Electric energy quality analysis method based on circuit breaker intelligent algorithm

Abstract

The invention discloses an electric energy quality analysis method based on a circuit breaker intelligent algorithm. The method comprises the steps that a harmonic parameter, an interference electricity parameter, a three-phase imbalance parameter and an environment temperature parameter of a loop where a circuit breaker is located are acquired through an acquisition module; constructing a power quality parameter coupling model, and calculating coupling influence factors and correlation coefficients among the parameters; dynamically adjusting the analysis weight of each parameter based on the coupling influence factor and the correlation coefficient in combination with the real-time operation condition; according to the dynamic weight and the measured value of each parameter, calculating a comprehensive electric energy quality index and dividing fault grades; correcting the coupling model and the dynamic weight through a closed-loop feedback mechanism; the method solves the problem that the parameter coupling influence is ignored in the prior art, and improves the electric energy quality analysis accuracy and the fault pre-judgment reliability.

Description

Technical Field

[0001] This invention relates to the field of power system power quality monitoring and analysis technology, specifically to a power quality analysis method based on a circuit breaker intelligent algorithm. Background Technology

[0002] In low-voltage power distribution systems, power quality directly affects the operational stability and lifespan of electrical equipment. Circuit breakers, as core control and protection devices in circuits, are crucial for ensuring system safety through power quality analysis of their respective circuits. Existing power quality analysis technologies generally adopt a single-problem-independent approach, monitoring and evaluating harmonics, voltage dips, and three-phase imbalances separately without considering the coupling effects between these issues. For example, increased harmonic content can exacerbate voltage sags, voltage dips can further amplify three-phase voltage imbalances, and three-phase imbalances can amplify specific harmonic orders in turn. This isolated analysis model makes it difficult for current technologies to accurately capture the root causes of power quality problems, easily leading to misjudgments of fault types or missed potential risks. For instance, voltage dips caused by harmonic coupling may be misdiagnosed as grid voltage fluctuations, or excessive harmonics caused by three-phase imbalances may be misdiagnosed as harmonic emissions from the load itself.

[0003] Meanwhile, existing technologies mostly use fixed analysis weights, which cannot be dynamically adjusted according to the real-time operating conditions of the circuit. When the load is inductive, the impact of harmonics on power quality increases significantly, but the fixed weights are still allocated according to the conventional proportions, resulting in insufficient sensitivity of harmonic analysis. When the load rate is too high, the risk of equipment shutdown caused by power fluctuations increases sharply, but existing technologies do not increase the analysis weight of power fluctuation parameters, and cannot prioritize early warning of such high-risk problems. These shortcomings result in a large deviation between the current power quality analysis results and actual operating conditions, making it difficult to meet the needs of refined power quality management in scenarios such as industrial production and data centers.

[0004] Based on the above problems, there is an urgent need for an analytical method that can quantify the coupled effects of multiple power quality issues and dynamically adapt to operating conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a power quality analysis method based on a circuit breaker intelligent algorithm, and the specific technical solution is as follows: The method includes steps such as collecting power quality parameters and ambient temperature parameters of the circuit breaker circuit, and further includes: constructing a power quality parameter coupling model, which is used to quantify the mutual influence between harmonics, power dips, and three-phase imbalance; calculating the coupling influence factor and correlation coefficient between each power quality parameter based on the coupling model; dynamically adjusting the weight of each power quality parameter in the analysis process based on the real-time operating conditions of the circuit; calculating a comprehensive power quality index based on the dynamic weights and the measured values ​​of each power quality parameter, and classifying power quality fault levels according to the comprehensive power quality index; and correcting the parameters of the coupling model and the dynamic weights through a closed-loop feedback mechanism using the matching results of the comprehensive power quality index and the fault level.

[0006] Preferably, the power quality parameters include harmonic parameters, voltage sag parameters, and three-phase imbalance parameters. The harmonic parameters include the content of each harmonic and the total harmonic distortion rate. The voltage sag parameters include the voltage rise value, voltage drop value, and voltage interruption duration. The three-phase imbalance parameters include the three-phase voltage imbalance degree and the three-phase current imbalance degree. The ambient temperature parameter is acquired by a temperature sensor. The acquisition step is implemented by a parameter acquisition module, which includes a harmonic sensor, a voltage transient sensor, a three-phase current / voltage sampling circuit, and a temperature sensor. The output terminal of the parameter acquisition module is connected to the input terminal of the microcontroller unit.

[0007] Preferably, the construction process of the coupling model includes: determining the basic influence weights of harmonics, power sloshing, and three-phase imbalance; analyzing the triggering probability of harmonics on power sloshing, the aggravation degree of power sloshing on three-phase imbalance, and the amplification coefficient of three-phase imbalance on harmonics; and incorporating the triggering probability, aggravation degree, and amplification coefficient as core parameters into the coupling model so that the coupling model can reflect the indirect influence of any two power quality parameter changes on the third parameter.

[0008] Preferably, the real-time operating conditions include the circuit's load type, load rate, and operating time. The load type includes resistive load, inductive load, and capacitive load. The adjustment criteria for the dynamic weights include: increasing the weight of the harmonic parameters when the load type is inductive; increasing the weight of the voltage fluctuation parameters when the load rate exceeds a preset threshold; and increasing the weight of the three-phase imbalance parameters when the operating time exceeds a preset duration.

