Power equipment operation state evaluation method and system using artificial intelligence

Abstract

The application relates to the technical field of power equipment evaluation, and discloses a power equipment operation state evaluation method and system adopting artificial intelligence. The method first collects multi-dimensional operation parameters of power equipment, and constructs a dynamic state model; then an artificial intelligence evaluation framework is set and its performance is verified to obtain evaluation capability indexes; subsequently, the framework is integrated into the dynamic state model; then the total energy consumption in a specified time period is analyzed, and theoretical evaluation requirements and evaluation data traffic are calculated in combination with the evaluation capability indexes; adjustment instructions of an evaluation control unit are generated based on the evaluation data traffic, and parameters are adjusted; finally, the evaluation effect is quantitatively analyzed. Through multi-dimensional parameter integration, framework performance verification, dynamic adjustment and effect quantification, the method improves the comprehensiveness, reliability and adaptability of power equipment operation state evaluation, and is suitable for state monitoring and evaluation of various power equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment evaluation, in particular to a power equipment operation state evaluation method and system using artificial intelligence. BACKGROUND

[0002] In the stable operation of the power system, the state evaluation of the power equipment is an important link to ensure the reliability of power supply. With the continuous expansion of the power network and the improvement of the complexity of the equipment, the traditional power equipment operation state evaluation method gradually exposes many limitations.

[0003] Traditional methods mostly rely on manual inspection or single parameter monitoring, which is difficult to fully capture the running state of the equipment. For example, by monitoring only a few parameters such as temperature or voltage of the equipment, it is impossible to reflect the complex physical changes and potential failure risks inside the equipment. At the same time, these methods often use fixed evaluation cycles, and for equipment with large fluctuations in running state, the problem of evaluation lag or over-evaluation is likely to occur.

[0004] With the increase of the running time of the power equipment, the aging and performance degradation of the equipment show nonlinear characteristics, and the traditional evaluation method based on empirical model is difficult to accurately describe these dynamic changes. In large-scale power systems, the massive operation data also put forward higher requirements for the real-time and accuracy of the evaluation method, and the traditional data processing and analysis means have been difficult to meet the actual demand.

[0005] In recent years, the application of artificial intelligence technology in various industries has provided a new idea for power equipment state evaluation, but the existing evaluation methods based on artificial intelligence still have shortcomings. Some methods fail to fully integrate the multi-dimensional running parameters of the equipment, resulting in insufficient comprehensiveness of the evaluation model; some methods lack in performance verification and dynamic adjustment of the evaluation framework, making the reliability and adaptability of the evaluation results need to be improved. Therefore, an artificial intelligence evaluation method that can comprehensively utilize multi-dimensional parameters, has dynamic adjustment capability and quantifiable evaluation effect is urgently needed to improve the accuracy and effectiveness of the power equipment operation state evaluation. SUMMARY

[0006] The purpose of the present application is to provide a power equipment operation state evaluation method and system using artificial intelligence to solve the problems raised in the background.

[0007] To achieve the above purpose, the present application provides a power equipment operation state evaluation method using artificial intelligence, which comprises:

[0008] Collecting multi-dimensional running parameters of the power equipment during operation, and constructing a dynamic state model of the power equipment based on the multi-dimensional running parameters;

[0009] An artificial intelligence evaluation framework is set up and performance verification is performed on the artificial intelligence evaluation framework to obtain an evaluation capability index of the artificial intelligence evaluation framework;

[0010] The artificial intelligence evaluation framework is integrated into the dynamic state model;

[0011] The total energy consumption of the power equipment in a specified time period is analyzed, and the theoretical evaluation requirement of the artificial intelligence evaluation framework in the specified time period is calculated. Combined with the evaluation capability index of the artificial intelligence evaluation framework, the evaluation data traffic of the artificial intelligence evaluation framework in the specified time period is calculated;

[0012] Based on the evaluation data traffic of the artificial intelligence evaluation framework in the specified time period, an adjustment instruction of an evaluation control unit in the artificial intelligence evaluation framework is generated;

[0013] Based on the adjustment instruction of the evaluation control unit, a parameter adjustment operation is performed on the evaluation control unit;

[0014] The evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment is quantitatively analyzed.

[0015] Preferably, the collection of multi-dimensional running parameters of the power equipment during operation and the construction of a dynamic state model of the power equipment based on the multi-dimensional running parameters include:

[0016] The physical structure information of the power equipment is obtained, and the power equipment is divided into a plurality of functional sub-regions;

[0017] The parameter monitoring points of each functional sub-region are extracted to form a multi-dimensional running parameter set of the power equipment;

[0018] The energy accumulation capacity of each parameter monitoring point is calculated, and the external interference factors acting on the parameter monitoring points are identified;

[0019] Based on the energy accumulation capacity of each parameter monitoring point and the external interference factors, an energy balance equation of the power equipment is established.

[0020] Preferably, the establishment of the energy balance equation of the power equipment based on the energy accumulation capacity of each parameter monitoring point and the external interference factors includes:

[0021] The interaction effect between each parameter monitoring point and the influence degree of the external interference factors on each parameter monitoring point are extracted respectively, and based on the energy accumulation capacity of each parameter monitoring point, the interaction effect between each parameter monitoring point and the influence degree of the external interference factors on each parameter monitoring point, the energy balance equation of the power equipment is constructed.

[0022] Preferably, the artificial intelligence evaluation framework is set up, and the performance of the artificial intelligence evaluation framework is verified to obtain evaluation capability indexes of the artificial intelligence evaluation framework, including:

[0023] The evaluation performance of the artificial intelligence evaluation framework in the data processing stage, the model inference stage and the result output stage is verified respectively to obtain the evaluation capability indexes of the artificial intelligence evaluation framework;

[0024] In the verification in the data processing stage, the pre-processing capability of the framework for the multi-dimensional operation parameters of the power equipment needs to be tested; in the verification in the model inference stage, the real-time performance and the accuracy of the artificial intelligence algorithm are evaluated; and in the verification in the result output stage, the stability and the explainability of the evaluation results are focused on.

