A method and system for monitoring and comprehensively evaluating power quality in power distribution networks

Abstract

This invention belongs to the field of power generation and supply technology, specifically relating to a method and system for monitoring and comprehensively evaluating the power quality of distribution networks. The method includes: obtaining environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order; calculating the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level; calculating the harmonic erosion index, which characterizes the complexity of the harmonic spectrum on the load side; calculating the thermal harmonic coupling degree used to determine the cause of high temperatures; calculating the grid health score; classifying the grid operating status into levels; determining the fault type; and issuing adjustment commands for different fault types. This invention effectively solves the technical problem that existing technologies cannot assess the dynamic coupling relationship between harmonic morphology and equipment thermal state.

Description

Technical Field

[0001] This invention relates to the field of power generation and supply technology. More specifically, this invention relates to a method and system for monitoring and comprehensively evaluating the power quality of a power distribution network. Background Technology

[0002] With the rapid development of new power systems, distribution networks are undergoing profound changes. The large-scale integration of numerous nonlinear loads, such as variable frequency air conditioners, electric vehicle charging stations, and distributed power sources, is making the current composition in the power grid increasingly complex. These devices inject a large number of high-order harmonics into the grid during operation, causing severe voltage waveform distortion and directly threatening the safe and stable operation of power equipment. Currently, traditional power quality monitoring methods mainly rely on monitoring terminals sampling single indicators such as transformer voltage distortion rate and making simple binary judgments of pass or fail based on static thresholds set by national standards. While this method can reflect the overall level of power quality to some extent, it is difficult to meet the requirements of refined management when dealing with complex nonlinear load scenarios.

[0003] However, this static threshold-based judgment method has technical flaws. It mechanically severs the deep connection between power quality electrical indicators and the physical health status of power distribution equipment, failing to reveal the dynamic coupling mechanism between electricity and heat. Specifically, for the same amplitude current, if it contains high-frequency harmonic components, the nonlinear amplification effect of the skin effect will cause the transformer windings to generate additional losses and temperature rises far exceeding those of the power frequency current. Conversely, if the transformer's own heat dissipation system, such as fans or oil circuits, malfunctions, even if the harmonic level of the current flowing through it is low, the equipment will exhibit abnormal thermal conditions. Existing monitoring technologies lack an effective mathematical model for evaluation. The depth of dynamic coupling between harmonic spectrum morphology and equipment thermal state leads to decision-making dilemmas for operation and maintenance systems. When transformer overheating is detected, it is impossible to accurately distinguish whether it is due to harmonic erosion from external loads or to a decline in heat dissipation capacity caused by internal aging of the equipment. Summary of the Invention

[0004] To address the technical problem that the existing technology cannot assess the dynamic coupling relationship between harmonic morphology and equipment thermal state, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a method for monitoring and comprehensively evaluating the power quality of a power distribution network, comprising:

[0006] Data acquisition and spectral feature extraction are performed by intelligent monitoring terminals installed on the low-voltage side of distribution transformers to obtain environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order. Based on the three-phase voltage and current data, the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, are calculated. Based on the enhanced effect of higher-frequency harmonics on the conductor skin effect and the rated current in the environmental reference parameters, the harmonic erosion index, which characterizes the complexity of the load-side harmonic spectrum, is calculated. Based on the difference in the impact of transformer harmonic current fluctuations and system faults on heat changes, and combined with the harmonic erosion index, the thermal harmonic coupling degree, used to determine the cause of high temperatures, is calculated. Based on the relationship between electrical environmental conditions and heat imbalance on the stable operation of the distribution network, and using the thermal harmonic coupling degree and harmonic erosion index, a grid health score is calculated. The grid operating status is classified according to the grid health score. The harmonic erosion index and thermal harmonic coupling degree corresponding to high-risk grid operating statuses are extracted to determine the fault type. Adjustment commands are issued for different fault types, and a visual assessment report is generated.

[0007] This invention achieves a comprehensive assessment of the power grid's operational status through multi-dimensional data collection and step-by-step calculation of multiple indicators. It accurately distinguishes between equipment anomalies caused by different reasons, avoiding previous misjudgments or biased assessments, and providing precise evidence for fault handling. Simultaneously, through power grid health scoring and status level classification, staff can clearly understand the power grid's operational status. Combined with fault type identification, this helps to quickly implement targeted measures, effectively improving the stability of power grid operation, reducing the impact of faults, and meeting the needs of refined management.

[0008] Preferably, obtaining environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order includes:

[0009] The intelligent monitoring terminal installed on the low-voltage side of the distribution transformer collects synchronous three-phase voltage data, three-phase current data, and top oil temperature data in real time at a fixed frequency. Environmental reference parameters are obtained from the database, the three-phase current data is windowed and truncated, and the fundamental current at the fundamental frequency is analyzed using fast Fourier transform and denoted as the transformer fundamental current. The harmonic currents from the 2nd to the preset cutoff number N and their corresponding harmonic numbers are denoted as the transformer harmonic current and the transformer harmonic current number.

[0010] Preferably, the calculation of the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, includes:

[0011] A fast Fourier transform is performed on the three-phase voltage data to extract the transformer fundamental voltage, transformer harmonic voltage, and the order of the transformer harmonic voltage. The square root of the sum of squares of the transformer harmonic voltages is calculated and compared with the transformer fundamental voltage to obtain the transformer voltage distortion rate. At the same time, the square of the transformer fundamental current and the sum of squares of all transformer harmonic currents are calculated, and the square root of the sum of squares is performed to obtain the total current flowing through the transformer, which is denoted as the transformer total current.