[0009] Preferably, the coupling influence factor includes the harmonic coupling influence factor Khc, and the calculation of Khc satisfies the following formula: ; in, The harmonic coupling influence factor is dimensionless. The nth harmonic content is expressed in % (%). These are harmonic order coefficients, dimensionless, corresponding to the nth harmonic. The value increases linearly as n increases; The ambient temperature is expressed in °C.

[0010] Preferably, the correlation coefficient includes the voltage fluctuation-three-phase imbalance correlation coefficient Ksu, and the calculation of Ksu satisfies the following formula: ; in, is the voltage dip-three-phase imbalance correlation coefficient, dimensionless; $\DeltaU_s$ is the voltage rise or fall amplitude during voltage dip, in V; Three-phase voltage imbalance, expressed in % %. The duration of a single voltage flicker is measured in milliseconds (ms). The reference voltage fluctuation duration is expressed in milliseconds (ms) and is set to 10 ms.

[0011] Preferably, the calculation of the comprehensive power quality index (PQI) satisfies the following formula: in, The power quality index is a dimensionless comprehensive power quality index. For dynamic weighting of harmonic parameters, For dynamic weighting of voltage sway parameters, For the dynamic weights of the three-phase imbalance parameters, and ; Total harmonic distortion (THD) is expressed as % (%). The reference total harmonic distortion (THD) is expressed as a percentage, with a value of 5%. This represents the maximum voltage fluctuation amplitude, in volts (V). This is the rated voltage of the circuit, in volts (V). The three-phase unbalance-harmonic correlation coefficient is dimensionless and is calculated by coupling the three-phase current unbalance degree with the third harmonic content rate. The maximum three-phase voltage imbalance is expressed in percent.

[0012] Preferably, the closed-loop feedback mechanism includes five sequentially executed steps: parameter acquisition and update, coupling model correction, dynamic weight adjustment, comprehensive index recalculation, and fault level verification. When the matching deviation between the comprehensive power quality index and the fault level exceeds a preset deviation threshold, the closed-loop feedback mechanism triggers the microcontroller to output an alarm signal and suspends the current power quality analysis process until the matching deviation drops below the preset deviation threshold.

[0013] Preferably, the microcontroller unit uses a chip with data processing and communication functions, and the microcontroller unit is connected to the parameter acquisition module via an SPI bus; the microcontroller unit is connected to the actuator of the circuit breaker via a PWM signal, and is used to output control commands according to the comprehensive power quality index to adjust the opening and closing state of the circuit breaker; the microcontroller unit is also connected to a storage module, which is used to store historical data of the power quality parameters, correction records of the coupling model, and the judgment results of the fault level.

[0014] Preferably, the method further includes the step of transmitting the comprehensive power quality index and the fault level to the remote operation and maintenance system; after receiving the comprehensive power quality index and the fault level, the remote operation and maintenance system generates a corresponding operation and maintenance work order, and optimizes the subsequent power quality analysis strategy according to the correction record of the coupling model; the remote operation and maintenance system and the microcontroller unit are connected through a 4G / 5G communication module.

[0015] Compared with existing technologies, this invention has the following advantages: By constructing a power quality parameter coupling model, this invention quantifies the coupled effects of harmonics, voltage fluctuations, and three-phase imbalance, thus overcoming the shortcomings of isolated analysis in existing technologies; by dynamically adjusting weights based on real-time operating conditions, it ensures the relevance of analysis under different loads and load rates; by correcting the model and weights through closed-loop feedback, it improves the accuracy of analysis; and ultimately achieves accurate calculation of the comprehensive power quality index and fault level classification, effectively avoiding misjudgments and omissions, and providing reliable support for power quality management and control of low-voltage distribution systems. Attached Figure Description

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of the power quality analysis method based on the intelligent algorithm of circuit breakers according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Traditional technical solutions have the following technical problems: existing power quality analysis technology only deals with harmonics, power fluctuations and three-phase imbalances separately, ignoring the coupling effect between the three, and the analysis weights are fixed, which cannot be adapted to real-time operating conditions, resulting in large deviations in analysis results and easy misjudgment or omission of faults.

[0021] Based on this, please refer to Figure 1 This embodiment provides a power quality analysis method based on a circuit breaker intelligent algorithm, including: S1: Steps for collecting power quality parameters and ambient temperature parameters of the circuit breaker circuit. Also includes: S2: Construct a power quality parameter coupling model, which is used to quantify the mutual influence between harmonics, power sag, and three-phase imbalance. S3: Calculate the coupling influence factor and correlation coefficient between various power quality parameters based on the coupling model; S4: Based on the real-time operating conditions of the circuit, dynamically adjust the weight of each power quality parameter in the analysis process; S5: Calculate the comprehensive power quality index based on the dynamic weights and the measured values ​​of each power quality parameter, and classify the power quality fault level according to the comprehensive power quality index; S6: By using the matching results of the comprehensive power quality index and the fault level through a closed-loop feedback mechanism, the parameters of the coupled model and the dynamic weights are corrected.

