Phase control cabinet control system and method based on multi-level phase compensation

Abstract

The present invention belongs to the technical field of phase control of phase-controlled cabinets, and specifically relates to a phase-controlled cabinet control system and method based on multi-stage phase compensation. The invention first performs preliminary compensation on the voltage waveform and current waveform output by the phase-controlled cabinet to reduce the initial phase deviation, and then uses environmental parameters to match with a pre-stored interference feature library. It can identify the type of interference source and quantify it as a thermal noise contribution value to calculate the interference intensity prediction value within the prediction window. The phase residual output by the interference intensity prediction value then provides data support for secondary compensation. During secondary compensation, the compensation gain parameter is determined based on the dynamic error coefficient, and the voltage waveform and current waveform are compensated again to converge the phase deviation to a preset range. Based on the phase convergence information after the second compensation, the sampling frequency and compensation weight factor of the sensor array are reversely adjusted to form a closed-loop compensation control link, thereby achieving continuous optimization and improvement of the compensation process.

Description

Technical Field

[0001] The present invention belongs to the technical field of phase control of phase-controlled cabinets, and in particular relates to a phase-controlled cabinet control system and method based on multi-stage phase compensation. Background Art

[0002] With the rapid development of power electronics technology, phase control cabinets, as a crucial component of power systems, have a significant impact on the overall operational efficiency of power systems through their phase control accuracy and stability. However, in practical applications, due to various external interferences and internal factors, the voltage and current waveforms output by the phase control cabinets often exhibit phase deviations. This not only affects the stability of the power system but can also lead to equipment damage and increased energy consumption. Therefore, achieving precise phase control of the phase control cabinet output waveform has become a pressing technical challenge in the field of phase control technology for phase control cabinets.

[0003] In the prior art, a phase compensation method with fixed parameters is usually adopted. However, this method is often difficult to achieve ideal phase control effects when facing a complex and changeable interference environment. For example, when the interference intensity is large or the interference type changes frequently, the phase compensation method with fixed parameters may cause the phase deviation to fail to converge effectively, or even cause over-compensation or under-compensation, further aggravating the instability of the power system. Based on this, the present application provides a phase control cabinet control method based on multi-level phase compensation, which aims to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a phase control cabinet control system and method based on multi-level phase compensation, which can dynamically adjust the phase compensation strategy according to the electrical parameters and environmental parameters collected in real time, ensuring that the voltage waveform and current waveform output by the phase control cabinet can maintain highly accurate phase synchronization under various interference environments, thereby improving the stability and operating efficiency of the power system.

[0005] The technical solutions adopted by the present invention are as follows:

[0006] The phase control cabinet control method based on multi-level phase compensation includes:

[0007] The electrical and environmental parameters of the phase control cabinet working environment are acquired through the pre-deployed sensor array;

[0008] Calculate the phase offset reference value of the collected electrical parameters in real time, and compensate the voltage and current waveforms output by the phase control cabinet step by step according to the phase offset reference value;

[0009] Match the environmental parameters with the pre-stored interference feature library, identify the interference source type, and quantify it into a thermal noise contribution value. Calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value;

[0010] The actual phase information of the voltage and current waveforms after step-by-step compensation is collected in real time, compared with the preset ideal phase information, and the phase residual is output;

[0011] The dynamic error coefficient is calculated based on the phase residual and the interference intensity prediction value, and the compensation gain parameter is determined based on the dynamic error coefficient. The voltage waveform and the current waveform are compensated again based on the compensation gain parameter so that the phase deviation converges to a preset range.

[0012] The phase convergence information after re-compensation is collected, and the sampling frequency and compensation weight factor of the sensor array are reversely adjusted according to the phase convergence information to form a closed-loop compensation control link.

[0013] In a preferred embodiment, the sensor array includes a temperature sensor, a humidity sensor, an electromagnetic field strength sensor, and a voltage and current sensor. The temperature sensor is used to monitor the temperature changes of the working environment of the phase control cabinet in real time. The humidity sensor is used to monitor the humidity changes of the working environment of the phase control cabinet in real time. The electromagnetic field strength sensor is used to monitor the electromagnetic field strength in the working environment of the phase control cabinet in real time. The voltage and current sensor is used to monitor the voltage and current output of the phase control cabinet in real time.

[0014] In a preferred embodiment, the step of calculating the phase offset reference value of the collected electrical parameters in real time and performing step-by-step compensation on the voltage waveform and current waveform output by the phase control cabinet according to the phase offset reference value includes:

[0015] Performing Fourier transform on the electrical parameters to obtain spectrum information of the electrical parameters, and identifying fundamental wave components and harmonic wave components in the spectrum information;

[0016] According to the phase information of the fundamental component and the harmonic component, a weighted average calculation is performed to obtain a phase offset reference value;

[0017] Comparing the phase offset reference value with a preset phase threshold, and triggering a compensation mechanism and generating a compensation signal when the phase offset reference value exceeds the preset phase threshold range;

[0018] The compensation signal is decomposed into multiple compensation sub-signals, each compensation sub-signal corresponds to a different compensation level, and the voltage waveform and the current waveform are compensated in descending order of the compensation levels.

