Micro-grid zero-carbon operation control method and system based on street lamp network

Abstract

The invention relates to the technical field of micro-grid control, and provides a micro-grid zero-carbon operation control method and system based on a street lamp network, and the method comprises the steps: estimating a temperature rise value and an expected voltage drop value of a cable in combination with a current, a local environment temperature and a preset cable physical parameter; comparing the voltage drop value of the street lamp unit with an expected voltage drop value, and judging whether the voltage drop is caused by a high-load heat effect or not by combining the temperature rise value of the cable, so as to obtain a voltage drop judgment result; if the voltage drop judgment result is yes, the regional control center takes adjustment measures to relieve the voltage drop; continuously monitoring the operation state of the street lamp unit, and if the output voltage recovers to be stable, continuing normal operation; and when the output voltage in the preset time window after the adjustment measures are taken is not recovered to be stable, or it is confirmed that equipment hard faults exist, isolation operation is executed. The method has the effect of improving the operation reliability of the micro-grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid control, and particularly relates to a micro-grid zero-carbon operation control method and system based on a streetlight network. BACKGROUND

[0002] In modern urban development, intelligent power grid construction is gradually deepening, and the streetlight network of urban roads is being transformed into a micro-grid system integrated with photovoltaic power generation devices, energy storage units, environmental perception devices, and local data processing nodes, aiming to achieve energy self-sufficiency and carbon emission balance in the region.

[0003] However, in actual operation, due to the complexity of urban environment and the unpredictability of emergencies, these micro-grid systems face unique challenges in energy scheduling and fault judgment. In particular, when large-scale energy transmission is needed to respond to emergencies, the existing system lacks sufficient perception of the state of physical transmission links, which can lead to false judgments and unnecessary system interruptions.

[0004] In order to protect the stability of the entire micro-grid system, a safety protocol will automatically execute, disconnecting these misjudged streetlight units that are actually the most contributing to the power grid. This protective action cuts off the key power source, causing the originally precarious regional power grid to lose external support, and the internal energy storage batteries to quickly run out under the huge charging load, ultimately leading to the power outage of several charging stations in the region, and even some streetlights are extinguished due to power depletion. In the process of trying to solve a problem, the entire intelligent system, due to the limitations of the perception of the physical world state, has created a bigger problem.

[0005] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0006] The present application discloses a micro-grid zero-carbon operation control method and system based on a streetlight network, aiming to solve the problem of misjudgment and unnecessary system interruption caused by insufficient perception of the state of physical transmission links in the energy scheduling and fault judgment of the existing streetlight network micro-grid system.

[0007] The technical solution of the present application is as follows: In a first aspect, the present application discloses a micro-grid zero-carbon operation control method based on a streetlight network, comprising the following steps: monitoring the output voltage and current of the streetlight unit; when the output voltage drops to a preset threshold, triggering an abnormal scenario analysis; In the process of abnormal scenario analysis, the temperature rise value of the cable and the expected voltage drop value are estimated in combination with the current, the local environmental temperature, and the preset cable physical parameters; The voltage drop of the street lamp unit is compared with an expected voltage drop, and in combination with a temperature rise of the cable, it is determined whether the voltage drop is caused by high load thermal effect, to obtain a voltage drop determination result; If the voltage drop determination result is yes, the regional control center takes adjustment measures to alleviate the voltage drop; The operating state of the street lamp unit is continuously monitored, and if the output voltage recovers to be stable, the normal operation is continued; When the output voltage does not recover to be stable within a preset time window after taking the adjustment measures, or it is confirmed that there is a device hard failure, an isolation operation is performed.

[0008] Through the technical solution, the voltage drop caused by high load thermal effect and the device hard failure can be effectively distinguished, the system interruption caused by misjudgment is avoided, and the operation stability and reliability of the micro-grid are improved.

