A power data processing method and system, and a storage medium

Abstract

This invention relates to the field of power data processing, and more particularly to a method, system, and storage medium for power data processing. The method includes the following steps: The invention acquires the operating parameters of multiple target power devices in a power system during a monitoring period, and determines the device operating status at a first monitoring time and a second monitoring time, respectively; calculates the change amplitude of monitoring parameters based on changes in operating status, and identifies the disturbance trajectory of device disturbance intensity evolving over time; acquires the physical connection relationships of the power devices, quantifies the degree of mutual influence between devices in conjunction with the disturbance trajectory, and outputs the interference intensity value between power devices; acquires historical aging data of the devices, analyzes the correlation between the disturbance trajectory, interference intensity value, and aging behavior, identifies disturbance change segments significantly affected by aging factors, and performs disturbance response correction accordingly, outputting disturbance-optimized operating data to achieve more accurate power data processing.

Description

Technical Field

[0001] This invention relates to the field of power data processing, and more particularly to a power data processing method, system, and storage medium. Background Technology

[0002] The perception of operational status, identification of operational anomalies, and health assessment of power equipment have become crucial links in ensuring the safe and stable operation of power systems. In actual power systems, complex physical connections and energy coupling characteristics exist between equipment. Various disturbances are transmitted and superimposed between equipment, leading to cascading effects and even large-scale system failures. Therefore, accurately identifying changes in equipment operational status, analyzing disturbance propagation paths, and assessing the potential impact of disturbances on equipment operational health have become important technical issues in the field of intelligent operation and maintenance of power equipment. Traditional power equipment condition assessment methods typically rely on static thresholds or fixed models for judgment, which are difficult to effectively cope with the complexity of the operating environment and the dynamic changes in disturbance response, especially in situations such as equipment aging, topology reconfiguration, or disturbance propagation, lacking flexibility and accuracy. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, and storage medium for processing power data to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, a method for processing power data includes the following steps:

[0005] Step S1: Obtain the operating parameters of multiple target power devices in the power system during the monitoring period, determine the first operating status of the device based on the operating parameters of each device at the first monitoring time, and determine the second operating status of the device based on the operating parameters of each device at the second monitoring time.

[0006] Step S2: Based on the first operating condition and the second operating condition, calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time, and identify the equipment disturbance evolution trajectory of the equipment disturbance intensity changing with time based on the change range of the monitoring parameters;

[0007] Step S3: Obtain the physical connection relationship of the power equipment in the power system and identify the equipment pairs with direct connection or indirect coupling paths; combine the equipment disturbance evolution trajectory to quantify the degree of mutual influence between each target equipment, and output the interference intensity value between power equipment based on the degree of influence between each equipment pair.

[0008] Step S4: Obtain historical aging data of power equipment, and determine the correlation between equipment aging behavior and disturbance based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment. Identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

[0009] The present invention also provides a power data processing system for performing the power data processing method described above, the power data processing system comprising:

[0010] The operation status monitoring module is used to acquire the operating parameters of multiple target power devices in the power system during the monitoring period, determine the first operating status of the device based on the operating parameters of each device at the first monitoring time, and determine the second operating status of the device based on the operating parameters of each device at the second monitoring time.

[0011] The disturbance evolution trajectory identification module is used to calculate the change range of monitoring parameters of the equipment between the first monitoring time and the second monitoring time according to the first operating condition and the second operating condition, and to identify the equipment disturbance intensity change trajectory over time based on the change range of monitoring parameters.

[0012] The interference intensity monitoring module is used to obtain the physical connection relationship of power equipment in the power system and identify equipment pairs with direct connection or indirect coupling paths; combined with the equipment disturbance evolution trajectory, it quantifies the degree of mutual influence between each target equipment and outputs the interference intensity value between power equipment based on the degree of influence between each equipment pair.

[0013] The disturbance response correction module is used to acquire historical aging data of power equipment, and based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment, determine the correlation between equipment aging behavior and disturbance, identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

[0014] A computer-readable storage medium storing a computer program, wherein the computer program is used to perform the method for processing the power data.

[0015] The present invention has the following beneficial effects:

[0016] 1) By extracting the operating parameters of power equipment over multiple monitoring periods and constructing the operating status at the first and second monitoring times respectively, a time-series model of the dynamic operating behavior of power equipment is achieved. Compared with existing methods that rely solely on static single-time parameters for evaluation, this method can reveal the changing trends of the equipment's operating status over continuous periods, providing more timely basic data support for subsequent disturbance identification and status analysis.

[0017] 2) By introducing the variation range of monitoring parameters, a quantitative index of the disturbance intensity of power equipment is constructed, and the disturbance evolution trajectory is further extracted. This not only enables numerical identification of the strength of the disturbance, but also reveals the periodic structure, change boundary and trend slope of the disturbance over time. This helps to accurately identify rising, oscillating or sudden change disturbance modes, thereby improving the ability to predict potential abnormalities or performance degradation of equipment.

[0018] 3) By combining the physical connection relationship and topology between power equipment, the coupling relationship of operational disturbances between equipment is included in the analysis scope. The degree of mutual influence between equipment is quantified by the interference intensity value, realizing the extension from "single equipment monitoring" to "system-level collaborative perception". This is conducive to identifying key transmission paths and highly sensitive nodes, and effectively supports the location of power grid disturbance sources and the risk management of chain faults.

[0019] 4) By integrating the equipment disturbance evolution trajectory with the disturbance intensity value and incorporating historical aging data for correlation analysis, the actual impact of aging factors on disturbance evolution can be dynamically identified. This allows for the execution of corrective calculations of the disturbance response, generating optimized disturbance data that more closely approximates the actual operating state. This not only improves the accuracy of disturbance analysis but also provides a basis for decision-making regarding equipment maintenance strategy optimization and aging compensation scheduling. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the steps of a method for processing power data;

[0021] Figure 2 This is a schematic diagram of a power data processing system according to the present invention;

[0022] Figure 3 This is a graph showing the disturbance intensity curve.