[0022] The implementation of this technical solution relies on a complete process architecture encompassing data acquisition, calculation, adjustment, evaluation, and feedback. First, parameter acquisition is achieved through a sensor array deployed at the circuit breaker's incoming line. The acquired power quality parameters cover harmonics, voltage dips, and three-phase imbalance; environmental parameters include only temperature. The acquisition frequency is set to 50Hz to ensure real-time capture of parameter changes. Second, during the construction of the coupling model, the basic influence weights are first determined experimentally, such as a basic weight of 0.4 for harmonics, 0.3 for voltage dips, and 0.3 for three-phase imbalance. Then, coupling parameters are obtained through extensive operating condition tests. For example, for every 1% increase in the 5th harmonic content, the probability of voltage dip triggering increases by 2%; for every 10V increase in voltage dip, the three-phase voltage imbalance worsens by 0.5%. These parameters are incorporated into the model, enabling it to reflect the chain reaction of increased harmonics, intensified voltage dips, and expanded three-phase imbalance. Dynamic weight adjustment requires real-time acquisition of load type, determined by the phase difference between current and voltage: positive phase difference for inductive loads, negative for capacitive loads, and near-zero for resistive loads. Load factor and real-time current / rated current are also considered. For example, when the load is inductive, the harmonic weight increases from 0.4 to 0.5; when the load factor exceeds 70%, the voltage fluctuation weight increases from 0.3 to 0.4. The comprehensive power quality index calculation integrates dynamic weights and coupling factors to classify fault levels: PQI < 0.5 is normal, 0.5~1.0 is a minor fault, 1.0~1.5 is a moderate fault, and > 1.5 is a severe fault. The closed-loop feedback loop executes according to a preset cycle. If PQI is 1.2 but a minor fault is identified, the harmonic coupling factor in the coupling model is corrected, and the weight allocation is adjusted until the deviation meets the preset requirements.

[0023] This solution breaks through the limitations of isolated analysis by using a coupled model, adapts dynamic weights to changes in operating conditions, and ensures real-time performance through closed-loop feedback. It effectively solves the problem of inaccurate analysis in existing technologies and provides accurate basis for power quality management.

[0024] Traditional technical solutions have the following technical problems: existing parameter acquisition modules mostly use a single sensor, which limits the monitoring dimensions and makes communication with the control unit unstable, resulting in incomplete or delayed measured data, affecting subsequent analysis.

[0025] Based on this, the power quality parameters include harmonic parameters, voltage sag parameters, and three-phase imbalance parameters. The harmonic parameters include the content of each harmonic and the total harmonic distortion rate. The voltage sag parameters include the voltage rise value, voltage drop value, and voltage interruption duration. The three-phase imbalance parameters include the three-phase voltage imbalance degree and the three-phase current imbalance degree. The ambient temperature parameter is acquired through a temperature sensor. The acquisition process is implemented through a parameter acquisition module, which includes a harmonic sensor, a voltage transient sensor, a three-phase current / voltage sampling circuit, and a temperature sensor. The output of the parameter acquisition module is connected to the input of the microcontroller unit.

[0026] The parameter acquisition module of this technical solution requires a multi-sensor collaborative design. The harmonic sensor is an FFT-based high-frequency sampling sensor, model AcrelAHF, with a sampling frequency of 2kHz. It can simultaneously output the 2nd-50th harmonic content and total harmonic distortion rate, with a measurement range of 0-30%, and transmits the data to the microcontroller unit via analog signals. The voltage transient sensor is a Tektronix P6015A with a bandwidth of 100MHz, capable of capturing voltage sags / dips as small as 5V, with a response time <1μs, and transmits voltage fluctuation parameters via digital signals. The three-phase current / voltage sampling circuit uses a combination of Hall effect current sensors and voltage divider circuits, with a current measurement range of 0-200A and a voltage range of 0-400V, a sampling accuracy of 0.5%, and the output analog signal is converted to a digital signal by an ADC before transmission. The temperature sensor is a temperature acquisition element suitable for industrial scenarios, with a measurement range of -40℃ to 125℃ and an accuracy of ±0.3℃, and communicates with the microcontroller unit via a corresponding communication interface. The microcontroller unit uses a chip with data processing and communication functions. Its input terminal is connected to the output terminals of each sensor via an SPI bus to achieve centralized data reception. At the same time, the microcontroller unit has a built-in data buffer to avoid data loss. In actual operation, each sensor synchronously collects data at a frequency of 50Hz. After the data is initially verified by the microcontroller unit, it is stored in the built-in buffer to provide complete and accurate measured data for subsequent coupled model calculations.

[0027] This solution utilizes multiple sensors to collect data, covering all key parameters. Stable communication connections ensure real-time data transmission, addressing the limitations of existing acquisition modules in terms of dimensionality and communication instability, and providing a reliable data foundation for subsequent analysis.

[0028] Traditional technical solutions have the following technical problems: existing coupling models simply superimpose the effects of each parameter without clarifying the indirect relationships between parameters, and cannot quantify the chain effects of harmonics, voltage fluctuations and three-phase imbalance, resulting in insufficient depth of model analysis.

[0029] Based on this, the construction process of the coupling model includes: determining the basic influence weights of harmonics, power sloshing, and three-phase imbalance; analyzing the triggering probability of harmonics on power sloshing, the aggravation degree of power sloshing on three-phase imbalance, and the amplification coefficient of three-phase imbalance on harmonics; and integrating the triggering probability, aggravation degree, and amplification coefficient as core parameters into the coupling model so that the coupling model can reflect the indirect influence of any two power quality parameter changes on the third parameter.

