuit breakers and disconnectors can cooperate within milliseconds to quickly isolate the faulty device from the system.

[0038] Through real-time data processing with the Kalman filter algorithm, it can effectively eliminate measurement noise and provide a more accurate state estimate. When monitoring the current of a 500 kV transmission line, the Kalman filter can smooth the data fluctuations caused by electromagnetic interference, reducing the standard deviation of the current measurement value from the original 0.5% to 0.1%, providing a more reliable data basis for fault diagnosis. Secondary fault diagnosis is an important link in ensuring the safety of high-voltage systems. Through continuous monitoring and analysis, hidden or derivative faults can be detected in a timely manner. For example, after isolating a primary transformer fault, the SVM model detected abnormal voltage fluctuations in the busbar during subsequent monitoring. Further diagnosis revealed that it was due to system imbalance caused by the isolation operation, and the reactive power compensation device was adjusted in a timely manner, avoiding a voltage collapse accident. The comprehensive application of this series of technologies not only improves the accuracy of high-voltage system fault diagnosis and the reliability of isolation operations, but also greatly enhances the system's ability to respond to complex faults. Through real-time data analysis, intelligent decision-making, and coordinated control, the power outage time is effectively reduced, and the overall reliability and resilience of the power grid are improved.

[0039] In an alternative embodiment of the present invention, step S3 is specifically as follows: Obtain the operating state data of the power measurement device, where the operating state data includes temperature and insulation resistance value; input the operating state data into a pre-trained neural network model to judge the aging trend and potential fault risk of the device, and obtain the aging trend prediction result and the fault risk level; generate a maintenance strategy for the high-voltage power measurement device according to the aging trend prediction result and the fault risk level; optimize the maintenance frequency and resource allocation using the dynamic programming algorithm to obtain an optimized maintenance strategy.

[0040] Specifically, by obtaining key operating state data such as temperature values and insulation resistance values, the health status of the device can be comprehensively grasped. The temperature sensor of a voltage transformer in a substation was affected by electromagnetic interference, and the original data fluctuated greatly. After applying the Kalman filter, the standard deviation of the temperature data decreased from ±2°C to ±0.5°C, laying a solid foundation for subsequent analysis. Inputting the filtered data into a pre-trained neural network model can accurately judge the aging trend and potential fault risk of the device. This predictive maintenance method is much better than the traditional regular maintenance mode. For example, through neural network analysis, it was found that the insulation performance of a current transformer in a 500 kV substation showed an accelerating deterioration trend, and it was predicted that an insulation breakdown fault would occur within 3 months. This early warning enabled the operation and maintenance personnel to take measures in advance, avoiding a large-scale power outage accident caused.

[0041] By adopting a dynamic programming algorithm to optimize the maintenance strategy based on the prediction results, both the reliability of the device and the resource utilization efficiency can be maximized. In a regional power grid covering multiple substations, the dynamic programming algorithm comprehensively considers factors such as the device status of each site, the distribution of maintenance personnel, and spare part inventory, and formulates an optimal maintenance strategy. This not only reduces the annual maintenance cost by 15%, but also improves the overall availability of the device. The fuzzy logic control algorithm plays an important role in determining the specific maintenance steps and can flexibly respond to complex on-site situations. When the partial discharge level of a certain voltage transformer is detected to be near the warning line, the fuzzy controller will dynamically adjust the urgency of the maintenance and the specific operation sequence according to multiple indicators such as the current load situation and weather conditions, ensuring power supply reliability and avoiding unnecessary power outages.

[0042] By applying a distributed control algorithm, the maintenance operations can be made more efficient and coordinated. During a large-scale substation device overhaul, multiple intelligent high-voltage power measurement devices can receive instructions synchronously and independently complete corresponding tasks according to their respective roles. This collaborative operation mode shortens the overhaul work that originally took 2 days to 8 hours, greatly reducing the power outage time. The real-time monitoring and secondary prediction mechanism provides dynamic optimization for device management. When a 220 kV substation is implementing a predetermined maintenance strategy, through real-time data analysis, it is found that the oil temperature of the main transformer has risen abnormally. The neural network model immediately conducts a secondary prediction to identify the potential risk of cooling system failure. The maintenance team adjusts the original plan accordingly, gives priority to dealing with the cooling system problem, and successfully prevents potential failures caused by transformer overheating. This closed-loop optimized maintenance strategy not only improves the reliability of the power measurement device but also significantly extends the device life.