[0019] In a preferred embodiment, the step of matching the environmental parameters with a pre-stored interference signature library, identifying the interference source type, and quantifying the interference source into a thermal noise contribution value includes:

[0020] Normalize the environmental parameters including temperature, humidity and electromagnetic field strength to generate an environmental parameter vector;

[0021] Calculate the similarity between the environmental parameters and the interference feature vectors in the interference feature library, and identify and match the interference source type corresponding to the interference feature vector with the highest similarity;

[0022] Based on the interference characteristic vector under the interference source type and the corresponding interference intensity coefficient, the thermal noise contribution value under each interference source is calculated;

[0023] The thermal noise contribution values of each interference source are dynamically weighted and fused to generate a comprehensive thermal noise contribution value set.

[0024] In a preferred solution, the step of calculating the interference intensity prediction value within the prediction window based on the thermal noise contribution value includes:

[0025] Perform time series analysis on the historical thermal noise contribution value set and construct a mapping relationship between the historical thermal noise contribution value and the interference intensity;

[0026] A sliding window is used to traverse the current thermal noise contribution value set to extract the trend component and random component of the thermal noise contribution value in the current window;

[0027] Make a preliminary prediction of the interference intensity within the prediction window based on the trend component and output it as the interference intensity prediction benchmark value;

[0028] The confidence interval of the interference intensity prediction benchmark value is adjusted in real time based on the variance of the random component, and the interference intensity prediction benchmark value is corrected according to the confidence interval to obtain the interference intensity prediction value.

[0029] In a preferred embodiment, the step of collecting the actual phase information of the voltage waveform and the current waveform after step-by-step compensation in real time, comparing them with the preset ideal phase information, and outputting the phase residual includes:

[0030] Extract characteristic points of the voltage and current waveforms after step-by-step compensation, including peaks, troughs, and zero-crossing points;

[0031] Perform phase measurement on the characteristic points of the voltage and current waveforms to obtain actual phase information;

[0032] Compare the actual phase information with the preset ideal phase information point by point to calculate the phase deviation;

[0033] The phase deviation is accumulated to obtain the phase residual, and a phase residual sequence is output.

[0034] In a preferred embodiment, the step of calculating the dynamic error coefficient based on the phase residual in combination with the interference intensity prediction value, and determining the compensation gain parameter based on the dynamic error coefficient includes:

[0035] Perform sliding window averaging on the phase residual sequence to remove abnormal fluctuation points and obtain a smoothed phase residual sequence;

[0036] The smoothed phase residual sequence is aligned with the interference intensity prediction value in the time domain, and correlation analysis is performed to obtain the dynamic error coefficient;

[0037] The dynamic error coefficient is input into the preset gain parameter mapping table, the corresponding compensation gain parameter value is found, and it is verified whether the compensation gain parameter meets the convergence requirements. If not, the compensation gain parameter is iteratively optimized until the convergence requirements are met. Otherwise, the determined compensation gain parameter is directly output.

[0038] In a preferred embodiment, the step of reversely adjusting the sampling frequency of the sensor array and compensating the weight factor according to the phase convergence information includes:

[0039] Extracting the phase residual decay rate, steady-state deviation and fluctuation amplitude from the phase convergence information;

[0040] The attenuation rate is compared with a preset attenuation threshold. When the attenuation rate is lower than the attenuation threshold, the sampling frequency of the sensor array is increased proportionally. Otherwise, the sampling frequency is kept unchanged. After the phase convergence is stable, the sampling frequency of the sensor array is gradually reduced.

[0041] The steady-state deviation is input into the weight adjustment coefficient calculation function, and the output result of the weight adjustment coefficient calculation function is recorded as the compensation weight factor adjustment amount, and the compensation weight factor is adjusted in real time according to the compensation weight factor adjustment amount;

[0042] Compare the current fluctuation amplitude with the historical fluctuation record. If the current fluctuation amplitude exceeds the historical average level, the anomaly detection mechanism is triggered and the sensor array is troubleshooted. Otherwise, the current compensation weight factor is maintained unchanged and the phase convergence information is continued to be monitored.

[0043] The adjusted sampling frequency and compensation weight factor of the sensor array are synchronously fed back to the calculation process of the phase offset reference value.

[0044] The present invention also provides a phase control cabinet control system based on multi-level phase compensation, using the above-mentioned phase control cabinet control method based on multi-level phase compensation, including:

[0045] Parameter acquisition module, used to obtain electrical parameters and environmental parameters in the working environment of the phase control cabinet through a pre-deployed sensor array;

[0046] The first-level compensation module is used to calculate the phase offset reference value of the collected electrical parameters in real time, and to compensate the voltage and current waveforms output by the phase control cabinet step by step according to the phase offset reference value;

[0047] The interference prediction module is used to match environmental parameters with the pre-stored interference feature library, identify the type of interference source, quantify it into a thermal noise contribution value, and calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value;

[0048] Phase residual calculation module, used to collect the actual phase information of the voltage waveform and current waveform after step-by-step compensation in real time, compare it with the preset ideal phase information, and output the phase residual;

[0049] The secondary compensation module is used to calculate the dynamic error coefficient based on the phase residual and the interference intensity prediction value, determine the compensation gain parameter based on the dynamic error coefficient, and compensate the voltage waveform and current waveform again according to the compensation gain parameter to make the phase deviation converge to a preset range;

[0050] The compensation feedback module is used to collect the phase convergence information after re-compensation, and reversely adjust the sampling frequency and compensation weight factor of the sensor array based on the phase convergence information to form a closed-loop compensation control link.