[0012] Preferably, the harmonic erosion index satisfies the following expression:

[0013] ;

[0014] In the formula, The harmonic erosion index is a dimensionless value. This represents the nth transformer harmonic current; Indicates the rated current; Indicates the order of transformer harmonic current; Indicates the preset maximum harmonic analysis number; Represents the natural logarithm function; It is a natural constant; It is a very small positive number, and the denominator is guaranteed to be non-zero.

[0015] The harmonic erosion index calculation in this invention comprehensively reflects the impact of factors related to different frequencies on equipment, avoiding a focus solely on numerical values ​​while ignoring frequency differences, thus making the assessment more realistic. The calculation method for the harmonic erosion index is simple, and the results clearly demonstrate the combined influence of relevant factors, providing strong support for determining the cause of temperature anomalies and assessing equipment operating status. It helps staff fully understand potential influencing factors, providing a reference for subsequent targeted measures and improving the comprehensiveness and practicality of the assessment.

[0016] Preferably, the thermal harmonic coupling degree satisfies the following expression:

[0017] ;

[0018] In the formula, It represents the degree of thermal harmonic coupling and is a dimensionless numerical value. This indicates the top oil temperature data; Kelvin constant; Indicates the average annual temperature; Indicates the rated temperature rise; Indicates the total current of the transformer; Indicates the rated current; Indicates the harmonic erosion index; This represents an exponential function with the natural constant as its base.

[0019] This invention can effectively distinguish temperature anomalies caused by different reasons by calculating the thermal harmonic coupling degree, solving the problem of difficulty in determining the root cause of temperature rise in the past. The index calculation combines multiple related factors, and the results can accurately reflect the relationship between the thermal state of the equipment and electrical factors, allowing staff to clearly understand the specific reasons for the temperature anomaly.

[0020] Preferably, calculating the power grid health score includes:

[0021] Retrieve thermal risk penalty scores, which characterize the transformer insulation class, from the database;

[0022] ;

[0023] In the formula, This represents the power grid health score, with a value range of (0,1]. Indicates the harmonic erosion index; Indicates the voltage distortion rate of the transformer; Indicates the degree of thermal harmonic coupling; The score is for thermal risk penalty.

[0024] The power grid health score in this invention integrates multiple key factors to comprehensively reflect the overall health status of the power grid, avoiding the limitations of single-indicator assessments. The score results are intuitive and easy to understand, allowing staff to quickly grasp the power grid's operational status. The calculation logic aligns with actual operating patterns, exhibiting reasonable tolerance for minor fluctuations while promptly identifying serious anomalies, avoiding both oversensitivity and ignoring critical issues. This provides a clear basis for subsequent status level classification and fault diagnosis, helping staff conduct maintenance work efficiently and improving management rationality.

[0025] Preferably, the power grid operating status is classified into levels, including:

[0026] A first judgment threshold and a second judgment threshold are set. If the power grid health score is greater than or equal to the first judgment threshold, the power grid operation status is determined to be healthy and displayed in green on the monitoring terminal. If the power grid health score is less than the first judgment threshold but greater than or equal to the second judgment threshold, the power grid operation status is determined to be sub-healthy and displayed in yellow on the monitoring terminal. If the power grid health score is less than the second judgment threshold, the power grid operation status is determined to be high-risk and displayed in red on the monitoring terminal, triggering an audible and visual alarm.

[0027] This invention transforms complex scoring data into intuitive status categories through a grading system, reducing the difficulty of information interpretation and facilitating rapid identification of power grid operation by staff. The corresponding identifiers and alarm methods for different grades promptly alert staff to anomalies, preventing the overlooking of critical issues. The grading standards are based on practical operational experience, aligning with management needs and helping staff rationally allocate maintenance resources, focusing their efforts on critical situations and improving operational efficiency and focus.

[0028] Preferably, determining the fault type includes:

[0029] When the system determines that the power grid is in a high-risk state, it automatically extracts the corresponding harmonic erosion index and thermal harmonic coupling degree, and obtains the preset harmonic anomaly threshold. If the harmonic erosion index is greater than the harmonic anomaly threshold, the power grid is determined to be in a load pollution type fault. If the thermal harmonic coupling degree is greater than the theoretical reference value of 1, and the harmonic erosion index is lower than or equal to the harmonic anomaly threshold, the power grid is determined to be in a transformer cooling system anomaly.

[0030] Preferably, adjustment instructions are issued for different fault types, including:

[0031] For load pollution type faults, the system issues harmonic compensation gain adjustment instructions to the grid-connected SVG; for transformer heat dissipation system abnormalities, the system sends load transfer suggestions to the dispatch center. At the same time, the changes in grid health score, the identified fault types and the control instructions taken during the assessment period are packaged to generate a visual assessment report.

[0032] Secondly, the present invention provides a power quality monitoring and comprehensive evaluation system for a power distribution network, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned power quality monitoring and comprehensive evaluation method for a power distribution network is implemented.

[0033] By adopting the above technical solution, a computer program for the above-mentioned power quality monitoring and comprehensive evaluation method of power distribution network is generated and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment can then be made based on the memory and processor for convenient use.