[0009] Further, in the process of abnormal scenario analysis, in combination with the current, the local environment temperature and the preset cable physical parameters, the steps of estimating the temperature rise of the cable and the expected voltage drop value include: During the low load or stable load operation of the street lamp unit, a micro disturbance is applied to the corresponding cable by the edge computing device; Based on the micro disturbance, the transient response of the voltage at both ends of the cable and the transient response of the current through the cable are monitored; The characteristics of the transient response are analyzed, and the resistivity and heat capacity parameters of the cable are reversely deduced; According to the resistivity and heat capacity parameters, the cable characteristic data stored internally is updated; When the output voltage drop reaches a preset threshold, abnormal scenario analysis is started, and in combination with the current, the local environment temperature and the updated cable characteristic data, the temperature rise of the cable and the expected voltage drop value are estimated.

[0010] Through the technical solution, the physical characteristic data of the cable can be dynamically updated, so that the estimation of the temperature rise of the cable and the expected voltage drop value is more accurate, and the accuracy of the abnormal scenario analysis is improved.

[0011] More specifically, in some embodiments, the step of analyzing the characteristics of the transient response and reversely deducing the resistivity and heat capacity parameters of the cable includes: Before applying the micro disturbance, the voltage at both ends of the cable and the current through the cable are sampled for baseline noise to obtain environmental noise characteristics; When monitoring the transient response, the transient response is compared with the environmental noise characteristics, and the environmental noise component is identified and filtered out to obtain a filtered transient response signal; The characteristics of the filtered transient response signal are analyzed, and the resistivity and heat capacity parameters of the cable are reversely deduced.

[0012] By the technical solution, the influence of environmental noise on the transient response signal can be effectively filtered out, the accuracy of cable parameter derivation is ensured, and the accuracy of system cable state perception is further improved.

[0013] Preferably, the step of analyzing the filtered transient response signal to inversely derive the resistivity and heat capacity parameters of the cable comprises: performing down-sampling processing on the filtered transient response signal to obtain a down-sampled signal; performing feature extraction on the down-sampled signal to extract a peak value, a rise time, a fall time, and a signal half-width; based on the peak value, the rise time, the fall time, and the signal half-width, obtaining the resistivity and heat capacity parameters of the cable by table lookup according to a pre-stored mapping relationship table of feature points and cable parameters established by offline simulation or experiment.

[0014] By the technical solution, the resistivity and heat capacity parameters of the cable can be efficiently and accurately obtained by feature extraction and table lookup, the parameter derivation process is simplified, and the real-time performance is improved.

[0015] On the basis of the above, the application further proposes that, before the micro-disturbance is applied, the step of obtaining environmental noise characteristics by sampling baseline noise of voltages at both ends of the cable and currents through the cable comprises: continuously sampling the voltages at both ends of the cable and the currents through the cable to obtain continuous sampling data; dividing the continuous sampling data into data of a plurality of time windows; performing statistical analysis on the data of each time window to obtain noise statistical characteristics of each time window; according to the noise statistical characteristics of each time window, identifying and eliminating abnormal noise peaks to obtain environmental background noise characteristics; in the process of monitoring the transient response, the transient response is collected in real time; combined with the latest identified environmental background noise characteristics, adjusting noise filtering parameters and filtering the transient response to obtain a filtered transient response signal.

[0016] By the technical solution, the environmental background noise characteristics can be updated in real time, and the noise filtering parameters can be dynamically adjusted, so that the noise can be more effectively filtered out in a complex and changeable environment, and the purity of the transient response signal is improved.

[0017] In order to enhance the function, the step of performing statistical analysis on the data of each time window to obtain noise statistical characteristics of each time window comprises: before performing statistical analysis on the data of each time window, performing instantaneous power change rate calculation on the data of each time window; Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data; In the statistical analysis, transient abnormal noise data is excluded, and statistical analysis is performed on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window.

[0018] This technical solution can identify and eliminate transient abnormal noise data, avoiding its interference with noise statistical characteristics, thus making the acquisition of noise characteristics more accurate and reliable.