[0043] In an alternative embodiment of the present invention, step S4 is specifically as follows: According to the comprehensive management optimization plan, use the fuzzy logic control algorithm to determine the specific steps and execution order of the maintenance operation to obtain a comprehensive management instruction; According to the comprehensive management instruction, use the distributed control algorithm to execute the comprehensive management operation; After the comprehensive management operation is executed, real-time monitor the operation status data of the high-voltage power measurement device after execution; Input the real-time monitored operation status data into the neural network model for a second aging trend prediction to determine whether the aging trend accelerates or the potential failure risk escalates; If the aging trend accelerates or the potential failure risk escalates, update the comprehensive management strategy and execute the corresponding operation; According to the updated comprehensive management strategy, use the dynamic programming algorithm to re-optimize the maintenance frequency and resource allocation to obtain the final comprehensive management optimization execution plan.

[0044] Specifically, the high-voltage power measurement device integrates the fault diagnosis results and maintenance strategies through an integrated management optimization plan, thus providing comprehensive guarantee for the long-term stable operation of the device. By analyzing the insulation aging trend and potential fault risks of current transformers, targeted isolation and maintenance strategies are formulated. This integration method not only improves the reliability of the device but also optimizes the resource allocation. Through the fuzzy logic control algorithm in determining the maintenance operation steps, when the partial discharge level of a certain voltage transformer is detected to be close to the warning line, the fuzzy controller will comprehensively consider multiple indicators such as load conditions and weather conditions, and dynamically adjust the urgency and specific operation sequence of the maintenance. This flexible decision-making mechanism ensures both power supply reliability and avoids unnecessary power outages.

[0045] By applying the distributed control algorithm, the maintenance operations become more efficient and coordinated. During a large-scale substation device maintenance, multiple intelligent terminal devices can receive instructions synchronously and independently complete corresponding tasks according to their respective roles. This collaborative operation mode shortens the maintenance work that originally took 48 hours to 12 hours, significantly reducing the power outage time and economic losses. The real-time monitoring and secondary prediction mechanism provides dynamic optimization for device management. When a 220kV substation is implementing the predetermined maintenance strategy, through real-time data analysis, it is found that the oil temperature of the main transformer rises abnormally. The neural network model immediately conducts secondary prediction and identifies the potential fault risk of the cooling system. The maintenance team adjusts the original plan accordingly and gives priority to dealing with the cooling system problem, successfully preventing potential faults caused by transformer overheating. If it is found that the aging trend accelerates or the fault risk escalates, the system will timely update the integrated management optimization plan. After it is found that the insulation performance of a certain current transformer shows an accelerating deterioration trend, the prediction system gives a warning that insulation breakdown may occur within three months. The management system immediately adjusts the maintenance strategy, increases the detection frequency of the device, and arranges for the replacement of spare parts. This rapid response mechanism effectively prevents large-scale power outage accidents that may be caused.

[0046] By optimizing the maintenance frequency and resource allocation through the dynamic programming algorithm, in a regional power grid covering multiple substations, this algorithm comprehensively considers factors such as the device conditions of each site, the distribution of maintenance personnel, and the spare parts inventory, and formulates an optimal maintenance strategy. This not only reduces the annual maintenance cost by 18% but also improves the overall availability of the device. By implementing this integrated management implementation plan, a provincial power company has achieved high-precision, high-response speed, and long-term stable operation of the high-voltage power measurement device. The average time between failures of the device has been extended by 35%, the annual maintenance cost has been reduced by 25%, and the power grid reliability index has been significantly improved. This systematic management mode provides strong support for the construction and operation of the smart grid, effectively solving the problems of measurement accuracy, aging maintenance, and fault isolation of devices in the high-voltage environment.

[0047] In an alternative embodiment of the present invention, the dynamic programming algorithm is optimized by using a comprehensive management optimization objective function, and the comprehensive management optimization objective function is:

[0048]

[0049] wherein, F(t) represents the comprehensive management optimization objective function at time t, Mi(t) represents the state of the i-th maintenance item at time t, αi is the weight coefficient, R(t) represents the resource usage at time t, β is the resource usage weight, C(t) represents the cost at time t, and γ is the cost weight. This formula comprehensively considers the maintenance status, resource utilization, and cost factors.