[0023] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0025] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3 A method for processing power data includes the following steps:

[0028] Step S1: Obtain the operating parameters of multiple target power devices in the power system during the monitoring period, determine the first operating status of the device based on the operating parameters of each device at the first monitoring time, and determine the second operating status of the device based on the operating parameters of each device at the second monitoring time.

[0029] In one embodiment, the operating parameters include, but are not limited to, voltage, current, temperature, frequency, harmonic content, and load rate. The system collects operating data through intelligent sensors deployed on the power equipment, setting the monitoring cycle to 15 minutes. Within this cycle, the sampling frequency is set to once every 10 seconds to ensure the timeliness and completeness of the data. The system sets the first monitoring time as the start time T1 of the current monitoring cycle and the second monitoring time as the end time T2 of the cycle. Taking a target device D1 as an example, its voltage at time T1 is 220V, current is 35A, and temperature is 42°C; at time T2, its voltage is 213V, current is 38A, and temperature is 45°C. The system extracts the operating parameters of the device at times T1 and T2 respectively, forming two sets of state feature vectors, which are recorded as the first operating state and the second operating state, respectively. Preprocessing such as data normalization, outlier removal, and time alignment is performed on the data to ensure the stability of the subsequent processing results.

[0030] In another embodiment, a simulated power distribution scenario is constructed, including 6 transformers and 8 load devices, totaling 14 monitoring objects. The SimPowerSystems simulation software simulates the real operating environment, with a monitoring cycle set to 20 minutes and a sampling frequency of 1 second. During monitoring, the system extracts the operating data of the devices at T1=0 minutes and T2=20 minutes, respectively, and introduces sudden power grid disturbances (such as load-side switching or capacitor switching). For example, load device L3 is operating under light load at time T1, with a current of only 18A, while at time T2, due to a sudden increase in load, its current rises to 48A, and the temperature rise significantly exceeds 10°C. The system identifies such changes and uses its states at the two times as the first and second operating states, respectively, for subsequent disturbance trajectory calculation.

[0031] Step S2: Based on the first operating condition and the second operating condition, calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time, and identify the equipment disturbance evolution trajectory of the equipment disturbance intensity changing with time based on the change range of the monitoring parameters;

[0032] In one embodiment, the system calculates the magnitude of change based on the numerical difference between the first and second operating states, specifically including voltage fluctuations, current change rates, and temperature rise increases. Taking target device D1 as an example, its voltage at times T1 and T2 is 220V and 213V respectively, with a fluctuation range of 7V, accounting for 3.2% of the rated voltage; the current increases from 35A to 38A, an increase of 8.6%; and the temperature rises from 42°C to 45°C, a temperature rise of 3°C. The system sets disturbance weighting factors α=0.4, β=0.3, and γ=0.3, respectively, for the voltage, current, and temperature dimensions, to weight the change values ​​and calculate the disturbance intensity. For example:

[0033]

[0034] The system then plots the disturbance evolution curve with time on the horizontal axis and disturbance intensity on the vertical axis, marking abrupt change points, such as disturbance intensity gradients that increase more than twofold within 10 minutes. The disturbance evolution trajectory is used to identify the dynamic response behavior of different devices in different periods.

[0035] In another embodiment, the curve fitting tool in Matlab is used to interpolate and smooth the time series data of the disturbance intensity to extract the disturbance trend more accurately. Taking device L3 as an example, the voltage, current, and temperature changes collected between T1 and T2 are 12V, 25A, and 11°C, respectively. Based on the disturbance intensity sequence sampled once per minute, the system obtains a total of 20 disturbance intensity points and constructs the disturbance time series:

[0036]

[0037] Calculate the difference in intensity between adjacent disturbances After obtaining the gradient sequence, the system further extracts the maximum gradient, the peak slope position, and the disturbance fallback segment, ultimately forming a disturbance evolution segment containing "rising segment – ​​peak segment – ​​falling segment". The system identifies obvious sudden load response behavior in L3 devices through the disturbance trajectory structure and marks the device as disturbance-sensitive for subsequent interference propagation path analysis and aging response modeling.

[0038] Step S3: Obtain the physical connection relationship of the power equipment in the power system and identify the equipment pairs with direct connection or indirect coupling paths; combine the equipment disturbance evolution trajectory to quantify the degree of mutual influence between each target equipment, and output the interference intensity value between power equipment based on the degree of influence between each equipment pair.

[0039] In one embodiment, the system extracts the physical connection relationships between multiple target power devices based on the power grid master station drawing data and SCADA system topology information. This connection relationship is constructed as a topological adjacency matrix A, where matrix element A(i,j) represents the connection status between device i and device j. Specifically: if there is a direct electrical connection between device i and device j, then A(i,j) = 1; if there is no direct connection between device i and device j but there is intermediate node coupling, then A(i,j) = ε, where 0 < ε < 1 is the coupling factor; if there is no physical path, A(i,j) = 0. Furthermore, the system analyzes the propagation trend of disturbances in the topology structure by combining the disturbance evolution trajectories of each target device generated in step S2. Taking device D1 as an example, between the first monitoring time T1 and the second monitoring time T2, its disturbance intensity S(D1) = 0.0127. The system detects disturbance responses from its neighboring devices D2 and D3, with disturbance intensities S(D2) = 0.0095 and S(D3) = 0.0042, respectively. The system calculates propagation characteristics such as disturbance response delay Δt and amplitude attenuation rate R to form a disturbance propagation characteristic vector, and quantifies the impact on each pair of devices using the following disturbance intensity function:

[0040]

[0041] in, , This is the adjustment coefficient. If If the value is ≥0.7, it is considered a strong interference path; if 0.4≤ <0.7 indicates a moderate interference path; if A value <0.4 indicates a weak interference path. Based on this, the system generates a device interference intensity matrix, providing input for subsequent disturbance correction and optimization.