[0019] As a technological improvement, the steps for marking data points whose instantaneous power change rate exceeds a preset threshold as instantaneous abnormal noise data include: After calculating the instantaneous power change rate for each time window, the mean and standard deviation of all instantaneous power change rates within the corresponding time window are calculated. Set a preset threshold based on the mean and standard deviation; Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data.

[0020] This technical solution enables the dynamic setting of thresholds for instantaneous abnormal noise based on real-time data, thereby improving the adaptability and accuracy of abnormal noise identification.

[0021] As a further improvement, the steps of excluding transient abnormal noise data in the statistical analysis and performing statistical analysis on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window include: After excluding transient abnormal noise data in the statistical analysis, the validity of the remaining data is evaluated to obtain validity evaluation information; Based on the validity assessment information, determine whether the amount of remaining data meets the preset minimum sample size for statistical analysis, and obtain the data amount judgment result; If the data volume judgment result is negative, the sampling duration of the current time window is extended until the minimum sample size is met or the sampling duration reaches the preset maximum extension duration. If the data volume judgment result is yes, then perform statistical analysis on the remaining data that meets the minimum sample size to obtain the noise statistical characteristics of each time window.

[0022] This technical solution ensures that the amount of data used for statistical analysis meets the minimum sample size requirement, avoids statistical bias caused by insufficient data, and thus improves the accuracy of noise statistical characteristics.

[0023] As a system extension, the steps for statistically analyzing the remaining data that meets the minimum sample size requirement to obtain the noise statistical characteristics of each time window include: Collect the noise statistics of the current street light unit and adjacent street light units during the same time period; The noise statistical characteristics of the current street light unit are correlated with those of adjacent street light units to identify local correlations in the noise statistical characteristics. Based on local correlation, the noise statistical characteristics of the current street light unit are corrected to obtain the noise statistical characteristics of each time window.

[0024] This technical solution can utilize noise information from adjacent street light units for correction, further improving the accuracy and robustness of the noise statistical characteristics of a single street light unit, especially in situations where local environmental noise is complex and variable.

[0025] Secondly, this application also discloses a microgrid zero-carbon operation control system based on a street light network, used to perform microgrid zero-carbon operation control based on a street light network, including: The anomaly analysis trigger module is used to monitor the output voltage and current of the street light unit; when the output voltage drops to a preset threshold, it triggers anomaly scenario analysis. The data change estimation module is used to estimate the temperature rise and expected voltage drop of the cable during abnormal scenario analysis by combining current, local ambient temperature and preset cable physical parameters. The voltage drop judgment execution module is used to compare the voltage drop value of the street lamp unit with the expected voltage drop value, and combine it with the temperature rise value of the cable to determine whether the voltage drop is caused by the high load thermal effect, and obtain the voltage drop judgment result. The adjustment measure execution module is used to take adjustment measures to alleviate the voltage drop if the voltage drop judgment result is yes. The voltage recovery and maintenance module is used to continuously monitor the operating status of the street light unit. If the output voltage stabilizes, it will continue to operate normally. The isolation operation implementation module is used to perform isolation operations when the output voltage fails to return to stability within a preset time window after adjustment measures are taken, or when a hard fault in the equipment is confirmed.

[0026] This technical solution provides a system for implementing the aforementioned control method. Through modular design, the system can efficiently and accurately execute zero-carbon operation control of the microgrid, improving the system's maintainability and scalability.

[0027] Beneficial Effects: This application provides a zero-carbon operation control method for a microgrid based on a street light network. It monitors the output voltage and current of the street light units and triggers anomaly analysis when the output voltage drops to a preset threshold. During the anomaly analysis, the method estimates the cable temperature rise and expected voltage drop by combining current, local ambient temperature, and preset cable physical parameters. Subsequently, the voltage drop of the street light unit is compared with the expected voltage drop, and the cable temperature rise is considered to determine whether the voltage drop is caused by high-load thermal effects. If the determination is yes, the regional control center takes adjustment measures to alleviate the voltage drop; if the output voltage stabilizes, normal operation continues; if it does not stabilize within a preset time window or a hard equipment fault is confirmed, isolation is performed.