[0051] And, an electronic device, comprising:

[0052] at least one processor;

[0053] and a memory communicatively coupled to the at least one processor;

[0054] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned phase control cabinet control method based on multi-stage phase compensation.

[0055] The technical effects achieved by the present invention are:

[0056] The present invention calculates the phase offset reference value in real time through the collected electrical parameters, and performs preliminary compensation on the voltage waveform and current waveform output by the phase control cabinet, effectively reducing the initial phase deviation. It also uses environmental parameters to match with the pre-stored interference feature library, can identify the type of interference source, and quantify it as a thermal noise contribution value, and calculates the interference intensity prediction value within the prediction window based on the thermal noise contribution value, providing an effective reference for subsequent dynamic compensation. Then, the output phase residual provides key data support for the secondary compensation. When the secondary compensation is executed, the compensation gain parameter is determined according to the dynamic error coefficient, and the voltage waveform and current waveform are compensated again, so that the phase deviation is further converged to the preset range, which significantly improves the control accuracy and stability of the phase control cabinet. Finally, according to the phase convergence information after the second compensation, the sampling frequency and compensation weight factor of the sensor array are reversely adjusted to form a closed-loop compensation control link, realizing continuous optimization and improvement of the compensation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic flow chart of the method of the present invention;

[0058] Figure 2 It is a schematic diagram of the system modules of the present invention;

[0059] Figure 3 It is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive of other embodiments.

[0063] See also Figure 1 As shown, the present invention provides a phase control cabinet control method based on multi-level phase compensation, including:

[0064] S1. Obtain electrical parameters and environmental parameters in the working environment of the phase control cabinet through the pre-deployed sensor array;

[0065] In step S1, before performing phase control on the phase control cabinet, it is necessary to pre-deploy a sensor array and collect electrical parameters and environmental parameters during the operation of the phase control cabinet based on the sensor array. The electrical parameters include but are not limited to voltage, current, frequency, etc., and the environmental parameters include but are not limited to temperature, humidity, electromagnetic interference intensity, etc., which serve as basic data for subsequent phase compensation and interference prediction, so as to improve the accuracy and adaptability of phase control. The sensor array includes a temperature sensor, a humidity sensor, an electromagnetic field strength sensor, and a voltage and current sensor. The temperature sensor is used to monitor the temperature changes of the working environment of the phase control cabinet in real time, the humidity sensor is used to monitor the humidity changes of the working environment of the phase control cabinet in real time, the electromagnetic field strength sensor is used to monitor the electromagnetic field intensity in the working environment of the phase control cabinet in real time, and the voltage and current sensor is used to monitor the voltage and current output of the phase control cabinet in real time.

[0066] S2. Calculate the phase offset reference value of the collected electrical parameters in real time, and compensate the voltage waveform and current waveform output by the phase control cabinet step by step according to the phase offset reference value;

[0067] In step S2, after the electrical parameters and environmental parameters of the phase control cabinet are collected, the phase offset reference value of the collected electrical parameters is calculated in real time, and based on the phase offset reference value, the voltage waveform and the current waveform output by the phase control cabinet are phase compensated step by step to preliminarily correct the phase deviation. The step of calculating the phase offset reference value of the collected electrical parameters in real time and compensating the voltage waveform and the current waveform output by the phase control cabinet step by step based on the phase offset reference value includes:

[0068] Performing Fourier transform on the electrical parameters to obtain spectrum information of the electrical parameters, and identifying fundamental wave components and harmonic wave components in the spectrum information;

[0069] According to the phase information of the fundamental component and the harmonic component, a weighted average calculation is performed to obtain a phase offset reference value;

[0070] Comparing the phase offset reference value with a preset phase threshold, and triggering a compensation mechanism and generating a compensation signal when the phase offset reference value exceeds the preset phase threshold range;

[0071] Decomposing the compensation signal into multiple compensation sub-signals, each compensation sub-signal corresponds to a different compensation level, and compensating the voltage waveform and the current waveform in descending order of compensation levels;

[0072] Specifically, when compensating the voltage and current waveforms output by the phase control cabinet, the collected electrical parameters are first subjected to Fourier transform processing to obtain spectral information of the electrical parameters. Based on the obtained spectral information, the fundamental component and each harmonic component are identified and distinguished. Then, a weighted average calculation is performed based on the phase information of the identified fundamental and harmonic components to calculate a phase offset reference value, which serves as a reference for subsequent compensation operations. The calculated phase offset reference value is then compared with a preset phase threshold. Once it is found that the phase offset reference value exceeds the preset phase threshold range, the corresponding compensation mechanism is immediately triggered and a corresponding compensation signal is generated accordingly. Finally, the compensation signal is further decomposed into multiple compensation sub-signals, each of which corresponds to a different compensation level. The voltage and current waveforms output by the phase control cabinet are compensated step by step in descending order of compensation level to ensure that the quality and stability of the waveforms are improved.