[0034] The beneficial effects of this invention are as follows: Through standardized processes and reasonable indicator calculations, it simplifies complex monitoring and evaluation work, reduces blind operations during operation and maintenance, and allows staff to allocate resources and handle problems more efficiently, saving time and manpower costs. The evaluation results are accurate and reliable, and the generation of reports and instructions helps form a complete management loop, improving the reliability of the current power grid operation and providing valuable references for subsequent equipment maintenance and management strategy optimization. The overall solution is highly practical, easy to operate, and adaptable to different operating scenarios, operating stably without complex procedures. It provides strong support for the stable operation of the distribution network and meets various needs in practical applications. Attached Figure Description

[0035] Figure 1 This diagram illustrates a flowchart of a power quality monitoring and comprehensive evaluation method for power distribution networks according to the present invention.

[0036] Figure 2 This diagram illustrates the sensitivity analysis of health scores based on baseline deviation in this invention.

[0037] Figure 3 The diagram illustrates the mapping between fault tracing logic and control strategy in this invention. Detailed Implementation

[0038] This invention discloses a method for monitoring and comprehensively evaluating the power quality of a distribution network, referring to... Figure 1 This includes steps S1-S4:

[0039] S1: The intelligent monitoring terminal installed on the low-voltage side of the distribution transformer performs data acquisition and spectrum feature extraction to obtain environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current and transformer harmonic current order.

[0040] It is important to note that in complex power distribution network environments, synchronous data acquisition is a prerequisite for all subsequent analyses. Traditional monitoring often collects electrical quantities (voltage and current) and physical quantities (temperature) separately. The misalignment of timestamps makes it impossible to accurately establish the causal relationship between the current spectrum and instantaneous temperature rise. For example, a short-lived harmonic surge may only manifest as a temperature rise tens of seconds later. Without high-frequency synchronous data, the system may easily misinterpret this temperature rise as an ambient temperature change or a heat dissipation failure. Furthermore, the rated parameters of a transformer are inherent attributes for assessing its health status, while ambient temperature is an external benchmark for assessing its thermal state. From the perspective of data validity, power quality data lacking rated parameters and environmental benchmarks cannot support subsequent in-depth analysis. Therefore, this invention constructs a multi-dimensional physical and electrical state space. Through high-frequency sampling combined with FFT transformation, we deconstruct the waveform signal in the time domain into clearly defined fundamental and harmonic components in the frequency domain. Only through this refined spectral decomposition can we capture those high-frequency harmonic components hidden deep within the current waveform that are destructive to equipment insulation, providing accurate data support for subsequent calculations of the harmonic erosion index.

[0041] Specifically, the intelligent monitoring terminal installed on the low-voltage side of the distribution transformer performs data acquisition and spectral feature extraction to obtain environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order, including:

[0042] The intelligent monitoring terminal installed on the low-voltage side of the distribution transformer collects synchronous three-phase voltage data, three-phase current data, and top oil temperature data in real time at a fixed frequency. The environmental reference parameters of the distribution transformer, including rated current, rated temperature rise, and annual average temperature, are obtained from the database. The three-phase current data is windowed and truncated, and the fundamental current at the fundamental frequency is analyzed using fast Fourier transform and denoted as the transformer fundamental current. The harmonic currents from the 2nd to the preset cutoff number N and their corresponding harmonic numbers are denoted as the transformer harmonic current and the transformer harmonic current number.

[0043] Thus, the environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order of the distribution transformer were obtained.

[0044] S2: Based on three-phase voltage and three-phase current data, calculate the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level; based on the enhanced effect of higher frequency harmonics on the conductor skin effect and the rated current in the environmental reference parameters, calculate the harmonic erosion index, which characterizes the complexity of the harmonic spectrum on the load side; based on the difference in the impact of transformer harmonic current fluctuations and system faults on heat changes, and combined with the harmonic erosion index, calculate the thermal harmonic coupling degree used to determine the cause of high temperature.

[0045] It should be noted that after obtaining fine-grained spectral data, macroscopic statistical indicators are still needed to characterize the current power quality level. Transformer voltage distortion rate is an evaluation parameter measuring the purity of the voltage waveform, reflecting the power quality provided by the grid. Total current, on the other hand, is a numerical basis for measuring the transformer load pressure, reflecting the electricity consumption scale on the user side. In real-world scenarios, there may be situations where the transformer fundamental current is very small but the distortion rate is very high (i.e., light load harmonic pollution), or vice versa (i.e., heavy load linear load). Without calculating these two macroscopic indicators, subsequent evaluations will lack a normalized benchmark. For example, a transformer harmonic current of 5A may account for as much as 50% when the total current is 10A, constituting severe pollution; however, when the total current is 1000A, it accounts for only 0.5%, which is almost negligible. Therefore, this invention re-aggregates microscopic spectral components into macroscopic statistical features, providing necessary background parameters for constructing a penalty model, ensuring that the evaluation results cover both local details and reflect the overall situation.

[0046] Specifically, based on three-phase voltage and three-phase current data, the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, are calculated, including:

[0047] A fast Fourier transform is performed on the three-phase voltage data to extract the transformer fundamental voltage, transformer harmonic voltage, and the order of the transformer harmonic voltage. The square root of the sum of squares of the transformer harmonic voltages is calculated and compared with the transformer fundamental voltage to obtain the transformer voltage distortion rate. At the same time, the square of the transformer fundamental current and the sum of squares of all transformer harmonic currents are calculated, and the square root of the sum of squares is performed to obtain the total current flowing through the transformer, which is denoted as the transformer total current.