[0025] Preferably, the artificial intelligence evaluation framework is integrated into the dynamic state model, including:

[0026] The parameter monitoring points in the dynamic state model are analyzed, the parameter monitoring point with the greatest influence is identified, and the artificial intelligence evaluation framework is set at the corresponding position in the dynamic state model mapped from the parameter monitoring point with the greatest influence.

[0027] Preferably, the total energy consumption of the power equipment in a specified time period is analyzed, the theoretical evaluation demand of the artificial intelligence evaluation framework in the specified time period is calculated, and the evaluation data flow of the artificial intelligence evaluation framework in the specified time period is calculated in combination with the evaluation capability indexes of the artificial intelligence evaluation framework, including:

[0028] The energy input of each parameter monitoring point in the specified time period is calculated and summarized to obtain the total energy consumption of the power equipment in the specified time period;

[0029] The energy consumed by the artificial intelligence evaluation framework itself in the specified time period is obtained, and the total energy consumption of the power equipment in the specified time period is combined to obtain the theoretical evaluation demand of the artificial intelligence evaluation framework in the specified time period;

[0030] The theoretical evaluation demand of the artificial intelligence evaluation framework in the specified time period is combined with the evaluation capability indexes of the artificial intelligence evaluation framework to obtain the evaluation data flow of the artificial intelligence evaluation framework in the specified time period.

[0031] Preferably, the adjustment instruction of the evaluation control unit in the artificial intelligence evaluation framework is generated based on the evaluation data flow of the artificial intelligence evaluation framework in the specified time period, including:

[0032] Test the unit data flow processing capability of the evaluation control unit in the active state, and combine the evaluation data flow of the artificial intelligence evaluation framework in the specified time period to determine the activation time length of the evaluation control unit.

[0033] Preferably, the parameter adjustment operation of the evaluation control unit based on the adjustment instruction of the evaluation control unit comprises:

[0034] Divide the specified time period into multiple discrete time period units, and uniformly distribute the activation time length of the evaluation control unit into multiple discrete time period units;

[0035] After the end of the previous discrete time period unit, determine whether the data flow processing of the evaluation control unit in the discrete time period unit reaches the expected target;

[0036] Adaptively correct the activation time length of the evaluation control unit in the next discrete time period unit until the parameter adjustment operation of the evaluation control unit in the specified time period is completed.

[0037] Preferably, the quantitative analysis of the evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment comprises:

[0038] Obtain the parameter change amount of the power equipment before and after state evaluation;

[0039] Obtain the actual evaluation requirement of the power equipment based on the parameter change amount;

[0040] Compare and analyze the actual evaluation requirement and the theoretical evaluation requirement to obtain the control weight of the evaluation control unit in the artificial intelligence evaluation framework;

[0041] Quantitatively analyze the evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment based on the control weight.

[0042] Preferably, the present application further comprises a power equipment running state evaluation system using artificial intelligence, which is used to realize the power equipment running state evaluation method using artificial intelligence as described above, and the system comprises:

[0043] A dynamic state model construction module, the dynamic state model construction module is used for collecting multi-dimensional running parameters of the power equipment in the running process, and constructing a dynamic state model of the power equipment based on the multi-dimensional running parameters;

[0044] An artificial intelligence evaluation framework construction module, the artificial intelligence evaluation framework construction module is used for setting an artificial intelligence evaluation framework, and verifying the performance of the artificial intelligence evaluation framework to obtain an evaluation capability index of the artificial intelligence evaluation framework;

[0045] a framework integration module for integrating the artificial intelligence evaluation framework into the dynamic state model;

[0046] an evaluation data flow calculation module for analyzing the total energy consumption of the power equipment within a specified time period, and calculating the theoretical evaluation requirement of the artificial intelligence evaluation framework in a specified time period, combining the evaluation capability index of the artificial intelligence evaluation framework, to calculate the evaluation data flow of the artificial intelligence evaluation framework in a specified time period;

[0047] an adjustment instruction generation module for generating adjustment instructions of the evaluation control unit in the artificial intelligence evaluation framework based on the evaluation data flow of the artificial intelligence evaluation framework in a specified time period;

[0048] a parameter adjustment control module for performing parameter adjustment operation on the evaluation control unit based on the adjustment instructions of the evaluation control unit;

[0049] an evaluation effect verification module for quantitatively analyzing the evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment.

[0050] Compared with the prior art, the beneficial effects of the present application are:

[0051] By collecting multi-dimensional running parameters to construct a dynamic state model, the state characteristics of the power equipment under different running conditions can be comprehensively reflected. The integration of multi-dimensional parameters covers voltage, current, temperature, vibration and other aspects in equipment operation, so that the model can capture potential correlations and abnormal signals that cannot be reflected by a single parameter, thereby being closer to the actual operation of the equipment.

[0052] The artificial intelligence evaluation framework is set and performance verification is performed to obtain evaluation capability indexes, providing a reliable benchmark for the evaluation process. The performance verification link can discover the performance differences of the framework in different scenarios in advance, ensure that it has stable evaluation capability in actual application, and reduce evaluation deviation caused by defects of the framework itself.

[0053] Integrating the artificial intelligence evaluation framework into the dynamic state model realizes the organic combination of the model and the evaluation algorithm. This integration is not a simple superposition, but an evaluation framework that can operate based on the real-time updated parameters of the dynamic model, so that the evaluation process and the change of the equipment state remain synchronized, enhancing the timeliness of the evaluation.

[0054] The total energy consumption is analyzed, and the theoretical evaluation demand and evaluation data flow are calculated, so as to provide data basis for adjustment of the evaluation control unit. By combining the evaluation capability index, the data processing amount in the evaluation process can be reasonably planned, so as to avoid that the system is too heavy due to too large data flow, or the evaluation accuracy is affected due to insufficient data, and the evaluation process is more efficient.