[0030] The construction of the coupling model for this technical solution requires three steps. The first step is to determine the basic influence weights: through comparative experiments under standard operating conditions, the independent influence of each parameter on power quality is measured. When the harmonic content exceeds the standard, the power quality decreases by 30%; during power fluctuations, it decreases by 25%; and during three-phase imbalance, it decreases by 20%. Based on this, the basic weights are set as follows: harmonics 0.4, power fluctuations 0.3, and three-phase imbalance 0.3. The second step is to obtain coupling parameters: through variable control experiments, the indirect influence coefficients are determined as follows: Harmonic-triggered voltage sag: the number of voltage sags is recorded under different harmonic content rates, and it is found that for every 1% increase in the 5th harmonic content rate, the voltage sag trigger probability increases by 2%, with a trigger probability coefficient of 0.02; Voltage sag exacerbates three-phase imbalance: the change in imbalance is measured under different voltage sag amplitudes, and it is found that for every 10V increase in the sag amplitude, the three-phase voltage imbalance increases by 0.5%, with an aggravation coefficient of 0.05; Three-phase imbalance amplifies harmonics: the change in harmonics is measured under different imbalance degrees, and it is found that for every 1% increase in the three-phase current imbalance, the 3rd harmonic content rate increases by 0.3%, with an amplification coefficient of 0.03. The third step is model integration: The basic weights and coupling parameters are written into the model formulas. For example, the indirect impact of harmonics on three-phase imbalance is calculated through the path "harmonics → voltage dips → three-phase imbalance." That is, an increase in the harmonic content ΔHn increases the voltage dip trigger probability by 0.02ΔHn, leading to an increase in the three-phase imbalance degree of 0.05 × 0.02ΔHn × ΔUs. This ultimately quantifies the indirect impact of any two parameters on the third parameter. After the model is built, it needs to be validated using 100 sets of test data under different operating conditions to ensure that the calculation error of the coupling effect is <5%.

[0031] This scheme determines the coupling parameters through experiments, clarifies the indirect interaction paths between the parameters, solves the problem of insufficient analytical depth in existing models, and enables the coupled model to accurately reflect the chain reaction of power quality problems.

[0032] Traditional technical solutions have the following technical problems: existing dynamic weight adjustment is based on only a single operating condition parameter and does not take into account key factors such as load type and runtime, resulting in insufficient targeting of weight adjustment and inability to adapt to complex operating condition changes.

[0033] Based on this, the real-time operating conditions include the load type, load rate, and operating time of the circuit. The load type includes resistive load, inductive load, and capacitive load. The adjustment criteria for the dynamic weights include: increasing the weight of the harmonic parameters when the load type is an inductive load; increasing the weight of the voltage fluctuation parameters when the load rate exceeds a preset threshold; and increasing the weight of the three-phase imbalance parameters when the operating time exceeds a preset duration.

[0034] The dynamic weight adjustment of this technical solution requires collaborative judgment based on multiple operating parameters. First, load type judgment: the microcontroller unit achieves this by collecting the phase difference θ between voltage and current—θ>0 indicates an inductive load, θ<0 indicates a capacitive load, and θ≈0 indicates a resistive load. When determined to be an inductive load, due to the amplification effect of inductive loads on harmonics (e.g., motor loads amplify the 5th and 7th harmonics), the harmonic parameter weight is increased from the base value of 0.4 to 0.5, while the weights for voltage fluctuation and three-phase imbalance are reduced to 0.25 and 0.25, respectively. If it is a capacitive load, such as a capacitor compensation device, the three-phase imbalance weight is increased to 0.4. Capacitive loads easily lead to three-phase current imbalance, so the harmonic and voltage fluctuation weights are reduced to 0.3 and 0.3, respectively. Secondly, load rate judgment: The preset load rate threshold is 70%. When the real-time load rate > 70%, the risk of circuit overload increases, and the probability of equipment shutdown caused by voltage fluctuations increases. Therefore, the weight of the voltage fluctuation parameter is increased from 0.3 to 0.4, and the weights of harmonics and three-phase imbalance are adjusted to 0.35 and 0.25, respectively. If the load rate < 30%, the weight of voltage fluctuation is reduced to 0.2, and the weight of three-phase imbalance is increased to 0.35, as the impact of three-phase imbalance on equipment is more significant under light load. Finally, runtime judgment: The preset runtime threshold is 8 hours. When the circuit breaker runs continuously for more than 8 hours, the heating of the circuit lines will aggravate the three-phase impedance imbalance, thereby increasing the three-phase voltage imbalance. Therefore, the weight of three-phase imbalance is increased from 0.3 to 0.4, and the weights of harmonics and voltage fluctuations are adjusted to 0.3 and 0.3, respectively. After the weight adjustment, the sum must be 1, and each adjustment increment must not exceed 0.1 to avoid sudden weight changes that could cause analysis fluctuations. In practical applications, the microcontroller updates the operating parameters once a preset period and adjusts the weights synchronously to ensure that the weights match the operating conditions in real time.

[0035] This solution addresses the lack of specificity in existing technologies by collaboratively adjusting the weights of multiple operating parameters, enabling the analysis weights of each parameter to be accurately adapted to different operating conditions and improving the accuracy of the analysis.