[0048] Thus, the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, were obtained.

[0049] It should be noted that in today's widespread use of power electronic equipment, simple superposition of current amplitudes is insufficient to describe the true impact of harmonics on equipment. In transformer physics, the skin effect causes high-frequency currents to tend to flow on the conductor surface, resulting in a reduced effective cross-sectional area and increased resistance, thus generating heat far exceeding that calculated from DC resistance. Without frequency weighting, a 10A 5th harmonic and a 10A 25th harmonic are numerically equal, but the latter may generate several times more heat loss. Without a unified standard of measurement, automation systems will be unable to distinguish the severity levels of these two situations. Therefore, this invention constructs a harmonic erosion index, using the harmonic order as a key weighting factor to dynamically modulate the harmonic current amplitude. By simulating the nonlinear enhancement effect of the skin effect through the growth characteristics of the natural logarithm function, a comprehensive index that reflects both harmonic amplitude and frequency characteristics is extracted, making this index directly correlated with the actual process impacts, namely equipment heating and insulation aging.

[0050] Preferably, based on the enhancing effect of higher frequency harmonics on the conductor's skin effect, and using the rated current in the environmental reference parameters, a harmonic erosion index characterizing the complexity of the load-side harmonic spectrum is calculated, including:

[0051] The harmonic erosion index satisfies the following expression:

[0052] ;

[0053] In the formula, The harmonic erosion index is a dimensionless value. This represents the nth transformer harmonic current; Indicates the rated current; Indicates the order of transformer harmonic current; Indicates the preset maximum harmonic analysis number; Represents the natural logarithm function; It is a natural constant; It is a very small positive number, and the denominator is guaranteed to be non-zero.

[0054] In the formula, This represents the ratio of the nth harmonic current to the rated current. This structure uses the constant rated capacity of the equipment as the absolute reference benchmark, eliminating the defect of abnormal amplification of the ratio due to the extremely small fundamental current under light load conditions, and truly reflecting the relative strength of the absolute erosion capability of the harmonic. As a frequency weighting factor, the growth characteristics of the natural logarithm function are utilized to simulate the increasing trend of the skin effect with increasing frequency, i.e., the harmonic order. The higher the value, the greater the additional loss weight it generates, and The introduction of this ensures that the weighting factor is always greater than 1; By summing the weighted effects of all subharmonics, thus making It can comprehensively characterize the spectral complexity and potential thermal erosion capability of harmonic components in load current.

[0055] For example, if Scenario 1: The 5th harmonic exists. ,but ,Right now Scenario 2: The 25th harmonic exists. ,but ,Right now It is evident that although the harmonic current amplitudes are the same, the 25th harmonic, with its higher frequency, generates a larger harmonic erosion index, consistent with the objective fact that higher-frequency harmonics pose a greater threat. The above calculation results... , Round to three decimal places. , Round to two decimal places.

[0056] It should be noted that this invention uses rated current, rather than real-time fundamental current, as the normalization denominator when calculating the relative harmonic content. This design overcomes the mathematical singularity problem caused by the real-time fundamental current approaching zero under extremely light load or no-load conditions, leading to an infinitely amplified and drastically fluctuating calculated ratio. Using a constant rated capacity of the equipment as the absolute reference ensures that the extracted harmonic erosion index stably and accurately reflects the degree of harmonic erosion across the entire load range, avoiding false alarms caused by drastic load fluctuations during periods of low grid power consumption.

[0057] Thus, the harmonic erosion index, which characterizes the complexity of the harmonic spectrum on the load side, was obtained.

[0058] It's important to note that thermal fault diagnosis in distribution transformers has long faced the challenge of identifying multi-factor coupling issues. In production sites, both thermal anomalies caused by transformer aging or heat dissipation failure and those caused by high-energy-consuming loads (corresponding to high-harmonic loads) manifest as temperature increases. Simply looking at absolute temperature is insufficient to determine whether the problem stems from equipment performance degradation or excessive load. From a thermodynamic perspective, transformers should have a theoretical reference temperature under specific load and ambient temperature. Deviations from this theoretical value indicate an anomaly. Therefore, we need to construct a thermal-harmonic coupling index. This model not only calculates temperature differences but also establishes a dynamic reference surface that fluctuates in real-time with ambient temperature and load rate. Furthermore, incorporating the harmonic erosion index using an exponential function allows analysis of the harmonic contribution to abnormal heating. This comparison enables precise differentiation between overheating caused by harmonics (i.e., an increase in thermal-harmonic coupling due to the harmonic erosion index in the molecule) and overheating caused by a heat dissipation system malfunction. This solves the challenge of determining the cause of high-temperature alarms in automated systems.

[0059] Preferably, based on the difference in the impact of transformer harmonic current fluctuations and system faults on heat changes, and combined with the harmonic erosion index, the thermal harmonic coupling degree used to determine the cause of high temperature is calculated, including:

[0060] The thermal harmonic coupling degree satisfies the following expression:

[0061] ;

[0062] In the formula, It represents the degree of thermal harmonic coupling and is a dimensionless numerical value. This indicates the top oil temperature data; Kelvin constant; Indicates the average annual temperature; Indicates the rated temperature rise; Indicates the total current of the transformer; Indicates the rated current; Indicates the harmonic erosion index; This represents an exponential function with the natural constant as its base.