[0028] This method effectively solves the problem of misjudgment caused by the lack of awareness of the physical transmission link status in existing technologies. In existing technologies, when a cable experiences a voltage drop due to high load heating, the system may incorrectly diagnose it as a device fault and isolate it, thereby cutting off critical power sources and exacerbating grid instability. This application, by introducing estimates of cable temperature rise and expected voltage drop and comparing them with the actual voltage drop, can accurately distinguish between voltage drops caused by high load thermal effects and hard device faults. This avoids erroneous isolation of healthy street light units, ensures the energy transmission capacity of the microgrid in emergency situations, maintains the stable operation of the regional power grid, effectively prevents system interruptions and energy supply interruptions caused by misjudgment, and significantly improves the operational reliability and resilience of the microgrid. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for zero-carbon operation control of a microgrid based on a street light network, according to one embodiment of the present invention. Figure 2 This is a flowchart of a method for zero-carbon operation control of a microgrid based on a street light network, according to another embodiment of the present invention. Figure 3 This is a system block diagram of a microgrid zero-carbon operation control system based on a street light network according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Zero-carbon operation control system for microgrid based on street light network; 11. Anomaly analysis triggering module; 12. Data change estimation module; 13. Voltage drop judgment and execution module; 14. Adjustment measure execution module; 15. Voltage restoration and maintenance module; 16. Isolation operation implementation module. Detailed Implementation

[0030] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0031] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] This embodiment discloses a zero-carbon operation control method for microgrids based on street light networks, combined with... Figure 1 As shown, it includes: S1 monitors the output voltage and current of the street light unit; when the output voltage drops to a preset threshold, it triggers an abnormal scenario analysis. S2, during the abnormal scenario analysis, combines the current, local ambient temperature and preset cable physical parameters to estimate the temperature rise and expected voltage drop of the cable. S3. Compare the voltage drop value of the street light unit with the expected voltage drop value, and combine it with the temperature rise value of the cable to determine whether the voltage drop is caused by the high load thermal effect, and obtain the voltage drop judgment result. S4. If the voltage drop assessment result is yes, the regional control center will take adjustment measures to alleviate the voltage drop. S5 continuously monitors the operating status of the street light unit. If the output voltage returns to a stable state, it will continue to operate normally. S6. If the output voltage does not return to stability within the preset time window after adjustment measures are taken, or if a hard fault in the equipment is confirmed, an isolation operation is performed.

[0033] The term "streetlight unit" as used in this application refers to the basic node constituting a streetlight network. It typically integrates photovoltaic power generation devices, energy storage units, environmental sensing equipment, and local data processing nodes, enabling energy self-sufficiency and interaction with the microgrid. A "microgrid" is an autonomous system composed of distributed power sources, loads, energy storage systems, and control devices, capable of self-control, protection, and management. It can operate either in parallel with the main grid or in isolation. "Zero-carbon operation" refers to the minimization or even zeroing of carbon emissions during microgrid operation through optimized energy dispatch and the utilization of renewable energy. "Abnormal scenario analysis" refers to a series of diagnostic and assessment processes initiated when the system detects an abnormal situation (such as a voltage drop), aiming to identify the root cause of the anomaly. "High-load thermal effect" refers to the phenomenon where, when a cable carries a large current for an extended period, Joule heating occurs due to the cable's resistance, leading to increased cable temperature, increased resistance, and a voltage drop. The "regional control center" is the core control unit responsible for the operation, management, and dispatch of the entire streetlight microgrid system, capable of receiving data from each streetlight unit and issuing control commands. "Isolation operation" refers to disconnecting the faulty street light unit from the microgrid when a hard fault in the equipment is confirmed or the voltage has not returned to stability after adjustment measures are taken, in order to prevent the fault from spreading and protect the operation of other parts of the system.