[0036] Traditional technical solutions have the following technical problems: existing harmonic coupling influence factor calculations do not consider the combined effects of harmonic order and ambient temperature, and only use fixed coefficients, resulting in a large deviation between the factor and the actual coupling influence, and making it impossible to accurately quantify the impact of harmonics on other parameters.

[0037] Based on this, the coupling influence factor includes the harmonic coupling influence factor Khc, and the calculation of Khc satisfies the following formula: ; in, The harmonic coupling influence factor is dimensionless. The nth harmonic content is expressed in % (%). These are harmonic order coefficients, dimensionless, corresponding to the nth harmonic. The value increases linearly as n increases; The ambient temperature is expressed in °C.

[0038] Harmonic coupling influence factor of this technical solution The calculations require a clear understanding of the physical meaning and value logic of each parameter, and verification based on actual working conditions. First, parameter definitions and values: Let n be the harmonic content, for example, the 5th harmonic content is 3%. ; The harmonic order coefficients are set based on experimental data on the influence of harmonic order on coupling, where order n is related to... The relationship is Such as the third harmonic The 5th harmonic is 0.9, and the 7th harmonic is 1.1. Because higher harmonics have higher frequencies, they have a stronger triggering effect on voltage transients. It increases linearly with n; This refers to the ambient temperature, such as 25℃. Secondly, an example of formula application: In an industrial setting, the content of the 5th harmonic was monitored. Ambient temperature ,but Substitute into the formula to calculate: , ,final This indicates that the coupling effect of harmonics on other parameters under this operating condition is 0.72 times the reference value. Finally, the formula was verified: through actual measurements at different temperatures and harmonic orders, the calculated Khc was compared with the actual coupling effect, such as the increase in the number of voltage flickering caused by harmonics, ensuring that the error was <3%. For example, when At that time, the actual number of electric shocks increased by 7.2%, which matched the calculated value, verifying the effectiveness of the formula.

[0039] This scheme integrates harmonic order and temperature parameters to construct a dynamic coupling influence factor formula, which solves the problem of large deviations in existing fixed coefficients and achieves accurate quantification of harmonic coupling effects.

[0040] Traditional technical solutions have the following technical problems: the existing calculation of the correlation coefficient between voltage fluctuation and three-phase imbalance does not consider the synergistic effect of voltage fluctuation amplitude, duration and initial imbalance, and only uses voltage fluctuation amplitude as a single variable, which makes the coefficient unable to reflect the actual correlation degree and affects the accuracy of the coupling model.

[0041] Based on this, the correlation coefficients include the voltage fluctuation-three-phase imbalance correlation coefficient. The The calculation satisfies the following formula: ; in, The voltage fluctuation-three-phase imbalance correlation coefficient is dimensionless. This represents the voltage rise or fall during voltage fluctuations, expressed in volts (V). Three-phase voltage imbalance, expressed in % %. The duration of a single voltage flicker is measured in milliseconds (ms). The reference voltage fluctuation duration is expressed in milliseconds (ms) and is set to 10 ms.

[0042] The correlation coefficient between voltage fluctuation and three-phase imbalance in this technical solution The calculations require a clear understanding of the mechanisms and formula logic of each parameter, and must be verified through experiments. First, the parameters are defined and their functions: For voltage fluctuations, a positive value is used for a temporary rise, and an absolute value is used for a temporary fall. For example, if the voltage drops by 15V, then... =15V, the coefficient 0.03 means that for every 1V increase in amplitude, the basic term of the correlation coefficient increases by 0.03; This refers to the initial three-phase voltage imbalance. For example, if the initial imbalance is 2%, then... The coefficient of 0.05 indicates that for every 1% increase in the initial imbalance, the correlation coefficient increases by 5%, because the higher the initial imbalance, the more significant the aggravating effect of the voltage fluctuation. This refers to the duration of the voltage flicker; for example, if it lasts for 15ms... ; The baseline duration is used to normalize the impact of duration; the longer the duration, the larger the correlation coefficient. Secondly, an example of formula application: A voltage dip occurs in a data center circuit. =12V, initial three-phase voltage imbalance Duration Substitute into the formula to calculate: , , ,final This indicates that the correlation between the voltage fluctuation event and the three-phase imbalance is 1.48 times the baseline value. Finally, experiments were conducted to verify that the increase in imbalance after the voltage fluctuation was measured under different voltage fluctuation parameters and initial imbalance, and compared with the calculated Ksu, ensuring an error of <4%. For example, when... When the actual imbalance increased by 1.48%, consistent with the calculated value, the rationality of the formula was verified.

[0043] This scheme solves the problem of large deviations in existing single-variable calculations by using multi-parameter collaborative calculation of correlation coefficients, and achieves accurate quantification of the correlation between voltage fluctuation and three-phase imbalance.

[0044] Traditional technical solutions have the following technical problems: the existing comprehensive power quality index calculation does not integrate dynamic weights and coupling coefficients, but simply superimposes the measured values ​​of each parameter, which makes the index unable to reflect the coupling effect between parameters and the adaptability of operating conditions, and cannot accurately assess the overall power quality level.