[0063] In the formula, the molecule The measured absolute thermodynamic temperature; the denominator is... Represents the current load rate Under these conditions, a transformer in a healthy state should possess a theoretical thermodynamic absolute temperature; this denominator structure solves the judgment problem under low load: when At this point, the denominator approaches the ambient absolute temperature and is greater than 0. If the top oil temperature data is also close to the ambient temperature, then... for If the top oil temperature data is significantly higher than the theoretical value, it is considered normal; otherwise, it is considered normal. This indicates that a malfunction or abnormal emergency has occurred. Contribution weights used to further amplify harmonic deviations.

[0064] For example, setting , , The Kelvin constant is 273.15. Scenario A exists, under normal no-load conditions: , , , The condition is determined to be completely normal; however, scenario B exists, under normal full load conditions: , , , The condition is determined to be completely normal; however, scenario C exists, where the transformer experiences a pure heat dissipation fault. , , , , A value greater than 1 accurately identified all anomalies; scenario D exists, namely, a sudden thermal harmonic fault in the transformer. , , , The result is greater than 1, accurately identifying the thermal risk caused by harmonics. and Round to two decimal places.

[0065] It should be noted that during winter cold waves in East China, nighttime ambient temperatures will drop to [a certain level]. Even below zero, during sensor calibration or system cold start in industrial settings, the system is extremely prone to collecting data. The critical value. If the underlying monitoring system directly performs ratio calculations based on Celsius, once the denominator exceeds the critical value. The entire calculation program will face an abnormal state of division by zero, causing the system to crash. This invention introduces... An absolute thermodynamic reference surface that is always positive was constructed. Although this sacrificed extremely small sensitivity, it solved the problem of the influence of winter. The risk of algorithm crashes due to temperature variations gives the monitoring terminal a high degree of system stability.

[0066] It should be noted that during the high temperatures of summer, such as those common in the Yangtze River Delta region... Using the annual average temperature as a benchmark during periods of high temperature can lead to... The value is abnormally high. However, this is precisely the "summer high-temperature thermal stress penalty mechanism" intentionally designed in this invention. Summer is a high-risk period for power distribution networks to ensure supply. If the benchmark is replaced with real-time summer high temperature according to conventional thinking, such as... A larger denominator will actually lower the value. The risk will be mitigated. This means that in summer, when transformers are most prone to burnout, the system's alarm threshold becomes more lenient, which is extremely dangerous in industrial applications. This invention employs a fixed value... Anchoring the denominator makes the calculation in summer... The values ​​naturally show an exponentially high level, which is not a misjudgment, but an objective reflection of the deterioration of basic heat dissipation and accelerated shortening of insulation life caused by extreme high temperatures in summer. This prompts the system to issue early warnings for even slight harmonics in summer, which is consistent with the conservative load reduction dispatch strategy adopted by the State Grid.

[0067] Thus, the thermo-harmonious coupling degree used to determine the cause of high temperature was obtained.

[0068] S3: Based on the relationship between electrical environment conditions and thermal imbalance conditions on the stable operation of the distribution network, calculate the power grid health score based on thermal harmonic coupling degree and harmonic erosion index; classify the power grid operating status according to the power grid health score; extract the harmonic erosion index and thermal harmonic coupling degree corresponding to high-risk power grid operating status to determine the fault type.

[0069] It should be noted that for the operation and maintenance management of distribution networks, a single indicator is often insufficient to reflect the overall state. Analyzing only the transformer voltage distortion rate may overlook the fact that equipment is already overheating; analyzing only the thermal state may fail to trace the root cause of the problem. This invention constructs a comprehensive power grid health score to comprehensively assess the system state. In constructing this scoring model, this invention draws on the concept of a penalty function from control theory. The ideal state of the system is equilibrium, and any deviation from equilibrium should be penalized. This penalty should not be linear, because small deviations may be within a tolerable range, but once a critical point is exceeded, such as complete blockage of radiators or excessive harmonics, the potential risk increases exponentially. Therefore, this invention designs a penalty model based on baseline deviation, utilizing square terms... This allows the scoring system to capture this nonlinear risk characteristic. It also makes the system more tolerant of minor fluctuations while being more sensitive to severe thermal harmonic coupling faults, thus calculating a power grid health score that intuitively reflects the overall health status of the system.

[0070] Specifically, based on the relationship between electrical environmental conditions and thermal imbalance on the stable operation of the distribution network, and using thermal harmonic coupling degree and harmonic erosion index, a power grid health score is calculated, including:

[0071] Retrieve the thermal risk penalty score, which characterizes the insulation level of the transformer, from the database.

[0072] The power grid health score satisfies the following expression:

[0073] ;

[0074] In the formula, This represents the power grid health score, with a value range of (0,1]. Indicates the harmonic erosion index; Indicates the voltage distortion rate of the transformer; Indicates the degree of thermal harmonic coupling; The score is for thermal risk penalty.