[0042] In another embodiment, a power grid simulation model containing 10 transformers and several feeders was constructed. A short-term disturbance was applied to device L5 using the PSCAD platform, and the responses of its adjacent nodes L4 and L6 were monitored. Simulation results showed that after the disturbance was applied, the current fluctuation at node L4 decreased by approximately 35%, while that at node L6 decreased by 12%. Based on this, the system determined that L4 and L5 were direct interference channels, and L6 was an indirect coupling response. The system quantified the interference intensity as I(L5→L4) = 0.82 and I(L5→L6) = 0.48, respectively, constructed and output the interference influence matrix, providing a reference for the correction of the disturbance response model.

[0043] Step S4: Obtain historical aging data of power equipment, and determine the correlation between equipment aging behavior and disturbance based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment. Identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

[0044] In one embodiment, the system retrieves historical operation and maintenance data of the target power equipment, including aging indicators such as insulation attenuation curves, number of circuit breaks, temperature rise trends, and overload duration. Taking equipment D2 as an example, the system fits an aging curve using insulation monitoring data from the past three years.

[0045]

[0046] in, For the initial insulation level, For aging rate, The aging index, The duration is specified. Based on the interference intensity value obtained in step S3, the system found that the impact intensity of the D1 disturbance on D2 was 0.72, and that its disturbance response was significantly higher than other segments during the 7th to 12th minute of the monitoring period. This indicates that aging behavior significantly amplified the disturbance response during this time period. The system performed disturbance correction on this time segment, including: introducing an aging correction factor θ=1.15 to the voltage parameter to enhance the characterization of fluctuation risk; increasing the rate of change of the temperature rise curve by 0.3°C / min to simulate the trend of thermal performance degradation; and outputting the D2-corrected disturbance optimization data, whose overall health score decreased from 0.74 to 0.69.

[0047] In another embodiment, the system conducted a disturbance experiment on three circuit breakers of the same model but with different aging levels to simulate the differences in response under short-circuit current impact. The results showed that the newer device B3 had an operating delay of 17ms and a current increase of 4%; the moderately aged device B2 had a delay of 23ms and a current increase of 8%; and the severely aged device B1 had a delay of 30ms and a current increase of 12%. Based on this, the system established a mapping function between disturbance response and aging level, and applied this mapping to the operating device L6 to identify its aging level as ≥75%. The system then implemented a protection parameter adjustment strategy for L6, adjusting the overcurrent protection sensitivity from 110% to 95%, and incorporated the corrected data into the optimized operation database to provide data support for future maintenance plans.

[0048] Preferably, step S1 includes the following steps:

[0049] Step S11: Obtain the operating parameters of multiple target power devices in the power system during the monitoring period;

[0050] Step S12: Normalize the acquired operating parameters, remove outliers, and calibrate the timestamps;

[0051] Step S13: Determine the first operating status of the equipment based on the digital segment data of voltage, current and temperature at the first monitoring time of each device's operating parameters;

[0052] Step S14: Determine the second operating status of the equipment based on the digital segment data of voltage, current and temperature determined at the second monitoring time based on the operating parameters of each device.

[0053] In one embodiment, the system monitors 10 power devices in a 35kV substation, using a multi-channel acquisition module deployed on-site to record three key operating parameters—voltage, current, and temperature—in real time every 30 seconds, with the entire monitoring cycle set to 30 minutes. The raw data generated by the acquisition module is indexed by timestamps and recorded for each device separately. To ensure the effectiveness of subsequent data comparison, in step S12, the system first normalizes the raw data. For example, voltage is compressed and normalized to a rated value of 220V, and temperature is proportionally transformed to a maximum value of 100℃, unifying all parameters to the [0,1] range. Secondly, data points exceeding the reasonable engineering range are removed. If the voltage reading of a device is below 100V or above 300V at a certain moment, it is determined to be abnormal and removed. Furthermore, to ensure the comparability of data between devices, the system aligns based on the master station timestamp and uses interpolation to supplement some delayed records, ensuring that all devices have complete data dimensions at the same time. The system sets the first monitoring time as the start time of the monitoring cycle, T1 = 0 minutes. At this time, it extracts three parameters—voltage, current, and temperature—from each device to form a three-dimensional state vector. For example, for device D5, its parameters at time T1 are: voltage 229V, current 34A, and temperature 41℃, which, after normalization, are (0.96, 0.34, 0.41). The system judges it to be in a "normal" state based on preset operating standards and generates a first operating status identifier. The second monitoring time is set to T2 = 30 minutes. At this time, the three operating parameters of D5 are collected again, resulting in voltage 212V, current 47A, and temperature 52℃, which, after normalization, are (0.87, 0.47, 0.52), showing a significant shift in state characteristics. The system compares the Euclidean distance between the two sets of state vectors and, combined with a temperature rise threshold, determines the device's operating status as "slightly abnormal" and generates a corresponding second operating status identifier.

[0054] In another embodiment, a virtual simulation test environment is constructed to simulate eight node devices in a medium-voltage power distribution system, and periodic disturbance sources are set, such as simulating sudden load increases or bus voltage dips. The system sets the monitoring period to 60 minutes and extracts device operating data at 5-minute intervals. At the first monitoring time T1=10 minutes, the voltage of simulated device L2 is 235V, the current is 25A, and the temperature is 39℃. At the second monitoring time T2=50 minutes, affected by the simulated disturbance, the device voltage drops to 215V, the current rises to 52A, and the temperature rises to 58℃. The system calculates the rate of change of the above two state vectors, quantifies the temperature rise rate, current rise amplitude, and voltage drop depth, and determines the change in its operating state level, transitioning from a steady state to a high-load transition state. In addition, in this embodiment, the system uses a unified structured matrix storage format for the data collected from different devices and uses the state information as input features for subsequent steps.