[0045] Based on this, the calculation of the comprehensive power quality index (PQI) satisfies the following formula: ; in, The power quality index is a dimensionless comprehensive power quality index. For dynamic weighting of harmonic parameters, For dynamic weighting of voltage sway parameters, For the dynamic weights of the three-phase imbalance parameters, and ; Total harmonic distortion (THD) is expressed as % (%). The reference total harmonic distortion (THD) is expressed as a percentage, with a value of 5%. This represents the maximum voltage fluctuation amplitude, in volts (V). This is the rated voltage of the circuit, in volts (V). The three-phase unbalance-harmonic correlation coefficient is dimensionless and is calculated by coupling the three-phase current unbalance degree with the third harmonic content rate. The maximum three-phase voltage imbalance is expressed in percent.

[0046] The calculation of the comprehensive power quality index (PQI) in this technical solution needs to integrate dynamic weights, coupling coefficients, and measured parameters, clarify the calculation logic and coordination relationship of each part, and verify it through actual operating conditions. First, parameter definition and values: , , For dynamic weighting, such as inductive load or load factor of 60%, , , ; This is the total harmonic distortion rate; for example, if the measured value is 4%, then... ; The harmonic limits are those specified in the national standards; This refers to the maximum voltage dip, such as a maximum voltage drop of 10V during a certain period. =10V; This refers to the rated voltage of the circuit, such as 220V. ; The three-phase unbalance-harmonic correlation coefficient is calculated using the following formula: , This refers to the three-phase current imbalance. The third harmonic content, such as , ,but ; This represents the maximum three-phase voltage imbalance. If the actual measured value is 1.8%, then... Secondly, an example of formula application: Substituting the above parameters, the harmonic term is... ( (Taken from the above example), the lightning flicker item is ( (Taken from the above example), the three-phase imbalance term is: The total PQI = 0.288 + 0.017 + 0.4896 ≈ 0.7946, which is classified as a minor fault. Finally, verification and calibration: by comparing the PQI with the actual equipment operating status, such as a 5% efficiency decrease in efficiency without equipment downtime during a minor fault, it is ensured that the PQI level classification matches the actual impact, with an error of <5%.

[0047] This scheme integrates dynamic weights and coupling coefficients to address the problem that existing indices cannot reflect coupling effects and operating condition adaptability, thereby achieving accurate assessment of overall power quality.

[0048] Traditional technical solutions have the following technical problems: the existing closed-loop feedback mechanism is missing or has a delayed response, and there is no abnormal handling logic, which leads to untimely model and weight correction, and even disorder in the analysis process, affecting the real-time performance and reliability of power quality analysis.

[0049] Based on this, the closed-loop feedback mechanism includes five sequentially executed steps: parameter acquisition and update, coupling model correction, dynamic weight adjustment, comprehensive index recalculation, and fault level verification. When the matching deviation between the comprehensive power quality index and the fault level exceeds a preset deviation threshold, the closed-loop feedback mechanism triggers the microcontroller to output an alarm signal and suspends the current power quality analysis process until the matching deviation drops below the preset deviation threshold.

[0050] The closed-loop feedback mechanism of this technical solution requires a clear understanding of the execution logic and exception handling process for each stage. First, the execution details: In the parameter acquisition and update stage, the microcontroller receives the latest data from the parameter acquisition module at a preset cycle and updates the measured parameters, such as THD and ΔU. s γ u In the coupling model correction stage, the coupling coefficients in the model, such as K, are adjusted based on the latest measured data and historical correction records. hc K in h K su The coefficient of 0.03 in the figure, for example, when the measured K hc When K is 5% higher than the calculated value, hThe following steps are implemented: First, the dynamic weight adjustment stage involves recalculating the dynamic weights based on the updated operating parameters (load type, load rate). For example, if the load rate increases to 75%, the power fluctuation weight increases from 0.25 to 0.4. Second, the comprehensive index recalculation stage involves recalculating the PQI by substituting the corrected model parameters and weights. Third, the fault level verification stage compares the new PQI with the original fault level to determine the matching deviation. For example, if the original level is a minor fault, the new PQI = 0.86, and the deviation is 0.06. Fourth, the anomaly handling logic is as follows: The preset matching deviation threshold is 0.1. When the deviation > 0.1, such as if the original level is a minor fault, the new PQI = 1.1, and the deviation is 0.14, the microcontroller triggers an alarm signal, outputting an audible and visual alarm at a frequency of 1kHz, flashing a red light, and simultaneously pausing the analysis process, stopping all stages except parameter acquisition and updating. Then, an emergency correction is initiated, increasing the correction magnitude of the model parameters, such as 3% for normal correction and 8% for emergency correction. The coupled model correction is then re-executed until the deviation is ≤ 0.1, at which point the normal analysis process resumes. Finally, feedback recording: The microcontroller stores the parameters, weights, and deviation values ​​of each correction to the storage module to form a correction log, which is used for subsequent optimization feedback strategies, such as statistically analyzing common operating conditions where deviations exceed the standard and adjusting the correction magnitude accordingly.

[0051] This solution addresses the delays and inconsistencies in existing technologies through a complete feedback loop and an anomaly handling mechanism, ensuring real-time correction of the model and weights and a stable and reliable analysis process.

[0052] Traditional technical solutions have the following technical problems: the communication interfaces between existing microcontrollers, acquisition modules, and actuators are not unified, the data transmission rate is low, and there is a lack of storage modules, which leads to data transmission delays and loss, and the inability to store historical data, affecting the continuity and traceability of analysis.