[0055] The adjustment instruction is generated based on the evaluation data flow, and the parameter adjustment of the evaluation control unit is performed, so that the evaluation system is self-adaptive. When the device running state changes greatly, the control unit can timely adjust the parameters, change the frequency and data sampling density of the evaluation, so that the evaluation method can adapt to different running scenes, whether it is a smooth stage of normal device running or a complex stage of abnormal fluctuation, and good evaluation performance can be maintained.

[0056] The quantitative analysis of the evaluation effect can clearly present the actual performance of the evaluation method. Through the quantitative index, the consistency degree of the evaluation result and the actual state of the device can be intuitively understood, the problems existing in the evaluation process can be found and targeted optimization can be performed, the evaluation system can be continuously improved, and the overall evaluation level can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A working principle diagram of the power equipment running state evaluation method using artificial intelligence is provided.

[0058] Figure 2 A flowchart for constructing a dynamic state model is provided.

[0059] Figure 3 A flowchart for integrating an artificial intelligence evaluation framework is provided.

[0060] Figure 4 A flowchart for calculating evaluation data flow is provided.

[0061] Figure 5 A flowchart for parameter adjustment of an evaluation control unit is provided. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] Please refer to Figure 1 The present application provides a power equipment running state evaluation method using artificial intelligence, which comprises:

[0064] A dynamic state model is constructed by collecting multi-dimensional operating parameters of power equipment, and an artificial intelligence evaluation framework is integrated to achieve efficient evaluation. The specific process includes: collecting multi-dimensional parameters such as voltage, current, temperature, vibration, etc. during the operation of power equipment, and establishing a dynamic state model based on the correlation between parameters; designing an artificial intelligence evaluation framework to verify its performance in data processing, model reasoning and result output stage, and obtaining evaluation capability indicators; integrating the framework into the dynamic state model, calculating the evaluation data flow by analyzing the total energy consumption of the power equipment and the theoretical evaluation demand of the framework; generating adjustment instructions for the evaluation control unit according to the data flow, and dynamically adjusting its parameters; finally, quantitatively analyzing the evaluation effect to realize accurate evaluation of the operating state of power equipment.

[0065] Embodiment 1: refer to Figure 2 The construction process of the dynamic state model of the power equipment is based on multi-dimensional operating parameters. By analyzing the energy changes of each functional area inside the equipment and external interference factors, a mathematical model is established that can reflect the actual operating state of the equipment. The core of this model is to accurately capture the energy flow and loss characteristics during the operation of the power equipment, thereby providing reliable data support for subsequent artificial intelligence evaluation.

[0066] Firstly, the power equipment is divided into multiple functional sub-regions according to its physical structure characteristics. For example, for transformer equipment, it can be mainly divided into winding region, core region, cooling system and insulation oil circulation system, etc. Each functional sub-region plays a different role in the operation of the equipment, and the change of its operating state directly affects the performance of the whole equipment. The winding region is responsible for the transmission and conversion of electric energy, and its operating state can be characterized by parameters such as current and temperature; the core region involves magnetic circuit closure and magnetic loss, and its state parameters include magnetic flux density, core temperature, etc.; the cooling system reflects the heat dissipation efficiency through parameters such as oil temperature and flow rate. By dividing the functional sub-regions, it is possible to more accurately locate the abnormalities or potential problems in the operation of the equipment.

[0067] In each functional sub-region, corresponding parameter monitoring points are set to collect multi-dimensional operating data. For example, the monitoring points in the winding region can include hot spot temperature sensors, current transformers and partial discharge detection devices; the monitoring points in the core region can be equipped with magnetic flux sensors and vibration sensors; the monitoring points in the cooling system include oil temperature sensors, oil flow meters, etc. These monitoring points constitute the multi-dimensional operating parameter set of the power equipment, covering electrical, mechanical, thermodynamic and other aspects of state information. The collected data not only includes real-time measurement values, but also records the historical change trend, so as to analyze the dynamic evolution law of the parameters.

[0068] For each parameter monitoring point, its energy accumulation ability needs to be calculated. The energy accumulation ability reflects the characteristics of the monitoring point in absorbing, storing or releasing energy during operation. For example, the energy accumulation ability of the winding monitoring point can be calculated by its resistance loss and heat capacity characteristics, which is embodied in the relationship between temperature rise and heat storage; the energy accumulation ability of the core monitoring point is related to the hysteresis loss and eddy current loss, which is embodied in the efficiency of magnetic energy conversion into heat energy. By quantifying the energy accumulation ability of each monitoring point, the role of different regions in energy balance can be determined.

[0069] At the same time, external interference factors acting on each monitoring point need to be identified. External interference factors include environmental temperature fluctuations, power grid voltage fluctuations, load mutations, etc. These factors directly affect the parameter changes of the monitoring point, for example, the increase of environmental temperature may cause the cooling system efficiency to decrease, and then cause the winding temperature to abnormally rise; power grid voltage fluctuations may exacerbate core magnetic saturation, leading to increased vibration. By analyzing the type, intensity and action path of the interference factors, the inducement of parameter changes can be more comprehensively understood.

[0070] After completing the analysis of the energy accumulation ability of the monitoring point and the external interference factors, the interaction effect between each monitoring point needs to be further studied. The power equipment is a complex coupled system, and there is mutual transmission of energy and signals between each functional sub-region. For example, the temperature rise of the winding will affect the core temperature through heat conduction, and the core vibration may in turn exacerbate the aging of the winding insulation. This interaction effect makes the parameter change of a single monitoring point may trigger a chain reaction. By establishing the correlation matrix between monitoring points, the influence weight between different regions can be quantified, so as to more accurately describe the dynamic behavior of the whole equipment.