[0055] Preferably, step S13 includes the following steps:

[0056] Step S131: Extract the preset monitoring start time of the power equipment in the initial stage of operation and determine the corresponding first monitoring time;

[0057] Step S132: Identify the voltage, current and temperature data in the operating parameters collected during the first monitoring time.

[0058] Step S133: Calculate the instantaneous voltage fluctuation amplitude of the voltage data during the first monitoring time, and use the maximum fluctuation amplitude as the voltage response characteristic;

[0059] Step S134: Calculate the average current value of the current data within the first monitoring time and extract its current imbalance factor between each phase line as a current stability index.

[0060] Step S135: Perform sliding window averaging on the temperature data and identify abrupt change points that exceed the temperature change threshold as thermal disturbance response points;

[0061] Step S136: Determine the first operating status of the target equipment at the first monitoring time based on voltage response characteristics, current stability index, and thermal disturbance response point.

[0062] In one embodiment, a target transformer device numbered D3 in a 35kV substation is selected as an example object. The system extracts the preset monitoring start time T0 of the initial operating phase based on the device's start-up signal and operation log records, and sets the first monitoring time as T1, which is the 5th minute after the device starts (T1 = T0 + 5min), to ensure that brief start-up and shutdown impact interference is avoided. The system retrieves continuous operating parameter data of the device within 30 seconds before and after time T1. The extracted voltage data sequence contains 120 points (sampling frequency of 2Hz), and the current data and temperature data have the same number of sampling points. The voltage data correspond to the three phases U_A, U_B, and U_C, respectively, and the current data correspond to I_A, I_B, and I_C. The temperature is the absolute value fed back by a single-channel temperature probe. The system calculates the difference between the maximum and minimum values ​​of each of the three-phase voltage sequences to obtain the instantaneous fluctuation amplitude. For example, the maximum value of phase U_A is 229.5V, the minimum value is 220.2V, and the fluctuation amplitude is 9.3V. The system takes the maximum value as the voltage response characteristic, i.e., the voltage response characteristic of device D3 at the first monitoring time is 9.3V. The system averages the current values ​​of the three phase lines I_A, I_B, and I_C over the entire sequence, obtaining average currents of 32.5A, 33.1A, and 28.4A, respectively. Further calculation of the current imbalance factor yields an imbalance rate of approximately 14.2%. The system uses this as a current stability indicator, initially determining that the device has a phase line load asymmetry problem. The system applies a sliding window algorithm to the temperature data, setting the window size to 20 data points and the step size to 1 point, calculating the average temperature for each window; the system calculates the temperature difference between consecutive windows. The system detects a temperature rise of 2.8°C between windows 15 and 16, exceeding the preset threshold of 2.5°C, therefore marking this point as a thermal disturbance response point. Based on the aforementioned analysis results, the system determined that the voltage fluctuations were large, the current imbalance was significant, and there were abrupt thermal disturbance points. Therefore, the system determined that the D3 device was in a "mild stress state" at the first monitoring moment, generated a corresponding first operating status label, and used it for subsequent evolution trajectory analysis.

[0063] In another embodiment, the system constructs a simulated power distribution network to simulate the coordinated operation of 10 typical load devices and 2 main power supply devices. A simulated transformer device D7 is set to be activated at the simulation start time T0 = 0 minutes, with T1 = 3 minutes as the first monitoring time. At this time, the system introduces a set of predefined input sequences to simulate disturbance sources, such as periodic pulse loads plus instantaneous voltage drops. The data sampling frequency of the system simulation platform is 10Hz, with a total of 300 sampling points collected. Simulation data shows that within the first monitoring time, the U_A voltage channel of D7 experiences three rapid voltage drops, with an instantaneous drop of 15V. The system therefore records the maximum fluctuation as 15V as its voltage response characteristic. The simulation system sets I_A = 28A, I_B = 27.5A, I_C = 33.8A, the average current is 29.8A, and the imbalance factor ΔI = 13.4%. Meanwhile, the temperature data jumped between the 26th and 29th seconds of the simulation period, with a temperature rise rate of 0.12°C / s, ultimately identifying three temperature abrupt change points. Combining indicators such as large abnormal voltage fluctuations, uneven current offsets, and frequent temperature abrupt changes, the system determined that the simulation device D7 was operating in a "strong disturbance response state" at the first monitoring moment, and simultaneously sent this feature vector to the subsequent disturbance intensity analysis module.

[0064] Preferably, step S14 includes the following steps:

[0065] Step S141: Set the monitoring time period corresponding to when the equipment enters the steady state phase as the second monitoring time;

[0066] Step S142: Take the sampling segment of voltage parameters in the second monitoring time as the target evaluation interval, and extract the voltage drop and rise amplitude per unit time as the voltage dynamic stability index.

[0067] Step S143: Extract the rate of change of frequency components in the current parameters, and calculate the harmonic interference response parameters based on the change of the energy ratio of the harmonic frequency band.

[0068] Step S144: Analyze the local variation range of temperature parameters within the second monitoring time, extract the high temperature duration and its temperature rise slope as thermal steady-state characteristic factors;

[0069] Step S145: Integrate voltage dynamic stability index, harmonic interference response parameters, and thermal steady-state characteristic factor as the second operating condition of the target equipment.