[0050] By introducing the comprehensive management optimization objective function into the dynamic programming algorithm, precise scheduling and optimization of various maintenance items can be carried out at different time points. By comprehensively considering the maintenance item status, resource usage, and cost, the optimal allocation of resources can be effectively achieved, the resource usage efficiency can be improved, and resource waste can be avoided. The weight coefficients in the optimization objective function can dynamically adjust the priorities, resource usage, and costs of various maintenance items according to the actual situation, so as to achieve the purpose of balancing maintenance work and cost expenditure. The system can flexibly adjust the priorities of each item according to real-time data to ensure that key projects are processed in a timely manner while controlling the overall cost. By comprehensively considering cost factors and combining the weights of resource usage, the cost expenditure during the maintenance process can be precisely controlled while the maintenance tasks are completed. This optimization process can effectively reduce unnecessary expenditures, reduce waste of resources and funds, and improve economic benefits. The present invention solves the comprehensive management optimization objective function in real time through the dynamic programming algorithm, enabling the system to quickly adjust and make decisions according to changing situations. This fast response mechanism can significantly improve decision-making efficiency, ensure optimal decisions can be made in complex environments, and thus avoid delays in maintenance work or waste of resources.

[0051] Such as Figure 2 , the high-voltage power measurement system 100 may include:

[0052] A data acquisition module 11, which is used to acquire real-time voltage and current data, and uses an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuations on the measurement accuracy, and obtains the preliminarily filtered voltage and current data;

[0053] An isolation strategy module 12, which is used to extract characteristic parameters from the voltage and current data, input the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generate an isolation strategy in a high-voltage environment according to the fault diagnosis result. The isolation strategy uses a control algorithm based on fuzzy logic to determine the priority and execution order of fault isolation;

[0054] The maintenance strategy module 13 is used to obtain the operation status data of the power measurement device, adopt a prediction model based on a neural network to judge the aging trend and potential failure risk of the device, and generate a maintenance strategy for the device in a high-voltage environment according to the aging trend and potential failure risk;

[0055] The management optimization module 14 is used to integrate the isolation strategy and the maintenance strategy to generate a comprehensive management optimization plan in a high-voltage environment.

[0056] For the specific functions and example descriptions of each module and sub-module of the device in the embodiments of the present invention, reference can be made to the relevant descriptions of the corresponding steps in the above method embodiments, which will not be elaborated here.

[0057] In the technical solution of the present invention, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0058] According to the embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0059] Figure 3 FIG. shows a schematic block diagram of an exemplary electronic device 200 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital assistant, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0060] As Figure 3 shown, the device 200 includes a computing unit 201, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 202 or the computer program loaded from the storage unit 208 into the random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the device 200 can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other through a bus 204. The input / output (I / O) interface 205 is also connected to the bus 204.

[0061] Multiple components in device 200 are connected to I / O interface 205, including: input unit 206, such as a keyboard, mouse, etc.; output unit 207, such as various types of displays, speakers, etc.; storage unit 208, such as a disk, optical disc, etc.; and communication unit 209, such as a network card, modem, wireless communication transceiver, etc. Communication unit 209 allows device 200 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0062] Computing unit 201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 201 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 201 executes the various methods and processes described above, such as a high-voltage power measurement optimization method. For example, in some embodiments, a high-voltage power measurement optimization method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 200 via ROM 202 and / or communication unit 209. When the computer program is loaded into RAM 203 and executed by computing unit 201, one or more steps of the high-voltage power measurement optimization method described above can be executed. Alternatively, in other embodiments, computing unit 201 can be configured to execute a high-voltage power measurement optimization method in any other suitable manner (e.g., by means of firmware).

[0063] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0064] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0065] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0066] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0067] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0068] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating a blockchain.

[0069] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved, and no limitations are imposed herein.

[0070] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A high voltage electric energy measurement optimization method, characterized in that: include: Acquire real-time voltage and current data, use adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuation on measurement accuracy, and obtain preliminary filtered voltage and current data; Extracting characteristic parameters from the voltage and current data, inputting the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generating an isolation strategy under a high-voltage environment according to the fault diagnosis result, wherein the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; Acquire the operating status data of the electric energy measuring device, use a prediction model based on a neural network to determine the aging trend and potential failure risk of the device, and generate a maintenance strategy for the device under a high-voltage environment according to the aging trend and the potential failure risk; The isolation strategy is integrated with the maintenance strategy to generate a comprehensive management optimization solution under a high-pressure environment.

2. The method according to claim 1, characterized in that The real-time voltage and current data are obtained by using an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuation on the measurement accuracy, and the voltage and current data after preliminary filtering are obtained, specifically: Acquire real-time voltage and current data under high voltage environment, and use multi-sensor fusion technology to synchronously collect and preprocess the voltage and current data; According to the pre-processed voltage and current data, an adaptive filtering algorithm is used to eliminate the influence of electromagnetic interference on the measurement accuracy, and a temperature compensation algorithm is used to eliminate the influence of temperature fluctuation on the measurement accuracy; The voltage and current data after adaptive filtering and temperature compensation are fused to obtain the voltage and current data after preliminary filtering.