[0075] In the formula, This reflects the level of pollution in the basic electrical environment of the power distribution network; This is the core benchmark penalty term, which uses a quadratic function to capture the degree to which the thermal harmonic coupling deviates from the theoretical benchmark value of 1. This deviation is essentially an imbalance between the electrical environment and the thermal state of the equipment in the distribution network, directly affecting the safe and stable operation of the distribution network. When the distribution network is in an ideal physical state, The score is only affected by electrical indicators; once an anomaly occurs in the distribution network, i.e., a transformer experiences a pure heat dissipation fault or a severe thermal harmonic fault, ( ), All will produce positive values, after The magnification rapidly increases the denominator, leading to... A significant decrease. In this invention A value of 10 represents the transformer insulation class and is used to amplify the impact of thermal faults on the score.

[0076] For example, consider scenario A, where the distribution network is under normal no-load conditions: , , At this point, the transformer is in a healthy state, proving that the system made no misjudgments under no-load conditions; however, scenario B exists, where the distribution network is under normal full load: , , At this point, the transformer is in a healthy state, which proves that the system can clearly eliminate interference from normal load temperature rise and avoid misjudgment due to high temperature; there is also scenario C, where the transformer in the distribution network experiences a pure heat dissipation fault: , , At this point, the power grid health score decreases, indicating that the system accurately judges that the transformer is in a physical heat dissipation state without harmonic interference; scenario D exists, where the transformer in the distribution network experiences an emergency fault due to thermal harmonic fluctuations: , , This indicates a high risk of transformer failure, meaning the system has effectively detected a complex and severe transformer fault. The above results... , , and Round to two decimal places.

[0077] It should be noted that in calculating the power grid health score, the harmonic erosion index both affects the calculation of the thermal-harmonic coupling degree and participates in the calculation of the power grid health score as an independent term. In fact, this is not a duplicate calculation, but rather the core thermal-electrical multidimensional cross-penalty mechanism of this invention. This is because the damage caused by malignant harmonics to the distribution network is cross-dimensional; that is, it can cause physical heating due to the skin effect, thus being penalized by the thermal-electrical multidimensional cross-penalty mechanism in the C expression. This captures the data; however, it also pollutes the electrical waveform quality of the power grid, thus affecting the denominator of the S expression. The algorithm directly captures the values. A severe harmonic source should be penalized simultaneously in both physical thermal effects and electromagnetic environment dimensions to truly reflect the overall power quality. Furthermore, the theoretical limit S=1.0 of the expression design represents an absolutely noise-free laboratory vacuum state. However, in industrial settings, minor distortions are unavoidable, so the grid health score stabilizes in the [0.90, 1.0) range under healthy conditions, as shown in the example above where the steady-state score is 0.99. This algorithm does not pursue an absolute 1.0, but rather uses a health judgment threshold of 0.90 to accommodate normal minor engineering deviations, making the score highly intuitive and discriminative.

[0078] It should be noted that, Figure 2 This is a sensitivity analysis chart for health scores based on baseline deviation. The chart shows the correspondence between the thermal harmonic coupling degree and the health score of the distribution network under different operating scenarios. Scenario A and Scenario B both have a thermal harmonic coupling degree of 1.0, corresponding to a health score of approximately 0.99; Scenario C has a thermal harmonic coupling degree of 1.12, corresponding to a health score of approximately 0.86; and Scenario D has a thermal harmonic coupling degree of 2.23, corresponding to a health score of approximately 0.06. This difference demonstrates that the power grid health score can reflect the comprehensive operating status of the distribution network by incorporating the deviation of the thermal harmonic coupling degree. It allows distribution networks operating normally to receive higher scores, while those operating abnormally receive lower scores, thus distinguishing distribution networks in different operating states.

[0079] Thus, a power grid health score, which characterizes the overall health status of the power quality of the distribution network, was obtained.

[0080] It's important to note that in monitoring centers, maintenance personnel deal with a massive data stream from numerous monitoring points. Directly displaying complex spectrum graphs could lead to difficulties in information identification. This invention discretizes continuous numerical values ​​into state levels: healthy, sub-healthy, and high-risk, meeting the need for intuitive judgment. This grading mechanism, based on extensive historical data and expert experience, ensures that high-risk states truly correspond to emergency situations requiring immediate intervention, such as potential overheating and fire risks or severe harmonic pollution. Through color coding, such as green, yellow, and red, the system can highlight anomalies in massive amounts of data, guiding maintenance resources to where they are most needed. This transforms the maintenance model from passive querying to proactive push notifications, ensuring that faults are detected in their early stages.

[0081] Preferably, the power grid operating status is classified into levels based on the power grid health score, including:

[0082] A first judgment threshold and a second judgment threshold are set. If the power grid health score is greater than or equal to the first judgment threshold, the power grid operation status is determined to be healthy and displayed in green on the monitoring terminal. If the power grid health score is less than the first judgment threshold but greater than or equal to the second judgment threshold, the power grid operation status is determined to be sub-healthy and displayed in yellow on the monitoring terminal. If the power grid health score is less than the second judgment threshold, the power grid operation status is determined to be high-risk and displayed in red on the monitoring terminal, triggering an audible and visual alarm.

[0083] It should be noted that this applies when the equipment is in an ideal physical state. for And there is only a slight background voltage distortion within the allowable range, such as At this time, the calculated power grid health score is usually in the range of [0.90, 1.0], representing that the system is in the normal operating range; therefore, this invention sets the first judgment threshold to 0.90. When early slight overheating occurs, such as When it rises to around 1.05, or when there is moderate harmonic pollution, the penalty item... Once activated, the score will quickly drop to the 0.60-0.90 range, prompting maintenance personnel to pay attention. When the grid health score falls below 0.60, it usually means that the thermal harmonic coupling degree has deviated from 1.0. If the harmonic erosion index is high, it indicates that the system has a substantial physical fault or serious pollution, and immediate intervention is required. Therefore, the second judgment threshold is set to 0.60 in this invention.