[0070] In one embodiment, taking target device D3 as an example, after completing the first operating condition identification, the device enters the load stabilization period. The system records its steady-state operation start time as T2, and sets the data for two consecutive minutes after T2 as the second monitoring time. During this stage, the device is under rated load conditions, which can comprehensively reflect its thermal, electrical, and frequency characteristics. In the second monitoring time, the system selects voltage sampling sequences U_A, U_B, and U_C, with a sampling frequency of 10Hz and a unit time period of 1 second, to analyze the fluctuation characteristics of each phase voltage. Taking U_A as an example, its voltage is as low as 225V within the 1-second interval, rising to 231V, with a total drop and rise amplitude of 6V. Based on this, the system extracts its voltage dynamic stability index as 6V / s. Subsequently, the system applies Fast Fourier Transform (FFT) to the three-phase current signal to extract the frequency domain components, obtaining the main frequency distribution and the energy coefficients of each harmonic. Statistics show that the harmonic energy ratio of device D3 at the 50Hz main frequency is 7.4%, higher than the previous 4.2%. Based on this, the system calculates the frequency change rate as 0.38 and records it as a harmonic interference response parameter. During the temperature feature extraction process, the system performs local extremum analysis on the temperature curve during the second monitoring time. It finds that the temperature remains above 67°C for 35-95 seconds, with a maximum temperature rise rate of 0.28°C / s. The system records the high temperature duration of 60 seconds and the temperature rise slope of 0.28°C / s as thermal steady-state characteristic factors. Finally, the system integrates three indicators: voltage stability of 6V / s, harmonic response of 0.38, and thermal steady-state characteristic of 60s / 0.28°C / s to generate the second operating state of target device D3, which is assigned the value of "moderately stable state" for subsequent disturbance evolution trajectory analysis and state comparison.

[0071] In another embodiment, the system simulates an inductive load device, designated D8, entering a steady-state operation phase, and sets the simulation system's second monitoring point at T3 (the 10th minute of the simulation). The system acquires 120 data points for voltage, current, and temperature parameters at a sampling rate of 1Hz. Specifically, D8 exhibits weak periodic disturbances in the voltage channel, with fluctuations not exceeding 2V per cycle; the system extracts a dynamic stability index of 1.8V / s. The current FFT results show a 12% increase in the third harmonic content, and the system records its harmonic response value as 0.51. Simultaneously, the temperature data shows a 6°C increase between the 70th and 110th seconds of the simulation, maintaining this temperature rise at a slope of 0.15°C / s, with the high temperature lasting for 40 seconds. Finally, the system combines the three feature vectors to form the evaluation criteria for the second operating condition, determining that device D8 is in a "low-interference steady state," providing a benchmark for subsequent interference intensity assessment.

[0072] Preferably, step S2 includes the following steps:

[0073] Step S21: Based on the first operating condition and the second operating condition, calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time;

[0074] Step S22: Calculate the operating disturbance difference of power equipment based on the change amplitude;

[0075] Step S23: Weight the disturbance difference based on the preset disturbance weight factor to generate the power equipment disturbance intensity characterizing the target;

[0076] Step S24: Identify the disturbance evolution trajectory of equipment disturbance intensity over time based on the disturbance intensity of power equipment.

[0077] In one embodiment, after acquiring the first and second operating states of the target power equipment, the system sequentially calculates the numerical differences of various key monitoring parameters and constructs a basic model of disturbance changes accordingly. Taking the target equipment numbered D5 as an example, its voltage dynamic stability at the first monitoring time is 6.2V / s, and at the second time it is 10.7V / s; the current harmonic responses are 0.28 and 0.43, respectively; and the temperature slopes are 0.21°C / s and 0.35°C / s, respectively. Based on this, the system calculates the change amplitudes of the three types of parameters to be 4.5V / s, 0.15, and 0.14°C / s, respectively. Subsequently, the system introduces a disturbance difference weighting model, setting the disturbance weighting factors for voltage, current, and temperature to α=0.4, β=0.35, and γ=0.25, respectively. Substituting the above change values ​​into the disturbance intensity function, the comprehensive disturbance intensity S of the target equipment is calculated using the following formula:

[0078] =0.4×4.5+0.35×0.15+0.25×0.14≈1.8725;

[0079] The obtained disturbance intensity value S characterizes the degree of change in the operating state experienced by the equipment between two monitoring times. The system repeatedly executes this calculation logic over multiple consecutive cycles, gradually extracting the time series of disturbance intensity, and finally plotting the disturbance evolution trajectory curve of equipment D5, marking abrupt change points and plateau periods in the trajectory. Plateau segments typically indicate that the equipment has entered a steady state or a decline period, while abrupt change points characterize problems such as load shocks, uneven aging, or environmental disturbances.

[0080] In another embodiment, the system constructs a multi-source disturbance environment for 14 devices based on simulation experiments, setting each cycle to 5 minutes, and records the voltage, current, and temperature disturbance responses of all devices. During the 3rd to 6th simulation cycles, the voltage fluctuation amplitude of device D9 increased from 3.1V / s to 9.8V / s, the current harmonic variation value increased from 0.12 to 0.51, and the temperature slope increased from 0.09°C / s to 0.38°C / s. The system also introduces weighting factors for weighted fusion, obtaining the disturbance intensity evolution sequence S={0.82,1.34,2.17,2.89}, and uses a sliding window fitting strategy to analyze the trajectory structure, identifying three disturbance modes: "climbing segment – ​​peak segment – ​​plateau segment". The system further matches the trajectory features with a preset template, marking device D9 as entering a "high disturbance activity period," indicating the need for further correlation with device aging risk level determination.