3. The method according to claim 1, characterized in that The characteristic parameters are extracted from the voltage and current data, and the characteristic parameters are input into a preset fault diagnosis model to obtain a fault diagnosis result. An isolation strategy under a high voltage environment is generated according to the fault diagnosis result. The isolation strategy uses a control algorithm based on fuzzy logic to determine the priority and execution order of fault isolation, which is specifically: Extracting the characteristic parameters under the high voltage environment according to the voltage and current data after preliminary filtering, wherein the characteristic parameters include the voltage fluctuation frequency and the current harmonic component; Generate a feature data set according to the feature parameters, input the feature data set into a preset fault diagnosis model, the fault diagnosis model uses a support vector machine algorithm to determine the fault type and location, and outputs the fault diagnosis result; generating an isolation strategy under a high-voltage environment according to the fault diagnosis result, wherein the isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; A distributed control algorithm is used to execute the fault isolation operation of the isolation strategy.

4. The method according to claim 1, characterized in that: The operation status data of the electric energy measuring device is obtained, and a prediction model based on a neural network is used to determine the aging trend and potential failure risk of the device, and a maintenance strategy for the device under a high-voltage environment is generated according to the aging trend and the potential failure risk, specifically: Acquiring the operating status data of the electric energy measuring device, wherein the operating status data includes temperature and insulation resistance value; Inputting the operating status data into a pre-trained neural network model, determining the aging trend and the potential failure risk of the device, and obtaining an aging trend prediction result and a failure risk level; generating the maintenance strategy of the high-voltage electric energy measuring device according to the aging trend prediction result and the fault risk level; A dynamic programming algorithm is used to optimize maintenance frequency and resource allocation to obtain the optimized maintenance strategy.

5. The method according to claim 1, characterized in that The isolation strategy is integrated with the maintenance strategy to generate a comprehensive management optimization solution under a high-pressure environment, specifically: Integrate the isolation strategy of the fault diagnosis result with the maintenance strategy of the high-voltage electric energy measuring device to generate the comprehensive management optimization plan under the high-voltage environment; According to the comprehensive management optimization plan, a fuzzy logic control algorithm is used to determine the specific steps and execution sequence of the maintenance operation to obtain a comprehensive management instruction; According to the integrated management instructions, a distributed control algorithm is used to perform integrated management operations.

6. The method according to claim 5, characterized in that After the comprehensive management operation is executed, real-time monitoring of the operating status data of the high-voltage electric energy measuring device after the execution; Inputting the real-time monitored operating status data into the neural network model to perform a second aging trend prediction to determine whether the aging trend is accelerated or whether the potential failure risk is upgraded; If the aging trend is accelerated or the potential failure risk is escalated, the comprehensive management strategy is updated and corresponding operations are performed; According to the updated comprehensive management strategy, a dynamic programming algorithm is used to re-optimize the maintenance frequency and resource allocation to obtain the final comprehensive management optimization execution plan.

7. The method according to claim 6, characterized in that The dynamic programming algorithm uses a comprehensive management optimization objective function for optimization, and the comprehensive management optimization objective function is: Among them, F(t) represents the comprehensive management optimization objective function at time t, Mi(t) represents the status of the i-th maintenance item at time t, αi is the weight coefficient, R(t) represents the resource usage at time t, β is the resource usage weight, C(t) represents the cost at time t, and γ is the cost weight.

8. A high voltage electric energy measurement system, characterized in that: include: A data acquisition module, which is used to acquire real-time voltage and current data, and adopts an adaptive filtering algorithm to eliminate the influence of electromagnetic interference and temperature fluctuation on the measurement accuracy, so as to obtain the voltage and current data after preliminary filtering; An isolation strategy module, the isolation strategy module is used to extract characteristic parameters from the voltage and current data, input the characteristic parameters into a preset fault diagnosis model to obtain a fault diagnosis result, and generate an isolation strategy under a high-voltage environment according to the fault diagnosis result. The isolation strategy uses a fuzzy logic-based control algorithm to determine the priority and execution order of fault isolation; A maintenance strategy module, the maintenance strategy module is used to obtain the operating status data of the electric energy measurement device, use a prediction model based on a neural network to determine the aging trend and potential failure risk of the device, and generate a maintenance strategy for the device under a high-voltage environment according to the aging trend and the potential failure risk; A management optimization module, wherein the management optimization module is used to integrate the isolation strategy with the maintenance strategy to generate a comprehensive management optimization solution under a high-pressure environment.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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