[0053] Based on this, the microcontroller unit uses a chip with data processing and communication functions. The microcontroller unit is connected to the parameter acquisition module via an SPI bus. The microcontroller unit is connected to the actuator of the circuit breaker via a PWM signal, and is used to output control commands according to the comprehensive power quality index to adjust the opening and closing state of the circuit breaker. The microcontroller unit is also connected to a storage module, which is used to store historical data of the power quality parameters, correction records of the coupling model, and the judgment results of the fault level.

[0054] The hardware connection and functional implementation of this technical solution require clear definition of the interface standards and data interaction logic of each module. First, the microcontroller selection and communication connection: a chip with data processing and communication functions is selected, and its SPI interface is connected to the SPI output of the parameter acquisition module to achieve synchronous data reception; the SPI interface is configured with 8 data bits, 1 stop bit, and no parity, ensuring compatibility with the SPI interfaces of each sensor and avoiding data loss due to communication protocol incompatibility. Second, the connection with the actuator: the microcontroller's timer outputs a PWM signal with a frequency of 1kHz and a duty cycle of 0-100%, which is connected to the drive circuit of the circuit breaker actuator; when PQI>1.5, a PWM signal with a 100% duty cycle is output, energizing the coil and tripping the circuit breaker; when PQI<0.5 and the circuit breaker is in the tripped state, a PWM signal with a 50% duty cycle is output, energizing the coil and closing the circuit breaker; the duty cycle of the PWM signal is set according to the rated current of the coil to ensure reliable coil operation without overheating. Finally, the storage module is configured as follows: an SD card storage module is selected, which connects to the microcontroller unit via an SPI interface; the stored content includes: historical power quality parameter data, stored once at a preset period, including THD and ΔU. s The system records corrections for the coupled model (γu, T, etc.), storing parameters before and after each correction, correction time, and fault level determination results. For each level change, it stores the PQI value, level, and time. The storage module supports cyclic overwriting; when the capacity is full, the oldest data is deleted. It also supports data export via USB for offline analysis and tracing. During actual operation, the microcontroller writes data to the storage module at a preset cycle to ensure no data loss; data is periodically exported via USB to clear storage space and prevent storage overflow.

[0055] This solution addresses the issues of latency, data loss, and poor traceability in existing technologies through a unified communication interface and storage module, ensuring stable data transmission and enabling the storage and traceability of historical data.

[0056] Traditional technical solutions have the following technical problems: existing power quality analysis methods are not linked with remote operation and maintenance systems, and only perform local analysis, which cannot achieve remote monitoring and strategy optimization. As a result, operation and maintenance personnel cannot obtain fault information in a timely manner, and analysis strategies cannot be iteratively optimized based on multi-circuit data.

[0057] Based on this, the method further includes the step of transmitting the comprehensive power quality index and the fault level to the remote operation and maintenance system; after receiving the comprehensive power quality index and the fault level, the remote operation and maintenance system generates a corresponding operation and maintenance work order, and optimizes the subsequent power quality analysis strategy according to the correction record of the coupling model; the remote operation and maintenance system and the microcontroller unit are connected through a 4G / 5G communication module.

[0058] The remote linkage of this technical solution requires clear definition of communication module selection, data transmission protocol, and the optimization logic of the operation and maintenance system functions and strategies. First, regarding the communication module and transmission protocol: the microcontroller unit connects to a 4G / 5G communication module, supports LTE Cat.1, and communicates with the remote operation and maintenance system via TCP / IP protocol; the data transmission format is JSON, including fields such as: device number, PQI value, fault level, acquisition time, and coupling model correction record; a data retransmission mechanism and verification are employed to avoid data transmission errors. Second, regarding the operation and maintenance system functions: after receiving data, the system displays the PQI and fault level of each circuit breaker in real time. Through a web interface, fault levels are categorized by device number and marked with different colors: green for normal, yellow for mild, orange for moderate, and red for severe. When the fault level is ≥ moderate, an operation and maintenance work order is automatically generated, including the device location (e.g., "Distribution Box No. 1 in Workshop A"), the fault description (e.g., "PQI=1.3, moderate fault, voltage fluctuation parameters exceed limits"), and handling suggestions (e.g., checking grid voltage stability, investigating the cause of voltage fluctuation), and is pushed to operation and maintenance personnel via SMS / APP. Finally, the strategy optimization logic is as follows: The system stores the coupling model correction records of all circuit breakers and periodically analyzes the correction patterns under different operating conditions—for example, in the workshop inductive load circuit, K... hc If the average correction magnitude is 5%, then the initial K for optimizing this type of circuit is... hc The value was adjusted from 0.72 to 0.76 to reduce the number of subsequent corrections. At the same time, the fault level threshold was optimized according to the fault level distribution of multiple loops. For example, if a certain area has frequent minor faults and the actual equipment is not significantly affected, the minor fault threshold can be adjusted from 0.5~1.0 to 0.6~1.1. The optimized strategy is then sent to the microcontroller unit to update the analysis logic.

[0059] This solution overcomes the limitations of local analysis in existing technologies by enabling remote data transmission and operation and maintenance linkage, thereby achieving timely fault warnings, automatic work order generation, and iterative optimization of strategies, thus improving operation and maintenance efficiency and analysis accuracy.