[0081] Preferably, step S21 includes the following steps:

[0082] Step S211: Extract the first numerical parameters corresponding to the voltage, current and temperature of each target device in the first operating state, and simultaneously extract the corresponding second numerical parameters in the second operating state;

[0083] Step S212: Calculate the parameter difference sequence between the first numerical parameter and the second numerical parameter respectively, and generate the voltage difference sequence, current difference sequence and temperature difference sequence;

[0084] Step S213: Perform normalization processing on each difference sequence, and assign preset weighting coefficients according to the disturbance detection sensitivity of voltage parameters, current parameters and temperature parameters;

[0085] Step S214: Calculate the product of the normalized difference sequence and the corresponding weighting coefficients to obtain the voltage disturbance intensity parameter, current response difference parameter and temperature offset amplitude parameter;

[0086] Step S215: Perform weighted merging of voltage disturbance intensity parameters, current response difference parameters, and temperature offset amplitude parameters to generate the change amplitude of monitoring parameters of the device between the first monitoring time and the second monitoring time.

[0087] In one embodiment, the system extracts the average voltage U1=227.5V, the effective current I1=18.3A, and the peak temperature T1=43.2°C from the first operating condition of the device; then it extracts the corresponding parameters U2=235.1V, I2=21.6A, and T2=48.9°C from the second operating condition. The system calculates the differences between each parameter sequentially, obtaining the difference sequence ΔU=7.6V, ΔI=3.3A, and ΔT=5.7°C. To unify the different dimensions, the system normalizes the difference sequence, obtaining normalized results of u'=0.152, i'=0.183, and t'=0.114. To improve the distinguishability of weighted differences in parameter disturbance responses, the system sets the disturbance detection weight coefficients for voltage, current, and temperature to α=0.45, β=0.35, and γ=0.20, respectively. The normalized values ​​are then multiplied by the corresponding weights to obtain the disturbance intensity parameter sequence: voltage disturbance intensity u=0.0684, current response difference i=0.06405, and temperature offset amplitude t=0.0228. Finally, the system weights and fuses the three types of disturbance parameters to calculate the total change amplitude of the device between two time points, S=0.0684+0.06405+0.0228≈0.1553, which serves as the basic indicator for subsequent disturbance intensity estimation and evolution trajectory analysis.

[0088] In another embodiment, the system performs batch monitoring and analysis on a group of 10 load devices operating in parallel. The system collects the operating status data of all devices and unifies the timestamps, synchronously performing difference sequence generation and weighted processing. For device D7, its voltage difference is 5.2V, normalized to 0.104, and weighted to 0.0468; its current difference is 4.5A, normalized to 0.225, and weighted to 0.07875; its temperature difference is 3.9°C, normalized to 0.078, and weighted to 0.0156. The system ultimately merges these values ​​to obtain a change range of 0.14115 for this device, and by comparing this data with historical change models, identifies that D7 is currently showing a slight disturbance tendency and recommends that it be added to the key monitoring sequence.

[0089] Preferably, step S24 includes the following steps:

[0090] Step S241: Extract the time series disturbance intensity set of the power equipment;

[0091] Step S242: Construct a disturbance time series curve with the monitoring time series as the horizontal axis and the disturbance intensity value as the vertical axis, and mark the disturbance abrupt change points in the curve;

[0092] Step S243: Calculate the gradient difference of the perturbation intensity between adjacent periods based on the perturbation time series curve, and generate a perturbation change slope vector sequence;

[0093] Step S244: Integrate the perturbation change slope vector sequence with the dense distribution characteristics of perturbation abrupt change points to identify perturbation evolution type segments, including rising segments, oscillating segments, and rapid change segments in the perturbation evolution process;

[0094] Step S245: Construct a disturbance pattern label sequence based on the disturbance evolution type segment, and output the device disturbance evolution trajectory containing disturbance periodic structure, disturbance trend boundary and disturbance rate characteristics.

[0095] In one embodiment, the system first extracts the time series of disturbance intensity of the target device D5 within a typical monitoring period (12 hours in total, with a sampling frequency of 1Hz), thus obtaining a total disturbance intensity set. The system plotted a complete disturbance curve with time on the horizontal axis and disturbance intensity on the vertical axis. Through sliding window analysis and second-order difference calculations, the system identified 23 strong disturbance abrupt change points and recorded their distribution intervals. Subsequently, the system calculated the rate of change of disturbance intensity between adjacent sampling times, obtaining a slope vector sequence, for example, at time t... 100 To t 200 The slope of the change within the interval increases significantly, jumping from 0.0034 to 0.0172, indicating a rising disturbance segment. At t 300 To t 400 The internal slope fluctuation value is less than 0.0015, but the abrupt change points are densely distributed within 6 seconds; the system identifies this as an oscillating disturbance segment. For example, at t... 500 To t 520 Between these points, the slope abruptly changes to -0.0389, indicating a typical abrupt change in the disturbance segment. Another example is at t... 500 To t 520 Between these points, the slope abruptly changes to -0.0389, indicating a typical abrupt change in disturbance segment. Based on the continuity and boundary characteristics of the segment, the system constructs a disturbance pattern label sequence, ultimately outputting the disturbance evolution trajectory of D5, identifying three main disturbance segments: "gradual rise – slight tremor – sudden drop." It also provides key parameters for each segment, such as start and end times, disturbance slope range, and maximum disturbance rate value, providing data support for subsequent equipment intervention and aging response optimization.

[0096] In another embodiment, to verify the applicability of disturbance evolution trajectory in fault precursor analysis, the system selected a circuit breaker D9 that had experienced abnormal temperature rise alarms in the past 30 days. The system reviewed its historical operating data and constructed a disturbance evolution map, finding that it exhibited a typical disturbance trajectory characteristic of "long, slow rise – oscillating accumulation – sudden drop" in the 12 hours before the fault, and the dense area of ​​abrupt change points highly overlapped with the starting point of the abnormal temperature rise. This confirms that the disturbance evolution trajectory of this device has potential early warning value, which can assist in fault prediction and strategy adjustment, providing reliable model support for intelligent health operation and maintenance systems.