[0073] S3. Match the environmental parameters with the pre-stored interference feature library, identify the interference source type, and quantify it into a thermal noise contribution value, and calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value;

[0074] In step S3, after the environmental parameters are output, they are matched with a pre-stored interference feature library to identify the specific interference source type and quantify it into a thermal noise contribution value. Then, based on the thermal noise contribution value, the interference intensity prediction value within the prediction window is calculated to provide a basis for subsequent compensation. The steps of matching the environmental parameters with the pre-stored interference feature library, identifying the interference source type, and quantifying it into a thermal noise contribution value include:

[0075] Normalize the environmental parameters including temperature, humidity and electromagnetic field strength to generate an environmental parameter vector;

[0076] Calculate the similarity between the environmental parameters and the interference feature vectors in the interference feature library, and identify and match the interference source type corresponding to the interference feature vector with the highest similarity;

[0077] Based on the interference characteristic vector under the interference source type and the corresponding interference intensity coefficient, the thermal noise contribution value under each interference source is calculated;

[0078] Dynamically weight and fuse the thermal noise contribution values of each interference source to generate a comprehensive thermal noise contribution value set;

[0079] Specifically, first, the temperature, humidity and electromagnetic field strength covered in the environmental parameters are normalized to ensure the comparability and consistency of the data. That is, the numerical range of each parameter is unified to a standard scale, and the corresponding environmental parameter vector is generated. The generated environmental parameter vector is then similarly calculated with each interference feature vector in the pre-established interference feature library (specifically, this can be achieved through algorithms such as cosine similarity and Euclidean distance). The degree of similarity between the environmental parameter vector and the interference feature vector in the interference feature library is compared one by one, and finally the interference feature vector with the highest similarity is identified and matched, and then the interference source type corresponding to the interference feature vector is synchronously determined. After the interference source type is determined, the interference feature vector under this interference source type and the corresponding interference intensity coefficient are combined and calculated to obtain the thermal noise contribution value generated by each interference source in the current environment ( , where represents the thermal noise contribution, Indicates the The interference intensity coefficient of the interference source is predefined by the feature library. Indicates the The weight of the class environment parameter, Indicates the The first The normalized parameter values of the parameters, Indicates the The first In order to more comprehensively evaluate the thermal noise level in the environment, the thermal noise contribution values of various interference sources will be dynamically weighted and fused. Different weights will be assigned to each interference source according to its actual situation and impact. Through methods such as weighted averaging, a comprehensive set of thermal noise contribution values will be generated to reflect the combined effects of multiple interference sources.

[0080] Secondly, the step of calculating the interference intensity prediction value within the prediction window based on the thermal noise contribution value includes:

[0081] Perform time series analysis on the historical thermal noise contribution value set and construct a mapping relationship between the historical thermal noise contribution value and the interference intensity;

[0082] A sliding window is used to traverse the current thermal noise contribution value set to extract the trend component and random component of the thermal noise contribution value in the current window;

[0083] Make a preliminary prediction of the interference intensity within the prediction window based on the trend component and output it as the interference intensity prediction benchmark value;

[0084] The confidence interval of the interference intensity prediction benchmark value is adjusted in real time based on the variance of the random component, and the interference intensity prediction benchmark value is corrected according to the confidence interval to obtain the interference intensity prediction value;

[0085] In the above, when determining the interference intensity prediction value, it is first necessary to perform a corresponding time series analysis on the existing historical thermal noise contribution value set (specifically, this can be achieved through time series prediction methods such as ARIMA model and LSTM network), so as to construct a mapping relationship between the historical thermal noise contribution value and the actual interference intensity. Then, the sliding window technology is used to comprehensively traverse the current thermal noise contribution value set, and the trend component and random component of the thermal noise contribution value in the current window are extracted to ensure the grasp of the current thermal noise situation. Then, based on the extracted trend component, a preliminary prediction of the interference intensity in the prediction window is made, and this prediction result is output as the benchmark value for the interference intensity prediction ( , where represents the interference intensity prediction benchmark value, Represents the trend component of the thermal noise contribution value in the current sliding window, represents the rate of change of the trend component, Represents the time span of the prediction window), and then based on the variance of the random component, the confidence interval of the interference intensity prediction benchmark value is adjusted in real time to ensure the reliability of the prediction result, and the interference intensity prediction benchmark value is corrected accordingly based on this confidence interval, so that a more accurate interference intensity prediction value can be obtained ( , where represents the variance of the random component in the current window, Indicates the quantile of the standard normal distribution corresponding to the confidence level a, represents the corrected interference intensity prediction value, specifically expressed in the form of an interval), thereby ensuring the accuracy and practicality of the interference intensity prediction results.