[0071] Based on the above analysis, the energy balance equation of the power equipment is constructed. The equation takes the energy input, output and internal loss of each monitoring point as the core, and describes the energy flow in the equipment operation through mathematical relationships. For example, the energy input of the winding region is electric power, the output is heat loss and heat conduction to the core, and the internal loss includes resistance heating and insulation medium loss; the energy input of the core region is magnetic energy, the output is vibration and heat, and the internal loss includes hysteresis and eddy current loss. By integrating these relationships into a system of equations, the energy distribution state of the equipment under different operating conditions can be dynamically simulated.

[0072] The solution of the energy balance equation relies on the real-time updated monitoring point data. During the operation of the device, the parameters of each monitoring point continuously change, and the equation needs to be dynamically adjusted according to the latest data. For example, when the load increases and the current rises, the electrical power input of the winding area increases, and the equation needs to adjust the calculation weight of its heat loss and conduction heat accordingly; when the ambient temperature drops, the heat dissipation efficiency of the cooling system improves, and the equation needs to reevaluate its impact on the winding temperature. This dynamic adjustment mechanism enables the model to track the changes in the running state of the device in real time.

[0073] The output of the model not only includes the energy distribution of each functional sub-region, but also further derives evaluation indicators of the health status of the device. For example, by comparing the deviation of the actual energy loss from the theoretical value, the degree of insulation aging can be judged; by analyzing the frequency spectrum characteristics of the core vibration energy, mechanical looseness or magnetic saturation phenomena can be identified. These indicators provide rich feature inputs for the subsequent artificial intelligence evaluation framework, enabling it to more comprehensively analyze the running state of the device.

[0074] The construction process of this dynamic state model fully considers the physical characteristics and operation rules of power equipment, and through multi-dimensional parameter collection, energy accumulation analysis, interference factor identification, and interaction modeling, it realizes the fine description of the device state. The dynamic adjustment capability of the model enables it to adapt to changes in different operating conditions, providing a reliable data foundation for artificial intelligence evaluation.

[0075] Example 2: Refer to Figure 3 The performance verification and integration process of the artificial intelligence evaluation framework is a key link in the method of evaluating the running state of power equipment. The design of this framework needs to meet the requirements of real-time, accuracy and stability, and its performance verification needs to cover the whole process of data processing, model reasoning and result output, and form a synergistic optimization evaluation system through reasonable integration strategy and dynamic state model.

[0076] In the verification of the data processing stage, the pre-processing ability of the framework for multi-dimensional operating parameters of power equipment needs to be tested. Power equipment monitoring data usually contains noise, missing values and outliers, and these data quality problems directly affect the reliability of subsequent analysis. The framework needs to have an adaptive filtering function, which can dynamically adjust the filtering parameters according to the signal characteristics, such as using sliding window mean filtering for high-frequency noise in current signals and using difference compensation for slow drift in temperature signals. For missing data, the framework needs to support multiple completion strategies, including linear interpolation, pattern filling based on historical data, and correlation completion of adjacent monitoring points. The outlier detection algorithm needs to dynamically adjust the threshold according to the device operating conditions to avoid false positives caused by normal operating conditions such as load mutation. By verifying the performance of different preprocessing methods under different data quality scenarios, the optimal combination of data cleaning and completion strategies can be determined.

[0077] The verification of the model inference stage focuses on evaluating the real-time performance and accuracy of the artificial intelligence algorithm. The state assessment of power equipment often uses deep neural network models, and the inference speed needs to meet the timeliness requirements of online monitoring. The framework needs to test the inference delay of different network structures on the target hardware platform, such as comparing the computational efficiency of convolutional neural networks and lightweight Transformer architectures on edge computing devices. Model accuracy verification needs to build a test dataset covering typical operating states, including normal conditions, early faults, intermediate faults, and severe faults. By analyzing the classification accuracy and confusion matrix of the model on different fault types, the sensitivity and specificity of the model to specific fault patterns can be identified. To address the potential overfitting problem of the model, regularization techniques and incremental learning strategies need to be used to ensure the model's generalization ability on new operating data.

[0078] The verification of the result output stage focuses on evaluating the stability and explainability of the evaluation results. The state assessment of power equipment needs to output clear health status levels or fault probabilities, and the volatility of these results needs to be controlled within a reasonable range. The framework needs to calculate the variance and trend consistency of consecutive evaluation results to avoid evaluation jumps caused by small changes in input data. To improve the credibility of the results, the framework should output auxiliary explanation information, such as the contribution ranking of key influencing parameters, comparison analysis with historical evaluation results, etc. For important fault warnings, a multi-level confirmation mechanism needs to be set up to trigger an alarm only when the same abnormal pattern is detected continuously multiple times, reducing the risk of false alarms.

[0079] After completing the phased performance verification, the artificial intelligence evaluation framework needs to be integrated into the dynamic state model. The core of the integration process is to establish a mapping relationship between the framework and the model parameter monitoring points. By analyzing the influence weight of each monitoring point in the dynamic state model, the key parameters that most determine the device state assessment are identified. The principal component analysis method is used to calculate the variance contribution rate of each monitoring point parameter to determine the core dimension that dominates the change of the device state. For example, in transformer state assessment, the parameters of top oil temperature, winding hot spot temperature, and oil dissolved gas content usually have high weights. Align the input features of the artificial intelligence evaluation framework with these key monitoring points to ensure that the framework receives the most informative input data.

[0080] The output of the framework needs to form a closed-loop feedback with the prediction module of the dynamic state model. The artificial intelligence evaluation results not only serve as the basis for the final state judgment, but also need to be fed back to the model's parameter adjustment mechanism. For example, when the framework detects insulation aging trends, the coefficients of the insulation material thermal aging equation in the model can be dynamically adjusted; when the evaluation results show a decrease in cooling efficiency, the correlation parameters of oil flow speed and heat dissipation capacity in the model can be corrected. This two-way interaction enables the dynamic state model to continuously optimize its parameters based on the evaluation results, forming a self-adaptive learning cycle.