[0084] At this point, different levels of power grid operation status were obtained.

[0085] It's important to note that triggering an audible and visual alarm after determining the power grid's operating status as high-risk is only the first step. Fault tracing is crucial for resolving the problem. Upon receiving a high-risk alarm, the core issue for maintenance personnel is identifying the responsible party, requiring analysis of two scenarios: user-side loads generating significant harmonics and abnormal transformer cooling on the grid side. The handling procedures for these two scenarios are drastically different. This invention utilizes the harmonic erosion index and thermal harmonic coupling degree. The harmonic erosion index is a characteristic parameter of the current waveform, directly indicating load characteristics; while the thermal harmonic coupling degree is a comprehensive indicator of physical condition, pointing to equipment health. Through cross-analysis of these two dimensions, the system can automatically provide a judgment result. For example, when the harmonic erosion index is small but the thermal harmonic coupling degree is large, harmonic interference is ruled out, directly pinpointing a physical fault in the cooling system. This automated logical reasoning based on absolute physical boundaries avoids inefficient troubleshooting lacking data support, achieving precise fault location.

[0086] Preferably, the harmonic erosion index and thermal harmonic coupling degree corresponding to high-risk power grid operating states are extracted to determine the fault type, including:

[0087] When the system determines that the power grid is in a high-risk state, it automatically extracts the corresponding harmonic erosion index and thermal harmonic coupling degree, and obtains the preset harmonic anomaly threshold. If the harmonic erosion index is greater than the harmonic anomaly threshold, the power grid is determined to be in a load pollution type fault. If the thermal harmonic coupling degree is greater than the theoretical reference value of 1, and the harmonic erosion index is lower than or equal to the harmonic anomaly threshold, the power grid is determined to be in a transformer cooling system anomaly.

[0088] It should be noted that the aforementioned preset harmonic anomaly threshold is not based on historical averages that are easily affected by random fluctuations in transformer load, but rather on an absolute safety red line that integrates multiple factors. The specific calibration method involves substituting the permissible injection limits of each harmonic current for the voltage level of the distribution network as specified in the national power quality standards into the expression for the harmonic erosion index, which uses the transformer rated current IR as a normalized reference. The calculated theoretical boundary value is then used as the preset harmonic anomaly threshold. For example, in this embodiment, for a distribution transformer of a specific capacity, the preset harmonic anomaly threshold is set to 0.50. This invention overcomes the potential for numerical inversion and misjudgment that may occur when the transformer load rate fluctuates significantly, such as during the recovery from a low-consumption period to a high-consumption period, by comparing the real-time calculated dynamic harmonic erosion index with this harmonic anomaly threshold, ensuring the rigor and uniqueness of the load pollution-type fault diagnosis logic.

[0089] It should be noted that, Figure 3This diagram maps fault tracing logic to control strategies, illustrating the distribution relationship between harmonic erosion index and thermal harmonic coupling degree in the distribution network, as well as the corresponding fault types and control strategies for different distribution areas. In the healthy and sub-healthy zones, both harmonic erosion index and thermal harmonic coupling degree are at low levels, requiring only routine monitoring. In the abnormal cooling system zone, where the transformer cooling system exhibits abnormal thermal harmonic coupling degree but a low harmonic erosion index, strategies such as activating backup cooling fans, checking for oil circuit blockages, or suggesting load transfer are necessary. In the nonlinear load pollution zone, where load pollution-type faults have a high harmonic erosion index, strategies such as issuing compensation commands, adjusting filter gain, or investigating and addressing the pollution source user are required. This distribution pattern demonstrates that the diagram can pinpoint the fault type in the distribution network by analyzing the combination of harmonic erosion index and thermal harmonic coupling degree, and match corresponding handling strategies, providing a reference for fault handling.

[0090] Thus, the fault types of high-risk power grid operating conditions have been identified.

[0091] S4: Issue adjustment instructions for different fault types and generate visual evaluation reports.

[0092] It's important to note that the ultimate goal of monitoring and evaluation is closed-loop control. An intelligent system should not only detect problems but also possess the ability to solve them. Based on the fault type, the system generates targeted control commands. For load pollution-related faults, the SVG command aims to dynamically compensate and eliminate the source; for transformer cooling system anomalies, it activates backup cooling or suggests switching power supply, while simultaneously generating a comprehensive evaluation report. The report integrates scoring curves and control records, forming a complete record chain, ensuring that every fault handling is based on evidence. This not only improves the power grid's self-healing capabilities but also provides valuable data references for future equipment selection and operation and maintenance strategy optimization.

[0093] Preferably, adjustment instructions are issued for different fault types, and a visual assessment report is generated, including:

[0094] For load pollution type faults, the system issues harmonic compensation gain adjustment instructions to the grid-connected SVG; for transformer heat dissipation system abnormalities, the system sends load transfer suggestions to the dispatch center. At the same time, the changes in grid health score, the identified fault types and the control instructions taken during the assessment period are packaged to generate a visual assessment report.

[0095] This completes the monitoring and comprehensive assessment of power quality in the distribution network.