[0086] S4, collecting the actual phase information of the voltage waveform and current waveform after step-by-step compensation in real time, comparing it with the preset ideal phase information, and outputting the phase residual;

[0087] In step S4, the actual phase information of the voltage waveform and the current waveform after step-by-step compensation is collected in real time, and compared in detail with the preset ideal phase information, and the phase residual is output to accurately evaluate the compensation effect. The steps of collecting the actual phase information of the voltage waveform and the current waveform after step-by-step compensation in real time, comparing it with the preset ideal phase information, and outputting the phase residual include:

[0088] Extract characteristic points of the voltage and current waveforms after step-by-step compensation, including peaks, troughs, and zero-crossing points;

[0089] Perform phase measurement on the characteristic points of the voltage and current waveforms to obtain actual phase information;

[0090] Compare the actual phase information with the preset ideal phase information point by point to calculate the phase deviation;

[0091] Accumulate the phase deviation to obtain the phase residual and output the phase residual sequence;

[0092] Specifically, after determining the phase residual after initial compensation, the key characteristic points in the voltage waveform and current waveform after step-by-step compensation are first extracted, including the peak point (peak), valley point (trough) and point where the waveform crosses zero value (zero crossing point) of the waveform. Then, corresponding phase measurements are performed on each characteristic point of the extracted voltage waveform and current waveform to obtain actual phase information. The actual phase information obtained is then compared point by point with the pre-set ideal phase information. The phase deviation of each corresponding point can be obtained through comparative calculation. Finally, all calculated phase deviations are accumulated so that the overall phase residual can be output, and this phase residual sequence can be output.

[0093] S5. Calculate a dynamic error coefficient based on the phase residual and the interference intensity prediction value, determine a compensation gain parameter based on the dynamic error coefficient, and compensate the voltage waveform and the current waveform again based on the compensation gain parameter so that the phase deviation converges to a preset range;

[0094] In step S5, after the phase residual after the initial compensation is output, the dynamic error coefficient is calculated in combination with the interference intensity prediction value, and an adaptive compensation gain parameter is determined based on the dynamic error coefficient. Then, the voltage waveform and the current waveform are compensated again based on the compensation gain parameter to ensure that the phase deviation can effectively converge to a preset range. The steps of calculating the dynamic error coefficient based on the phase residual in combination with the interference intensity prediction value and determining the compensation gain parameter based on the dynamic error coefficient include:

[0095] Perform sliding window averaging on the phase residual sequence to remove abnormal fluctuation points and obtain a smoothed phase residual sequence;

[0096] The smoothed phase residual sequence is aligned with the interference intensity prediction value in the time domain, and correlation analysis is performed to obtain the dynamic error coefficient;

[0097] Input the dynamic error coefficient into the preset gain parameter mapping table, find the corresponding compensation gain parameter value, and verify whether the compensation gain parameter meets the convergence requirement. If not, iteratively optimize the compensation gain parameter until the convergence requirement is met. Otherwise, directly output the determined compensation gain parameter.

[0098] Specifically, first, for the phase residual sequence, the sliding window averaging method is used to perform segment-by-segment averaging calculation on the data in the phase residual sequence, and the abnormal fluctuation points caused by accidental factors are eliminated to obtain a relatively smooth phase residual sequence. Then, the smoothed phase residual sequence and the interference intensity prediction value are aligned in the time domain to ensure the consistency of the two in the time dimension. On this basis, the correlation analysis of the smoothed phase residual sequence and the interference intensity prediction value is performed to obtain the dynamic error coefficient between the two ( , where Represents the dynamic error coefficient. The larger the absolute value, the more significant the impact of interference on the phase residual. represents the smoothed phase residual sequence, represents the covariance between the phase residual sequence and the interference intensity prediction value, represents the standard deviation of the smoothed phase residual sequence, Represents the standard deviation of the interference intensity prediction value), and then inputs the output dynamic error coefficient into a preset gain parameter mapping table. By looking up the corresponding relationship in the mapping table, the preliminary compensation gain parameter value is determined. In order to ensure the effectiveness of the compensation effect, the determined compensation gain parameter needs to be verified for convergence. If the verification result shows that the compensation gain parameter does not meet the convergence requirements, it needs to be iteratively optimized by continuously adjusting the parameter value until the convergence requirements are met. Conversely, if the compensation gain parameter meets the convergence requirements during the initial verification, the determined compensation gain parameter can be directly output for subsequent phase compensation operations, thereby improving compensation accuracy and efficiency.

[0099] S6. Collecting the phase convergence information after the recompensation, and reversely adjusting the sampling frequency and compensation weight factor of the sensor array according to the phase convergence information to form a closed-loop compensation control link;

[0100] In step S6, the phase convergence information after re-compensation is collected, and the sampling frequency and compensation weight factor of the sensor array are reversely adjusted based on this information to form a closed-loop compensation control link, thereby continuously improving the control accuracy and stability of the phase control cabinet. The step of reversely adjusting the sampling frequency and compensation weight factor of the sensor array based on the phase convergence information includes:

[0101] Extracting the phase residual decay rate, steady-state deviation and fluctuation amplitude from the phase convergence information;

[0102] The attenuation rate is compared with a preset attenuation threshold. When the attenuation rate is lower than the attenuation threshold, the sampling frequency of the sensor array is increased proportionally. Otherwise, the sampling frequency is kept unchanged. After the phase convergence is stable, the sampling frequency of the sensor array is gradually reduced.