[0081] The integrated system needs to be verified for consistency, testing the data interaction efficiency and logical consistency of the artificial intelligence framework and the dynamic state model. Check if there is a conflict between the framework output and the model prediction, such as whether the energy distribution state calculated by the model and the health level evaluated by the framework are logically consistent. For inconsistent cases, an arbitration mechanism needs to be established, such as using the Bayesian fusion method to integrate the two evaluation results. The system also needs to verify the rationality of resource allocation to ensure that the calculation load of the artificial intelligence framework does not affect the real-time updating capability of the dynamic state model.

[0082] The performance optimization of the artificial intelligence evaluation framework is a continuous iterative process. With the accumulation of power equipment operation data, the model parameters and logical rules of the framework need to be updated regularly. The framework should support online learning function, which can automatically filter valuable new data samples and trigger model retraining. At the same time, a version control mechanism needs to be established to preserve historical versions of the evaluation framework as a benchmark for comparison, facilitating the analysis of the actual effect of algorithm improvement.

[0083] Embodiment 3: refer to Figure 4 The calculation process of the evaluation data flow is based on the analysis of the energy consumption of the power equipment and the running demand of the artificial intelligence evaluation framework. This process determines the data processing scale required for the evaluation task by quantifying the relationship between the energy consumption of the equipment operation and the consumption of the evaluation system itself, providing accurate basis for the adjustment of the subsequent evaluation control unit. The calculation process needs to consider the characteristics of equipment operation, performance indicators of the evaluation framework, and dynamic changes in the time dimension.

[0084] The total energy consumption of the power equipment in a specified time period is calculated from the energy input statistics of the parameter monitoring points. Each functional sub-region's monitoring point continuously records the energy conversion data during the operation process, such as the Joule heat loss represented by the product of current square and resistance in the winding area, the sum of hysteresis loss and eddy current loss in the core area, and the power consumption of the oil pump motor in the cooling system. After these data are collected at a fixed sampling frequency, their cumulative amount in the time dimension is calculated by numerical integration method. For discrete sampling systems, the trapezoidal rule is used for integral calculation to balance the precision and computational complexity:

[0085]

[0086] where, represents the total energy consumption, is the sampling time interval, represents the instantaneous power value at the i-th sampling point, and n is the total number of sampling points. This calculation needs to cover all key parameter monitoring points and couple the energy consumption of each sub-region according to the physical correlation. For example, the temperature rise caused by winding heat will increase the cooling system load, and this interaction needs to be reflected in the total energy calculation.

[0087] The artificial intelligence evaluation framework also consumes energy during operation, which is sourced from the power supply system of the electrical equipment. The energy consumption of the framework mainly includes the computing power consumption of the data processing module, the operation energy consumption of the model inference unit, and the communication power consumption of the result output module. The energy consumption of the data processing module is linearly related to the input data volume, and the model inference energy consumption depends on the complexity of the neural network structure and the inference frequency. It is necessary to establish a real-time monitoring mechanism for the energy consumption of the framework, collect indicators such as the number of instruction cycles and cache hit rate of CPU / GPU through hardware performance counters, and estimate the actual energy consumption combined with the chip-level power consumption model. The total energy consumption value of the framework itself running is obtained by time integration of the above energy consumption components within a specified time period.

[0088] The theoretical evaluation demand reflects the actual net energy support required for the evaluation task, and its calculation needs to consider the allocation relationship between the total energy consumption of the equipment and the energy consumption of the framework itself. After deducting the basic energy consumption of the framework running from the total energy consumption of the electrical equipment, the remaining energy is the resource that can be used for the state evaluation task. This part of energy determines the maximum evaluation intensity that the system can support, including the data processing depth, model inference frequency, and result output accuracy. The calculation of the theoretical evaluation demand needs to introduce an energy allocation weight coefficient, which is determined by the equipment running priority strategy. For example, in the heavy load working condition, the energy quota of the evaluation task may be appropriately reduced to ensure the safety of the equipment itself.

[0089] The calculation of the evaluation data flow converts the theoretical evaluation demand into a specific data processing scale. The performance indicators of the artificial intelligence evaluation framework include a unit energy processing capacity parameter, which is obtained through offline testing and represents the standard data volume that the framework can process per unit of energy consumed. In the voltage sag detection scenario, the unit energy processing capacity may be represented as the number of waveform sampling points that can be analyzed per joule of energy; in the temperature field reconstruction task, it may represent the number of grid nodes that can be processed per joule of energy. Multiplying the theoretical evaluation demand by the unit energy processing capacity gives the upper limit of the evaluation data flow that the system can support within a specified time period.

[0090] The dynamic adjustment mechanism in the time dimension needs to be considered in the calculation process. The running state of the electrical equipment has time-varying characteristics, and its energy consumption often presents non-stationary characteristics. For example, when the load of the transformer fluctuates periodically, the winding loss will fluctuate with the change of the current; when the ambient temperature changes day and night, the heat dissipation energy consumption of the cooling system will also be adjusted accordingly. The calculation of the evaluation data flow cannot simply use the average value, but needs to establish a sliding time window mechanism to dynamically update the calculation results at multiple time scales. Typical implementation methods include calculating the instantaneous data flow to guide real-time control at a minute-level window and calculating the cumulative flow for resource planning at a hour-level window.

[0091] The spatial distribution characteristics of data traffic also need to be analyzed. Different functional sub-regions of power equipment generate data with different information density and value weight. For example, partial discharge monitoring data, although the data volume is not large, is essential for insulation state evaluation; while temperature distribution data is larger, the change of a single measuring point may have limited impact. Under the constraint of total data traffic, a data importance weighted allocation mechanism needs to be established to reserve sufficient processing resources for core monitoring parameters. This allocation can be achieved through a data value coefficient matrix, where the matrix elements represent the relative importance of each monitoring point data in state evaluation.