[0060] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art can make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0061] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A power quality analysis method based on a circuit breaker intelligent algorithm, characterized in that, include: Collect power quality parameters and ambient temperature parameters of the circuit breaker circuit; construct a power quality parameter coupling model, which is used to quantify the mutual influence between harmonics, power dips, and three-phase imbalance; calculate the coupling influence factor and correlation coefficient between each power quality parameter based on the coupling model; dynamically adjust the weight of each power quality parameter in the analysis process based on the real-time operating conditions of the circuit; calculate the comprehensive power quality index based on the dynamic weights and the measured values ​​of each power quality parameter, and classify the power quality fault level according to the comprehensive power quality index; By using a closed-loop feedback mechanism, the parameters of the coupled model and the dynamic weights are corrected based on the matching results of the comprehensive power quality index and the fault level.

2. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The power quality parameters include harmonic parameters, voltage sag parameters, and three-phase imbalance parameters. The harmonic parameters include the harmonic content rate and total harmonic distortion rate. The voltage sag parameters include voltage sag amplitude, voltage drop amplitude, and voltage interruption duration. The three-phase imbalance parameters include three-phase voltage imbalance degree and three-phase current imbalance degree. The ambient temperature parameter is acquired by a temperature sensor. The acquisition process is implemented through a parameter acquisition module, which includes a harmonic sensor, a voltage transient sensor, a three-phase current / voltage sampling circuit, and a temperature sensor. The output of the parameter acquisition module is connected to the input of the microcontroller unit.

3. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The construction process of the coupling model includes: determining the basic influence weights of harmonics, power sloshing, and three-phase imbalance; analyzing the triggering probability of harmonics on power sloshing, the aggravation degree of power sloshing on three-phase imbalance, and the amplification coefficient of harmonics by three-phase imbalance; and integrating the triggering probability, aggravation degree, and amplification coefficient as core parameters into the coupling model so that the coupling model can reflect the indirect influence of any two power quality parameter changes on the third parameter.

4. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The real-time operating conditions include the load type, load rate, and operating time of the circuit. The load type includes resistive load, inductive load, and capacitive load. The adjustment criteria for the dynamic weights include: increasing the weight of the harmonic parameters when the load type is inductive; increasing the weight of the voltage fluctuation parameters when the load rate exceeds a preset threshold; and increasing the weight of the three-phase imbalance parameters when the operating time exceeds a preset duration.

5. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The coupling influence factors include the harmonic coupling influence factor K. hc The K hc The calculation satisfies the following formula: ； in, The harmonic coupling influence factor is dimensionless. The nth harmonic content is expressed in % (%). These are harmonic order coefficients, dimensionless, corresponding to the nth harmonic. The value increases linearly as n increases; The ambient temperature is expressed in °C.

6. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The correlation coefficients include the voltage fluctuation-three-phase imbalance correlation coefficient. The The calculation satisfies the following formula: ； in, The voltage fluctuation-three-phase imbalance correlation coefficient is dimensionless. This represents the voltage rise or fall during voltage fluctuations, expressed in volts (V). The three-phase voltage imbalance is expressed as % (%). The duration of a single voltage flicker is measured in milliseconds (ms). The reference voltage fluctuation duration is expressed in milliseconds (ms) and is set to 10 ms.

7. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The calculation of the comprehensive power quality index (PQI) satisfies the following formula: ； in, The power quality index is a dimensionless comprehensive power quality index. For dynamic weighting of harmonic parameters, For dynamic weighting of voltage sway parameters, For the dynamic weights of the three-phase imbalance parameters, and ; Total harmonic distortion (THD) is expressed as % (%). The reference total harmonic distortion (THD) is expressed as a percentage, with a value of 5%. This represents the maximum voltage fluctuation amplitude, in volts (V). This is the rated voltage of the circuit, in volts (V). The three-phase unbalance-harmonic correlation coefficient is dimensionless and is calculated by coupling the three-phase current unbalance degree with the third harmonic content rate. The maximum three-phase voltage imbalance is expressed in percent.

8. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The closed-loop feedback mechanism includes five sequentially executed steps: parameter acquisition and update, coupling model correction, dynamic weight adjustment, comprehensive index recalculation, and fault level verification. When the matching deviation between the comprehensive power quality index and the fault level exceeds a preset deviation threshold, the closed-loop feedback mechanism triggers the microcontroller to output an alarm signal and suspends the current power quality analysis process until the matching deviation drops below the preset deviation threshold.

9. A power quality analysis method based on a circuit breaker intelligent algorithm according to claim 2, characterized in that, The microcontroller unit uses a chip with data processing and communication functions. The microcontroller unit is connected to the parameter acquisition module via an SPI bus. The microcontroller unit is connected to the actuator of the circuit breaker via a PWM signal, and is used to output control commands according to the comprehensive power quality index to adjust the opening and closing state of the circuit breaker. The microcontroller unit is also connected to a storage module, which is used to store historical data of the power quality parameters, correction records of the coupling model, and the judgment results of the fault level.

10. The power quality analysis method based on a circuit breaker intelligent algorithm according to claim 1, characterized in that, The method further includes the step of transmitting the comprehensive power quality index and the fault level to the remote operation and maintenance system; after receiving the comprehensive power quality index and the fault level, the remote operation and maintenance system generates a corresponding operation and maintenance work order, and optimizes the subsequent power quality analysis strategy according to the correction record of the coupling model; the remote operation and maintenance system and the microcontroller unit are connected through a 4G / 5G communication module.

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