[0103] The steady-state deviation is input into the weight adjustment coefficient calculation function, and the output result of the weight adjustment coefficient calculation function is recorded as the compensation weight factor adjustment amount, and the compensation weight factor is adjusted in real time according to the compensation weight factor adjustment amount;

[0104] Compare the current fluctuation amplitude with the historical fluctuation record. If the current fluctuation amplitude exceeds the historical average level, the anomaly detection mechanism is triggered and the sensor array is troubleshooted. Otherwise, the current compensation weight factor is maintained unchanged and the phase convergence information is continued to be monitored.

[0105] The adjusted sampling frequency and compensation weight factor of the sensor array are synchronously fed back to the calculation process of the phase offset reference value;

[0106] Specifically, it is first necessary to accurately extract key parameters such as the attenuation rate, steady-state deviation, and fluctuation amplitude of the phase residual from the phase convergence information, and then compare the extracted attenuation rate with the pre-set attenuation threshold. If the attenuation rate is lower than the attenuation threshold, it means that the current phase convergence speed is slow. At this time, it is necessary to proportionally increase the sampling frequency of the sensor array to increase the density of data acquisition, thereby accelerating the phase convergence process and improving the control efficiency. Conversely, if the attenuation rate is not lower than the attenuation threshold, the current sampling frequency is kept unchanged, and after the phase convergence reaches a stable state, the sampling frequency of the sensor array is gradually and slowly reduced to optimize resource consumption and extend the service life of the equipment. At the same time, the steady-state deviation is input into the pre-set weight adjustment coefficient measurement function, and the calculated output result is recorded as the adjustment amount of the compensation weight factor, where the expression of the weight adjustment coefficient measurement function is:

[0107] ;

[0108] Where, represents the compensation weight factor adjustment amount, and Respectively represent the proportional adjustment coefficient and the integral adjustment coefficient, wherein the proportional adjustment coefficient is used to directly respond to the current deviation, and the integral adjustment coefficient is used to respond to the impact of the accumulated historical deviation. Indicates the steady-state deviation;

[0109] Then, the current compensation weight factor is adjusted in real time according to the compensation weight factor adjustment amount to ensure the accuracy and stability of the compensation process. In addition, the current fluctuation amplitude is comprehensively compared and analyzed with the historical fluctuation records. If the current fluctuation amplitude significantly exceeds the historical average level, the abnormality detection mechanism is immediately triggered, and a comprehensive troubleshooting of the sensor array is carried out to eliminate potential problems. Conversely, if the current fluctuation amplitude is within the normal range, the current compensation weight factor is maintained unchanged, and the phase convergence information is continuously monitored to promptly detect and respond to possible abnormal situations. In this process, the adjusted sensor array sampling frequency and compensation weight factor will be synchronously fed back to the calculation process of the phase offset reference value in real time. On this basis, the phase compensation strategy is continuously optimized to achieve more precise phase control.

[0110] See also Figure 2 The phase control cabinet control system based on multi-level phase compensation uses the above-mentioned phase control cabinet control method based on multi-level phase compensation, including:

[0111] Parameter acquisition module, used to obtain electrical parameters and environmental parameters in the working environment of the phase control cabinet through a pre-deployed sensor array;

[0112] The first-level compensation module is used to calculate the phase offset reference value of the collected electrical parameters in real time, and to compensate the voltage and current waveforms output by the phase control cabinet step by step according to the phase offset reference value;

[0113] The interference prediction module is used to match environmental parameters with the pre-stored interference feature library, identify the type of interference source, quantify it into a thermal noise contribution value, and calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value;

[0114] Phase residual calculation module, used to collect the actual phase information of the voltage waveform and current waveform after step-by-step compensation in real time, compare it with the preset ideal phase information, and output the phase residual;

[0115] The secondary compensation module is used to calculate the dynamic error coefficient based on the phase residual and the interference intensity prediction value, determine the compensation gain parameter based on the dynamic error coefficient, and compensate the voltage waveform and current waveform again according to the compensation gain parameter to make the phase deviation converge to a preset range;

[0116] The compensation feedback module is used to collect the phase convergence information after re-compensation, and reversely adjust the sampling frequency and compensation weight factor of the sensor array based on the phase convergence information to form a closed-loop compensation control link.