[0092] The verification of the calculation results is carried out using the principle of energy-data conservation. The total input energy of the system should be equal to the sum of the energy consumption of the equipment itself, the energy consumption of the framework operation, and the energy consumption of the evaluation data processing. By establishing an energy flow balance checking mechanism, it can be found that there may be error accumulation or unreasonable allocation in the data traffic calculation process. When there is a significant imbalance, it is necessary to backtrack to check the accuracy of the energy consumption monitoring data, the rationality of the theoretical evaluation requirements, and the applicability of the unit energy processing capacity, etc.

[0093] This embodiment establishes a quantitative bridge between the physical operation state of power equipment and the artificial intelligence evaluation digital system through strict energy metering and data conversion calculation. The calculation process fully considers the time dynamic characteristics and spatial distribution characteristics, so that the evaluation data traffic can accurately reflect the actual system resource constraints, providing an objective basis for the formulation of evaluation control strategies. The entire calculation system forms a closed-loop verification mechanism, which guarantees the rationality and reliability of the calculation results through the principle of energy conservation, supporting the long-term stable operation of the power equipment state evaluation system.

[0094] Example 4: see Figure 4 The adjustment instruction generation and execution process of the evaluation control unit is based on the evaluation data traffic calculated in Example 3, and is realized through a discrete time allocation and dynamic correction mechanism to achieve precise control. This process takes the transformer partial discharge online monitoring system as an example to show how to adjust the working mode of the control unit according to the data traffic demand.

[0095] In the transformer partial discharge monitoring scenario, the evaluation control unit is responsible for managing the working rhythm of the high-frequency signal acquisition card, the data processing module, and the classification inference unit. Assuming that Example 3 calculates the total evaluation data traffic in a 10-second time window as 850MB, the control unit needs to reasonably allocate the active time under the premise of ensuring data integrity. The system first tests the performance of the control unit in different working modes, and records the key parameters shown in Table 1.

[0096] Table 1: The system first tests the performance of the control unit in different working modes, and records the key parameters shown in the following table.

[0097]

[0098] The balanced mode is selected as the reference operating mode based on the test data, which achieves a good balance between data processing volume and quality. The 10-second evaluation period is divided into 20 time units, each lasting 500 ms. According to the total data flow of 850 MB, theoretically, 28.3 times of balanced mode activation is required (850 MB / 30 MB per time), and the total activation time needs 2264 ms. Due to the time window limit, a uniform distribution strategy is adopted, allocating 113.2 ms of activation time in each 500 ms unit.

[0099] When the system performs the first round of adjustment, the control unit is activated for 113.2 ms in the first time unit (0-500 ms). Actual monitoring found that there was a slight disturbance in the power grid during this period, resulting in increased signal noise, and the actual processing data volume only reached 27 MB. The system starts the dynamic correction mechanism according to the deviation: the remaining data volume to be processed is 823 MB (850-27), and the remaining time is 9500 ms. After adjustment, the single activation time of the subsequent unit is increased to 116 ms (823 MB / (30 MB / 80 ms)), and the correction value retains 3% redundancy to deal with possible interference.

[0100] During the execution process of the subsequent time units, the system continuously monitors two key indicators: the actual processing data volume in the unit and the signal quality score. When the signal quality significantly improves (reaches 97%) in the 5th time unit (2000-2500 ms), the system appropriately reduces the activation time to 110 ms while maintaining stable data processing volume. Conversely, when the 12th time unit (5500-6000 ms) encounters strong electromagnetic interference, the system temporarily switches to high-speed mode, increasing the sampling rate while maintaining the original activation time, ensuring the complete capture of key discharge pulses.

[0101] The parameter adjustment of the control unit not only involves time allocation, but also includes the optimization of the combination of operating modes. During the night period when the transformer load is low, the system may adopt an alternating strategy of energy-saving mode and balanced mode: using energy-saving mode to process basic monitoring data for 3 consecutive time units, and switching to balanced mode for fine analysis in the 4th unit. This combination not only maintains the evaluation coverage, but also reduces the overall power consumption by about 15%.

[0102] The abnormal handling mechanism in the adjustment process is particularly important. When the actual processing data volume of two consecutive time units is less than 85% of the expected value, the system automatically triggers the diagnostic process: first, check the impedance matching of the signal acquisition channel, second, verify the adaptability of the data processing algorithm, and finally evaluate the power grid working condition changes. According to the diagnostic results, three countermeasures may be taken: adjusting the cutoff frequency of the pre-filter, switching to the backup data processing thread, or temporarily increasing the activation time by 10% for compensation.

[0103] When the final check is performed, the system aggregates the cumulative processing data volume of each time unit and compares it with the target value of 850 MB. Assuming that 842 MB is actually completed, the deviation rate is 0.94%, which is within the allowed 2% error range. The system records the parameters adjusted this time as historical benchmarks, including the average activation duration, mode switching frequency, dynamic correction amplitude, etc., for optimizing the initial allocation strategy in the subsequent evaluation period.

[0104] Through specific cases of transformer partial discharge monitoring, the complete process of evaluation control unit adjustment is demonstrated. From mode selection, time unit division to dynamic correction execution, the system always focuses on fine control around the data flow target. The test data in the table provides objective basis for initial decision-making, while the real-time monitoring and feedback mechanism ensures that the adjustment process adapts to changes in actual operating conditions. The entire implementation process embodies the core concept of adaptive control: under the constraint of limited resources, the best evaluation effect is achieved through continuous optimization.

[0105] Example 5: Quantitative analysis process of evaluation effect By comparing the parameter changes of power equipment before and after state evaluation with the theoretical expectation, the actual efficiency of the artificial intelligence evaluation framework is verified. Taking the insulation state evaluation of power transformers as an example, this process elaborates how to achieve objective measurement of evaluation effect through parameter difference analysis, demand deviation calculation and weight correction, etc.

[0106] In the baseline state establishment stage before evaluation, the system records the initial values of transformer key parameters, including winding direct current resistance, dielectric loss tangent value, oil dissolved gas content, etc. These parameters constitute the original feature set of equipment state, and each parameter is assigned an initial monitoring weight according to its physical meaning and importance in health evaluation. For example, winding direct current resistance reflects conductor integrity, and its change may indicate joint loosening or conductor fracture; dielectric loss tangent value represents the aging degree of insulation material; oil dissolved gas composition and content are key indicators for diagnosing internal latent faults.