[0097] Preferably, step S3 includes the following steps:

[0098] Step S31: Obtain the physical connection relationship of the power equipment in the power system, construct the topology of each target power equipment, and identify the device pairs with direct connection or indirect coupling paths;

[0099] Step S32: Combine the equipment disturbance evolution trajectory to analyze the transmission path and direction of equipment disturbance in the topology, identify the propagation link of parameter disturbance and the trend of influence diffusion, and form a feature vector of disturbance transmission effect;

[0100] Step S33: Based on the connection strength and disturbance propagation effect feature vectors between nodes in the topology, quantify the degree of mutual influence between target devices;

[0101] Step S34: Output the interference intensity value between power equipment based on the degree of influence between each pair of equipment.

[0102] In one embodiment, the system first collects the physical connection information of each target power device in the power substation, such as the wiring relationship between transformer T1 and circuit breaker B1 and bus L2, and constructs a node topology diagram of the power devices, treating each device as a node and expressing the connection relationship in the form of edges, thus generating a structured device network. The system further identifies direct connection pairs (such as T1-B1) and device pairs with indirect coupling paths (such as T1 and the remote load F1 coupled through L2 and B3). Combining the disturbance evolution trajectory output in step S2 above, the system tracks the disturbance origin and its propagation path for each device, and performs vector modeling of the disturbance propagation direction on the path in the topology. For example, when device T1 experiences a disturbance rise segment at time t1, the system identifies, through topology chain analysis, that the disturbance propagates from T1 through B1 and L2 to B3, with a delay of approximately 0.35 seconds for the disturbance to propagate to F1. The system constructs the disturbance transmission effect vector of this disturbance path. Where ΔS_T1 is the disturbance amplitude, τ is the propagation delay, and γ is the attenuation factor. Next, the system comprehensively evaluates the degree of mutual interference between devices based on the connection strength (such as impedance Z and coupling conductance G) and disturbance propagation characteristics along each path. For example, if ΔS_T1 is large and γ is close to 1, it indicates high disturbance propagation fidelity, and the interference intensity of T1 on F1 is large; conversely, if there are multiple buffer nodes in the path or the connection strength in the topology is low, the interference value is correspondingly smaller. Finally, the system forms an interference intensity matrix per device pair, for example, T1→F1 is 0.73, T1→B3 is 0.58, and T1→T2 is 0.11, serving as the basis for subsequent coordination analysis between device groups.

[0103] In another embodiment, to verify the rationality of the aforementioned interference intensity values, a 10-node simulated power topology network was constructed. A thermal disturbance was simulated on device D3 during operation, with the disturbance intensity increasing from 0 to 0.87. The system tracked the propagation path of the disturbance in the topology in real time and observed the operational responses of adjacent devices D4 and D5. In the experiment, after receiving the disturbance, D4's current parameter fluctuated and increased by 0.3A within 3 seconds, and its temperature increased by 2.1℃; while D5 remained relatively stable. The system calculated the interference intensity values ​​for D3→D4 to be 0.62, and for D3→D5 to be only 0.07, verifying that the interference path and intensity identified by the system are highly consistent with actual operational performance, demonstrating effectiveness and engineering adaptability.

[0104] Please see Figure 3 The present invention also provides a power data processing system for performing the power data processing method described above, the power data processing system comprising:

[0105] The operation status monitoring module 101 is used to acquire the operation parameters of multiple target power devices in the power system during the monitoring period, determine the first operation status of the device based on the operation parameters of each device at the first monitoring time, and determine the second operation status of the device based on the operation parameters of each device at the second monitoring time.

[0106] The disturbance evolution trajectory identification module 102 is used to calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time according to the first operating condition and the second operating condition, and to identify the equipment disturbance intensity change over time based on the change range of the monitoring parameters.

[0107] The interference intensity monitoring module 103 is used to acquire the physical connection relationship of the power equipment in the power system and identify the equipment pairs with direct connection or indirect coupling paths; combined with the equipment disturbance evolution trajectory, it quantifies the degree of mutual influence between each target equipment and outputs the interference intensity value between the power equipment based on the degree of influence between each equipment pair.

[0108] The disturbance response correction module 104 is used to acquire historical aging data of power equipment, and based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment, determine the correlation between equipment aging behavior and disturbance, identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

[0109] A computer-readable storage medium storing a computer program, wherein the computer program is used to perform the method for processing the power data.

[0110] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for processing power data, characterized in that, Includes the following steps: Step S1: Obtain the operating parameters of multiple target power devices in the power system during the monitoring period, determine the first operating status of the device based on the operating parameters of each device at the first monitoring time, and determine the second operating status of the device based on the operating parameters of each device at the second monitoring time. Step S2: Based on the first operating condition and the second operating condition, calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time, and identify the equipment disturbance evolution trajectory of the equipment disturbance intensity changing with time based on the change range of the monitoring parameters; Step S3: Obtain the physical connection relationship of the power equipment in the power system and identify the equipment pairs with direct connection or indirect coupling paths; combine the equipment disturbance evolution trajectory to quantify the degree of mutual influence between each target equipment, and output the interference intensity value between power equipment based on the degree of influence between each equipment pair. Step S4: Obtain historical aging data of power equipment, and determine the correlation between equipment aging behavior and disturbance based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment. Identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

2. The method for processing power data according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the operating parameters of multiple target power devices in the power system during the monitoring period; Step S12: Normalize the acquired operating parameters, remove outliers, and calibrate the timestamps; Step S13: Determine the first operating status of the equipment based on the digital segment data of voltage, current and temperature at the first monitoring time of each device's operating parameters; Step S14: Determine the second operating status of the equipment based on the digital segment data of voltage, current and temperature determined at the second monitoring time based on the operating parameters of each device.