[0117] In the above, the parameter acquisition module comprehensively and accurately obtains various electrical parameters and environmental parameters of the phase control cabinet in the actual working environment through the pre-deployed sensor array to ensure the comprehensiveness and accuracy of the data. The first-level compensation module is responsible for calculating the phase offset reference value of the collected electrical parameters in real time, and based on the phase offset reference value, compensates the voltage waveform and current waveform output by the phase control cabinet step by step to preliminarily correct the phase deviation. The interference prediction module accurately matches the collected environmental parameters with the pre-stored interference feature library, identifies the specific type of interference source, and quantifies it as a thermal noise contribution value. Then, based on the thermal noise contribution value, the interference intensity prediction value within the prediction window is calculated to provide a basis for subsequent secondary compensation. The phase residual calculation module is responsible for calculating the phase offset reference value of the collected electrical parameters in real time, and compensates the voltage waveform and current waveform output by the phase control cabinet step by step to preliminarily correct the phase deviation. The interference prediction module accurately matches the collected environmental parameters with the pre-stored interference feature library, identifies the specific type of interference source, and quantifies it as a thermal noise contribution value. Then, based on the thermal noise contribution value, the interference intensity prediction value within the prediction window is calculated to provide a basis for subsequent secondary compensation. The actual phase information of the voltage waveform and current waveform after step-by-step compensation is collected and compared with the preset ideal phase information, and finally the phase residual data is output. The secondary compensation module calculates the dynamic error coefficient based on the phase residual data and the interference intensity prediction value, and determines the compensation gain parameter based on this dynamic error coefficient. Then, the voltage waveform and current waveform are subjected to more detailed secondary compensation according to the compensation gain parameter to ensure that the phase deviation can effectively converge to the preset range. The compensation feedback module is responsible for collecting the phase convergence information after the second compensation, and according to the phase convergence information, reversely adjusts the sampling frequency and compensation weight factor of the sensor array, thereby forming a closed-loop compensation control link to ensure the stability and efficiency of the entire control system.

[0118] See also Figure 3 , an electronic device, the electronic device comprising:

[0119] at least one processor;

[0120] and a memory communicatively coupled to the at least one processor;

[0121] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned phase control cabinet control method based on multi-stage phase compensation.

[0122] The processor of the above-mentioned electronic device can be a chip with data processing capabilities such as a central processing unit (CPU), a microprocessor (MCU), a digital signal processor (DSP) or a field programmable gate array (FPGA). The memory can be a storage device such as random access memory (RAM), read-only memory (ROM), flash memory (Flash) or a hard disk. The electronic device also includes other necessary components that communicate with the processor and memory, such as an arithmetic unit, input and output interfaces, a communication module, a power module, etc. The arithmetic unit can be an arithmetic logic unit (ALU), which is responsible for performing various arithmetic and logical operations. The input and output interfaces are used to connect external devices such as keyboards, displays, sensors, etc. to realize data input and output. The communication module supports wired or wireless communication methods, enabling the electronic device to transmit and interact with other devices or networks. The power module is responsible for providing a stable and reliable power supply for the electronic device to ensure that the electronic device can operate continuously and stably.

[0123] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0124] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A phase control cabinet control method based on multi-level phase compensation, characterized by: include: The electrical and environmental parameters of the phase control cabinet working environment are acquired through the pre-deployed sensor array; Calculate the phase offset reference value of the collected electrical parameters in real time, and compensate the voltage and current waveforms output by the phase control cabinet step by step according to the phase offset reference value; Match the environmental parameters with the pre-stored interference feature library, identify the interference source type, and quantify it into a thermal noise contribution value. Calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value; The actual phase information of the voltage and current waveforms after step-by-step compensation is collected in real time, compared with the preset ideal phase information, and the phase residual is output; The dynamic error coefficient is calculated based on the phase residual and the interference intensity prediction value, and the compensation gain parameter is determined based on the dynamic error coefficient. The voltage waveform and the current waveform are compensated again based on the compensation gain parameter so that the phase deviation converges to a preset range. Collect the phase convergence information after the second compensation, and reversely adjust the sampling frequency and compensation weight factor of the sensor array based on the phase convergence information to form a closed-loop compensation control link; The step of matching the environmental parameters with a pre-stored interference signature library, identifying the interference source type, and quantifying the interference source into a thermal noise contribution value includes: Normalize the environmental parameters including temperature, humidity and electromagnetic field strength to generate an environmental parameter vector; Calculate the similarity between the environmental parameters and the interference feature vectors in the interference feature library, and identify and match the interference source type corresponding to the interference feature vector with the highest similarity; Based on the interference characteristic vector under the interference source type and the corresponding interference intensity coefficient, the thermal noise contribution value under each interference source is calculated; Dynamically weight and fuse the thermal noise contribution values of each interference source to generate a comprehensive thermal noise contribution value set; The step of reversely adjusting the sampling frequency of the sensor array and compensating the weight factor according to the phase convergence information includes: Extracting the phase residual decay rate, steady-state deviation and fluctuation amplitude from the phase convergence information; The attenuation rate is compared with a preset attenuation threshold. When the attenuation rate is lower than the attenuation threshold, the sampling frequency of the sensor array is increased proportionally. Otherwise, the sampling frequency is kept unchanged. After the phase convergence is stable, the sampling frequency of the sensor array is gradually reduced. The steady-state deviation is input into the weight adjustment coefficient calculation function, and the output result of the weight adjustment coefficient calculation function is recorded as the compensation weight factor adjustment amount, and the compensation weight factor is adjusted in real time according to the compensation weight factor adjustment amount; Compare the current fluctuation amplitude with the historical fluctuation record. If the current fluctuation amplitude exceeds the historical average level, the anomaly detection mechanism is triggered and the sensor array is troubleshooted. Otherwise, the current compensation weight factor is maintained unchanged and the phase convergence information is continued to be monitored. The adjusted sampling frequency and compensation weight factor of the sensor array are synchronously fed back to the calculation process of the phase offset reference value.