[0107] After the evaluation process starts, the artificial intelligence evaluation framework performs multi-dimensional analysis on the running state of the transformer, and outputs the insulation state grade and potential risk prediction. The system continuously monitors the dynamic changes of each parameter during the evaluation process and records the final value of each parameter at the end of the evaluation period. By comparing the initial value and the final value of each parameter, the relative change rate is calculated. For example, the dielectric loss tangent value increases from 0.005 to 0.006, with a change rate of 20%; the oil ethyne gas content increases from 3 μL / L to 4 μL / L, with a change rate of 33%. These changes reflect the actual evolution of the equipment state during the evaluation process.

[0108] Based on the parameter change amount, the actual evaluation demand is backstepped, and the mapping relationship between the parameter change and the energy consumption needs to be established. The degree of change of each parameter corresponds to a specific evaluation resource demand, such as gas chromatography detection energy consumption for oil gas analysis, and energy consumption for applying test voltage for medium loss measurement. The system converts the change amount of each parameter into an equivalent evaluation energy value according to the preset parameter-energy conversion coefficient, and the actual evaluation total demand is obtained by summarizing. The value reflects the actual resource scale consumed by the evaluation framework to capture the device state change, and forms a comparable benchmark with the theoretical evaluation demand calculated in embodiment 3.

[0109] Demand deviation analysis is the core link of comparing the actual evaluation demand and the theoretical evaluation demand. The system calculates the absolute difference and the relative deviation rate of the two, and analyzes the source distribution of the deviation. The deviation may come from multiple aspects: the theoretical model underestimates the sensitivity of some parameters, such as the energy consumption of trace gas detection in oil; additional resource consumption caused by sudden working condition changes in the evaluation process, such as temporary increase in partial discharge monitoring demand; or fluctuations in the efficiency of the evaluation framework algorithm. By decomposing the contribution of each parameter to the total deviation, the main influencing factors are identified to provide directional guidance for control weight adjustment.

[0110] The control weight correction of the evaluation control unit is the key output of quantitative analysis. According to the deviation analysis results, the system dynamically adjusts the decision weight of each parameter in the evaluation process. For parameter categories whose actual demand is consistently higher than the theoretical expectation, appropriately increase its control weight and increase resource allocation; for parameters whose actual demand is lower than the theoretical expectation, appropriately reduce the weight. The weight adjustment adopts a gradual strategy, and the correction amplitude is controlled within 5%-10% of the original value, avoiding sharp fluctuations affecting the stability of the evaluation. The adjusted weight value will be used for the calculation of the theoretical demand in the next evaluation period, forming a closed-loop optimization mechanism.

[0111] Long-term tracking of evaluation effect needs to establish a historical data comparison mechanism. The system saves the parameter change mode, demand deviation characteristics and weight adjustment record of each evaluation, forming an evaluation efficiency evolution curve. By analyzing the convergence trend of the deviation rate in multiple evaluation cycles, it is judged whether the system gradually tends to be optimal evaluation state. At the same time, monitor the frequency and amplitude change of weight adjustment, verify the adaptive learning ability of the evaluation framework. Historical data is also used to identify the seasonal regularity or load correlation of device state evolution, providing reference for periodic optimization of evaluation strategy.

[0112] Through the specific application of transformer insulation state evaluation, the complete process of quantitative analysis of evaluation effect is demonstrated. From parameter change capture to demand deviation calculation, to dynamic adjustment of control weight, the system realizes the objective evaluation and continuous improvement of the effectiveness of artificial intelligence evaluation framework. The whole process emphasizes data-driven analysis logic, avoids the interference of subjective judgment, and ensures the scientificity and reliability of evaluation optimization. The quantitative analysis results not only verify the effectiveness of the current evaluation system, but also provide a clear improvement direction for subsequent algorithm upgrade and model iteration, forming a positive cycle of continuous improvement of power equipment state evaluation capability.

[0113] It should be noted that, in this text, relational terms such as first and second are used only to distinguish one entity or action from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0114] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power equipment operation state evaluation method using artificial intelligence, characterized by, The method comprises the following steps: Collecting multi-dimensional operation parameters of the power equipment during operation, and constructing a dynamic state model of the power equipment based on the multi-dimensional operation parameters; Setting up an artificial intelligence evaluation framework, and verifying the performance of the artificial intelligence evaluation framework to obtain evaluation capability indicators of the artificial intelligence evaluation framework; Integrating the artificial intelligence evaluation framework into the dynamic state model; Analyzing the total energy consumption of the power equipment within a specified time period, and calculating the theoretical evaluation demand of the artificial intelligence evaluation framework within the specified time period, combining the evaluation capability indicators of the artificial intelligence evaluation framework to calculate the evaluation data flow of the artificial intelligence evaluation framework within the specified time period; Generating adjustment instructions of an evaluation control unit in the artificial intelligence evaluation framework based on the evaluation data flow of the artificial intelligence evaluation framework within the specified time period; Performing parameter adjustment operations on the evaluation control unit based on the adjustment instructions of the evaluation control unit; Quantitatively analyzing the evaluation effect of the artificial intelligence evaluation framework on the operation state of the power equipment; The step of setting up an artificial intelligence evaluation framework, and verifying the performance of the artificial intelligence evaluation framework to obtain evaluation capability indicators of the artificial intelligence evaluation framework comprises the following steps: Respectively verifying the evaluation performance of the artificial intelligence evaluation framework in the data processing stage, the model inference stage and the result output stage to obtain the evaluation capability indicators of the artificial intelligence evaluation framework; In the verification in the data processing stage, the pre-processing capability of the framework on the multi-dimensional operation parameters of the power equipment needs to be tested; the real-time performance and accuracy of the artificial intelligence algorithm are the focus of the verification in the model inference stage; the stability and interpretability of the evaluation results are concerned in the verification in the result output stage.