3. The method for processing power data according to claim 2, characterized in that, Step S13 includes the following steps: Step S131: Extract the preset monitoring start time of the power equipment in the initial stage of operation and determine the corresponding first monitoring time; Step S132: Identify the voltage, current and temperature data in the operating parameters collected during the first monitoring time. Step S133: Calculate the instantaneous voltage fluctuation amplitude of the voltage data during the first monitoring time, and use the maximum fluctuation amplitude as the voltage response characteristic; Step S134: Calculate the average current value of the current data within the first monitoring time and extract its current imbalance factor between each phase line as a current stability index. Step S135: Perform sliding window averaging on the temperature data and identify abrupt change points that exceed the temperature change threshold as thermal disturbance response points; Step S136: Determine the first operating status of the target equipment at the first monitoring time based on voltage response characteristics, current stability index, and thermal disturbance response point.

4. The method for processing power data according to claim 2, characterized in that, Step S14 includes the following steps: Step S141: Set the monitoring time period corresponding to when the equipment enters the steady state phase as the second monitoring time; Step S142: Take the sampling segment of voltage parameters in the second monitoring time as the target evaluation interval, and extract the voltage drop and rise amplitude per unit time as the voltage dynamic stability index. Step S143: Extract the rate of change of frequency components in the current parameters, and calculate the harmonic interference response parameters based on the change of the energy ratio of the harmonic frequency band. Step S144: Analyze the local variation range of temperature parameters within the second monitoring time, extract the high temperature duration and its temperature rise slope as thermal steady-state characteristic factors; Step S145: Integrate voltage dynamic stability index, harmonic interference response parameters, and thermal steady-state characteristic factor as the second operating condition of the target equipment.

5. The method for processing power data according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Based on the first operating condition and the second operating condition, calculate the change range of the monitoring parameters of the equipment between the first monitoring time and the second monitoring time; Step S22: Calculate the operating disturbance difference of power equipment based on the change amplitude; Step S23: Weight the disturbance difference based on the preset disturbance weight factor to generate the power equipment disturbance intensity characterizing the target; Step S24: Identify the disturbance evolution trajectory of equipment disturbance intensity over time based on the disturbance intensity of power equipment.

6. The method for processing power data according to claim 5, characterized in that, Step S21 includes the following steps: Step S211: Extract the first numerical parameters corresponding to the voltage, current and temperature of each target device in the first operating state, and simultaneously extract the corresponding second numerical parameters in the second operating state; Step S212: Calculate the parameter difference sequence between the first numerical parameter and the second numerical parameter respectively, and generate the voltage difference sequence, current difference sequence and temperature difference sequence; Step S213: Perform normalization processing on each difference sequence, and assign preset weighting coefficients according to the disturbance detection sensitivity of voltage parameters, current parameters and temperature parameters; Step S214: Calculate the product of the normalized difference sequence and the corresponding weighting coefficients to obtain the voltage disturbance intensity parameter, current response difference parameter and temperature offset amplitude parameter; Step S215: Perform weighted merging of voltage disturbance intensity parameters, current response difference parameters, and temperature offset amplitude parameters to generate the change amplitude of monitoring parameters of the device between the first monitoring time and the second monitoring time.

7. The method for processing power data according to claim 5, characterized in that, Step S24 includes the following steps: Step S241: Extract the time series disturbance intensity set of the power equipment; Step S242: Construct a disturbance time series curve with the monitoring time series as the horizontal axis and the disturbance intensity value as the vertical axis, and mark the disturbance abrupt change points in the curve; Step S243: Calculate the gradient difference of the perturbation intensity between adjacent periods based on the perturbation time series curve, and generate a perturbation change slope vector sequence; Step S244: Integrate the perturbation change slope vector sequence with the dense distribution characteristics of perturbation abrupt change points to identify perturbation evolution type segments, including rising segments, oscillating segments, and rapid change segments in the perturbation evolution process; Step S245: Construct a disturbance pattern label sequence based on the disturbance evolution type segment, and output the device disturbance evolution trajectory containing disturbance periodic structure, disturbance trend boundary and disturbance rate characteristics.

8. The method for processing power data according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the physical connection relationship of the power equipment in the power system, construct the topology of each target power equipment, and identify the device pairs with direct connection or indirect coupling paths; Step S32: Combine the equipment disturbance evolution trajectory to analyze the transmission path and direction of equipment disturbance in the topology, identify the propagation link of parameter disturbance and the trend of influence diffusion, and form a feature vector of disturbance transmission effect; Step S33: Based on the connection strength and disturbance propagation effect feature vectors between nodes in the topology, quantify the degree of mutual influence between target devices; Step S34: Output the interference intensity value between power equipment based on the degree of influence between each pair of equipment.

9. A power data processing system, characterized in that, The power data processing system for performing the power data processing method as described in claim 1 includes: The operation status monitoring module is used to acquire the operating parameters of multiple target power devices in the power system during the monitoring period, determine the first operating status of the device based on the operating parameters of each device at the first monitoring time, and determine the second operating status of the device based on the operating parameters of each device at the second monitoring time. The disturbance evolution trajectory identification module is used to calculate the change range of monitoring parameters of the equipment between the first monitoring time and the second monitoring time according to the first operating condition and the second operating condition, and to identify the equipment disturbance intensity change trajectory over time based on the change range of monitoring parameters. The interference intensity monitoring module is used to obtain the physical connection relationship of power equipment in the power system and identify equipment pairs with direct connection or indirect coupling paths; combined with the equipment disturbance evolution trajectory, it quantifies the degree of mutual influence between each target equipment and outputs the interference intensity value between power equipment based on the degree of influence between each equipment pair. The disturbance response correction module is used to acquire historical aging data of power equipment, and based on the equipment disturbance evolution trajectory and the interference intensity value between power equipment, determine the correlation between equipment aging behavior and disturbance, identify the change segment of equipment aging factors during the disturbance process according to the correlation, perform disturbance response correction, and output power equipment disturbance optimization operation data.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the power data processing method as described in any one of claims 1 to 8.

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