2. The phase control cabinet control method based on multi-level phase compensation according to claim 1 is characterized in that: The sensor array includes a temperature sensor, a humidity sensor, an electromagnetic field strength sensor, and a voltage and current sensor. The temperature sensor is used to monitor the temperature changes of the phase control cabinet working environment in real time. The humidity sensor is used to monitor the humidity changes of the phase control cabinet working environment in real time. The electromagnetic field strength sensor is used to monitor the electromagnetic field strength in the phase control cabinet working environment in real time. The voltage and current sensors are used to monitor the voltage and current output by the phase control cabinet in real time.

3. The phase control cabinet control method based on multi-level phase compensation according to claim 1 is characterized in that: The step of calculating the phase offset reference value of the collected electrical parameters in real time and performing step-by-step compensation on the voltage waveform and current waveform output by the phase control cabinet according to the phase offset reference value includes: Performing Fourier transform on the electrical parameters to obtain spectrum information of the electrical parameters, and identifying fundamental wave components and harmonic wave components in the spectrum information; According to the phase information of the fundamental component and the harmonic component, a weighted average calculation is performed to obtain a phase offset reference value; Comparing the phase offset reference value with a preset phase threshold, and triggering a compensation mechanism and generating a compensation signal when the phase offset reference value exceeds the preset phase threshold range; The compensation signal is decomposed into multiple compensation sub-signals, each compensation sub-signal corresponds to a different compensation level, and the voltage waveform and the current waveform are compensated in descending order of the compensation levels.

4. The phase control cabinet control method based on multi-stage phase compensation according to claim 1 is characterized in that: The step of calculating the interference intensity prediction value within the prediction window based on the thermal noise contribution value includes: Perform time series analysis on the historical thermal noise contribution value set and construct a mapping relationship between the historical thermal noise contribution value and the interference intensity; A sliding window is used to traverse the current thermal noise contribution value set to extract the trend component and random component of the thermal noise contribution value in the current window; Make a preliminary prediction of the interference intensity within the prediction window based on the trend component and output it as the interference intensity prediction benchmark value; The confidence interval of the interference intensity prediction benchmark value is adjusted in real time based on the variance of the random component, and the interference intensity prediction benchmark value is corrected according to the confidence interval to obtain the interference intensity prediction value.

5. The phase control cabinet control method based on multi-level phase compensation according to claim 1 is characterized in that: The step of collecting the actual phase information of the voltage waveform and the current waveform after step-by-step compensation in real time, comparing it with the preset ideal phase information, and outputting the phase residual includes: Extract characteristic points of the voltage and current waveforms after step-by-step compensation, including peaks, troughs, and zero-crossing points; Perform phase measurement on the characteristic points of the voltage and current waveforms to obtain actual phase information; Compare the actual phase information with the preset ideal phase information point by point to calculate the phase deviation; The phase deviation is accumulated to obtain the phase residual, and a phase residual sequence is output.

6. The phase control cabinet control method based on multi-stage phase compensation according to claim 5 is characterized in that: The step of calculating the dynamic error coefficient based on the phase residual in combination with the interference intensity prediction value, and determining the compensation gain parameter based on the dynamic error coefficient includes: Perform sliding window averaging on the phase residual sequence to remove abnormal fluctuation points and obtain a smoothed phase residual sequence; The smoothed phase residual sequence is aligned with the interference intensity prediction value in the time domain, and correlation analysis is performed to obtain the dynamic error coefficient; The dynamic error coefficient is input into the preset gain parameter mapping table, the corresponding compensation gain parameter value is found, and it is verified whether the compensation gain parameter meets the convergence requirements. If not, the compensation gain parameter is iteratively optimized until the convergence requirements are met. Otherwise, the determined compensation gain parameter is directly output.

7. The phase control cabinet control system based on multi-level phase compensation is characterized by: The phase control cabinet control method based on multi-stage phase compensation according to any one of claims 1 to 6 comprises: Parameter acquisition module, used to obtain electrical parameters and environmental parameters in the working environment of the phase control cabinet through a pre-deployed sensor array; The first-level compensation module is used to calculate the phase offset reference value of the collected electrical parameters in real time, and to compensate the voltage and current waveforms output by the phase control cabinet step by step according to the phase offset reference value; The interference prediction module is used to match environmental parameters with the pre-stored interference feature library, identify the type of interference source, quantify it into a thermal noise contribution value, and calculate the interference intensity prediction value within the prediction window based on the thermal noise contribution value; Phase residual calculation module, used to collect the actual phase information of the voltage waveform and current waveform after step-by-step compensation in real time, compare it with the preset ideal phase information, and output the phase residual; The secondary compensation module is used to calculate the dynamic error coefficient based on the phase residual and the interference intensity prediction value, determine the compensation gain parameter based on the dynamic error coefficient, and compensate the voltage waveform and current waveform again according to the compensation gain parameter to make the phase deviation converge to a preset range; The compensation feedback module is used to collect the phase convergence information after re-compensation, and reversely adjust the sampling frequency and compensation weight factor of the sensor array based on the phase convergence information to form a closed-loop compensation control link.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the phase control cabinet control method based on multi-stage phase compensation according to any one of claims 1 to 6.

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