2. The power equipment operation state evaluation method using artificial intelligence according to claim 1, characterized by, The step of collecting multi-dimensional operation parameters of the power equipment during operation, and constructing a dynamic state model of the power equipment based on the multi-dimensional operation parameters comprises the following steps: Obtaining physical structure information of the power equipment, and dividing the power equipment into a plurality of functional sub-regions; Extracting parameter monitoring points of each functional sub-region to form a multi-dimensional operation parameter set of the power equipment; Calculating the energy accumulation capability of each parameter monitoring point, and identifying external interference factors acting on the parameter monitoring points; Based on the energy accumulation capability of each parameter monitoring point and the external interference factors, an energy balance equation of the power equipment is established.

3. The power equipment operation state evaluation method using artificial intelligence according to claim 2, characterized by, The step of establishing an energy balance equation of the power equipment based on the energy accumulation capability of each parameter monitoring point and the external interference factors comprises the following steps: Respectively extracting the interaction effect between each parameter monitoring point and the influence degree of the external interference factors on each parameter monitoring point, and based on the energy accumulation capability of each parameter monitoring point, the interaction effect between each parameter monitoring point and the influence degree of the external interference factors on each parameter monitoring point, constructing an energy balance equation of the power equipment.

4. The power equipment operation state evaluation method using artificial intelligence according to claim 1, characterized by, The step of integrating the artificial intelligence evaluation framework into the dynamic state model comprises the following steps: Analyzing the parameter monitoring points in the dynamic state model, identifying the parameter monitoring point with the greatest influence, and setting the artificial intelligence evaluation framework at the corresponding position in the dynamic state model mapped from the parameter monitoring point with the greatest influence. 5.The power equipment operation state evaluation method using artificial intelligence according to claim 1, characterized in that, The analysis power equipment total energy consumption in a specified period, and calculates the theoretical evaluation demand of the artificial intelligence evaluation framework in a specified period, combined with the evaluation ability index of the artificial intelligence evaluation framework, calculates the evaluation data flow of the artificial intelligence evaluation framework in a specified period, including: Calculate and aggregate the energy input of each parameter monitoring point in a specified period, and obtain the total energy consumption of the power equipment in a specified period; Obtain the energy consumed by the artificial intelligence evaluation framework itself in a specified period, and combine the total energy consumption of the power equipment in a specified period to obtain the theoretical evaluation demand of the artificial intelligence evaluation framework in a specified period; Combine the theoretical evaluation demand of the artificial intelligence evaluation framework in a specified period with the evaluation ability index of the artificial intelligence evaluation framework to obtain the evaluation data flow of the artificial intelligence evaluation framework in a specified period. 6.The power equipment operation state evaluation method using artificial intelligence according to claim 1, characterized in that, Based on the evaluation data flow of the artificial intelligence evaluation framework in a specified period, the adjustment instruction of the evaluation control unit in the artificial intelligence evaluation framework is generated, including: Test the unit data flow processing capability of the evaluation control unit in the activated state, and combine the evaluation data flow of the artificial intelligence evaluation framework in a specified period to determine the activation time length of the evaluation control unit.

7. The power equipment operation state evaluation method using artificial intelligence according to claim 6, characterized by, Based on the adjustment instruction of the evaluation control unit, the parameter adjustment operation of the evaluation control unit includes: Divide the specified period into multiple discrete time period units, and evenly distribute the activation time length of the evaluation control unit to multiple discrete time period units; After the end of the previous discrete time period unit, judge whether the data flow processing of the evaluation control unit in the discrete time period unit reaches the expected target; Adaptively correct the activation time length of the evaluation control unit in the next discrete time period unit until the parameter adjustment operation of the evaluation control unit in a specified period is completed. 8.The power equipment operation state evaluation method using artificial intelligence according to claim 1, characterized in that, The quantitative analysis of the evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment includes: Obtain the parameter change amount of the power equipment before and after state evaluation; Based on the parameter change amount, obtain the actual evaluation demand of the power equipment; Compare and analyze the actual evaluation demand and the theoretical evaluation demand to obtain the control weight of the evaluation control unit in the artificial intelligence evaluation framework; Based on the control weight, the evaluation effect of the artificial intelligence evaluation framework on the running state of the power equipment is quantitatively analyzed.

9. An electric power equipment operation state evaluation system using artificial intelligence, characterized by, The system for implementing the artificial intelligence-based power equipment running state evaluation method as claimed in any one of claims 1 to 8, the system comprising: A dynamic state model construction module, the dynamic state model construction module is used for collecting multi-dimensional running parameters of power equipment in the running process, and constructing a dynamic state model of the power equipment based on the multi-dimensional running parameters; An artificial intelligence evaluation framework construction module, the artificial intelligence evaluation framework construction module is used for setting an artificial intelligence evaluation framework, and verifying the performance of the artificial intelligence evaluation framework to obtain the evaluation ability index of the artificial intelligence evaluation framework; a framework integration module, configured to integrate the artificial intelligence evaluation framework into the dynamic state model; an evaluation data flow calculation module, configured to analyze total energy consumption of the power equipment in a specified time period, and to calculate theoretical evaluation requirements of the artificial intelligence evaluation framework in the specified time period, and to calculate evaluation data flow of the artificial intelligence evaluation framework in the specified time period in combination with evaluation capability indexes of the artificial intelligence evaluation framework; an adjustment instruction generation module, configured to generate adjustment instructions of an evaluation control unit in the artificial intelligence evaluation framework based on the evaluation data flow of the artificial intelligence evaluation framework in the specified time period; a parameter adjustment control module, configured to perform parameter adjustment operation on the evaluation control unit based on the adjustment instructions of the evaluation control unit; an evaluation effect verification module, configured to quantitatively analyze evaluation effects of the artificial intelligence evaluation framework on the running state of the power equipment.

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