[0096] This invention also discloses a power quality monitoring and comprehensive evaluation system for power distribution networks, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a power quality monitoring and comprehensive evaluation method for power distribution networks according to this invention.

[0097] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0098] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for monitoring and comprehensively evaluating power quality in a distribution network, characterized in that, include: The intelligent monitoring terminal installed on the low-voltage side of the distribution transformer performs data acquisition and spectrum feature extraction to obtain environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current and transformer harmonic current order; Based on three-phase voltage and three-phase current data, the transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, are calculated. Based on the enhancement effect of higher frequency harmonics on the conductor skin effect, and the rated current in the environmental reference parameters, the harmonic erosion index, which characterizes the complexity of the harmonic spectrum on the load side, is calculated. Based on the differences in the impact of transformer harmonic current fluctuations and system faults on heat changes, and combined with the harmonic erosion index, the thermal-harmonic coupling degree used to determine the cause of high temperatures is calculated; the harmonic erosion index... satisfy: ； It is a dimensionless numerical value; This represents the nth transformer harmonic current; Indicates the rated current; Indicates the order of transformer harmonic current; This indicates the preset maximum harmonic analysis number; Represent the natural logarithm function; It is a natural constant; Thermal harmonic coupling satisfy: ； It is a dimensionless numerical value; This indicates the top oil temperature data; Kelvin constant; Indicates the average annual temperature; Indicates the rated temperature rise; Indicates the total current of the transformer; Indicates the rated current; Indicates the harmonic erosion index; Represents an exponential function with the natural constant as its base; Based on the relationship between electrical environment conditions and thermal imbalance conditions on the stable operation of the distribution network, a power grid health score is calculated based on thermal harmonic coupling degree and harmonic erosion index; the power grid operating status is classified according to the power grid health score. Extract the harmonic erosion index and thermal harmonic coupling degree corresponding to high-risk power grid operating conditions to determine the fault type; Adjustment instructions are issued for different fault types, and visual evaluation reports are generated.

2. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The acquisition of environmental reference parameters, three-phase voltage data, three-phase current data, transformer harmonic current, and transformer harmonic current order includes: The intelligent monitoring terminal installed on the low-voltage side of the distribution transformer collects synchronous three-phase voltage data, three-phase current data, and top oil temperature data in real time at a fixed frequency. Environmental reference parameters are obtained from the database, the three-phase current data is windowed and truncated, and the fundamental current at the fundamental frequency is analyzed using fast Fourier transform and denoted as the transformer fundamental current. The harmonic currents from the 2nd to the preset cutoff number N and their corresponding harmonic numbers are denoted as the transformer harmonic current and the transformer harmonic current number.

3. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The calculation of transformer voltage distortion rate and total transformer current, which characterize the overall power quality level, includes: A fast Fourier transform is performed on the three-phase voltage data to extract the transformer fundamental voltage, transformer harmonic voltage, and the order of the transformer harmonic voltage. The square root of the sum of squares of the transformer harmonic voltages is calculated and compared with the transformer fundamental voltage to obtain the transformer voltage distortion rate. At the same time, the square of the transformer fundamental current and the sum of squares of all transformer harmonic currents are calculated, and the square root of the sum of squares is performed to obtain the total current flowing through the transformer, which is denoted as the transformer total current.

4. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The calculation of the power grid health score includes: Retrieve thermal risk penalty scores, which characterize the transformer insulation class, from the database; ； In the formula, This represents the power grid health score, with a value range of (0,1]. Indicates the harmonic erosion index; Indicates the voltage distortion rate of the transformer; Indicates the degree of thermal harmonic coupling; The score is for thermal risk penalty.

5. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The classification of power grid operating status includes: A first judgment threshold and a second judgment threshold are set. If the power grid health score is greater than or equal to the first judgment threshold, the power grid operation status is determined to be healthy and displayed in green on the monitoring terminal. If the power grid health score is less than the first judgment threshold but greater than or equal to the second judgment threshold, the power grid operation status is determined to be sub-healthy and displayed in yellow on the monitoring terminal. If the power grid health score is less than the second judgment threshold, the power grid operation status is determined to be high-risk and displayed in red on the monitoring terminal, triggering an audible and visual alarm.

6. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The determination of the fault type includes: When the system determines that the power grid is in a high-risk state, it automatically extracts the corresponding harmonic erosion index and thermal harmonic coupling degree, and obtains the preset harmonic anomaly threshold. If the harmonic erosion index is greater than the harmonic anomaly threshold, the power grid is determined to be in a load pollution type fault. If the thermal harmonic coupling degree is greater than the theoretical reference value of 1, and the harmonic erosion index is lower than or equal to the harmonic anomaly threshold, the power grid is determined to be in a transformer cooling system anomaly.

7. The method for monitoring and comprehensively evaluating power quality in a distribution network according to claim 1, characterized in that, The issuance of adjustment instructions for different fault types includes: For load pollution type faults, the system issues harmonic compensation gain adjustment instructions to the grid-connected SVG; for transformer heat dissipation system abnormalities, the system sends load transfer suggestions to the dispatch center. At the same time, the changes in grid health score, the identified fault types and the control instructions taken during the assessment period are packaged to generate a visual assessment report.

8. A power quality monitoring and comprehensive evaluation system for power distribution networks, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a power quality monitoring and comprehensive evaluation method for a power distribution network according to any one of claims 1-7.

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