[0034] In the embodiments of this application, it is first necessary to monitor the output voltage and current of the street light unit. This monitoring can be achieved by integrating voltage and current sensors within each street light unit. For example, the voltage sensor can be a combination of a voltage divider resistor network and an analog-to-digital converter (ADC) to acquire the output voltage value of the street light unit in real time; the current sensor can be a Hall effect sensor or a shunt resistor to measure the current flowing through the street light unit in real time. The data collected by these sensors can be periodically uploaded to the local controller or edge computing device of the street light unit for preliminary processing. When the monitored output voltage drops to a preset threshold, for example, when the voltage value is lower than 90% of the rated voltage for several consecutive sampling periods, an abnormal scenario analysis will be triggered. This preset threshold can be configured according to power grid operation standards and actual application scenarios to balance the system's sensitivity and false alarm rate.

[0035] During abnormal scenario analysis, it is necessary to combine current, local ambient temperature, and preset cable physical parameters to estimate the cable's temperature rise and expected voltage drop. Specifically, the local controller or edge computing device of the streetlight unit can receive real-time current data and local ambient temperature data. Preset cable physical parameters, such as cable length, cross-sectional area, material resistivity, and thermal capacity, can be stored in the device's memory. Based on this data, thermodynamic and electrical models can be used to estimate the cable's temperature rise. For example, the cable's heat output can be calculated using Joule's law, and then combined with the cable's thermal capacity and heat dissipation coefficient to estimate the cable's temperature rise. Simultaneously, based on the cable's resistivity changing with temperature and the estimated temperature rise, the increase in cable resistance can be calculated, and thus the expected voltage drop caused by the increase in cable resistance can be estimated.

[0036] Subsequently, the voltage drop value of the street light unit is compared with the expected voltage drop value, and combined with the cable temperature rise value, to determine whether the voltage drop is caused by the high load thermal effect, thus obtaining the voltage drop judgment result. Specifically, the local controller or edge computing device of the street light unit compares the actual monitored voltage drop value with the estimated expected voltage drop value. If the actual voltage drop value matches the expected voltage drop value within a certain error range, and the estimated cable temperature rise value is significant, it can be preliminarily determined that the voltage drop is caused by the high load thermal effect. For example, a tolerance range can be set; if the difference between the actual voltage drop value and the expected voltage drop value is less than this tolerance, and the cable temperature rise exceeds a certain safety threshold, the judgment result is "yes".

[0037] If the voltage drop assessment result is positive, the regional control center will take adjustment measures to alleviate the voltage drop. After receiving the assessment result from the street light unit, the regional control center will take corresponding adjustment measures based on the overall operating status of the microgrid and the energy dispatch strategy. For example, it can issue instructions to adjacent street light units with lower loads, requesting them to increase energy output to share the pressure on the high-load street light units; or it can adjust the output power of the high-load street light units to temporarily reduce their load and reduce cable heating. In addition, the regional control center can also mitigate voltage drops by optimizing the charging and discharging strategies of energy storage units to smooth load fluctuations.

[0038] After taking adjustment measures, the operating status of the street light unit needs to be continuously monitored. If the output voltage stabilizes, meaning the voltage value returns to the normal range and remains there for a period of time, the street light unit will continue to operate normally. This indicates that the adjustment measures taken were effective and successfully alleviated the voltage drop problem caused by the high load thermal effect.

[0039] However, if the output voltage fails to stabilize within a preset time window after adjustments are implemented, or if a hard equipment fault is confirmed, an isolation operation will be performed. The preset time window can be set based on system response speed and fault recovery requirements, such as 5 minutes or 10 minutes. If the voltage still fails to stabilize within this time window, or if a hard equipment fault (such as inverter damage, battery failure, etc.) is confirmed through other diagnostic methods (e.g., street light unit self-test reports, remote diagnostic tools), the area control center will perform an isolation operation. The isolation operation aims to disconnect the faulty street light unit from the microgrid to prevent fault propagation and protect the stable operation of other healthy street light units and the entire microgrid system.

[0040] Optional, combined Figure 2 As shown, in the abnormal scenario analysis process, S2, combining current, local ambient temperature, and preset cable physical parameters, includes the following steps in estimating the cable's temperature rise and expected voltage drop: S21, When the street light unit is operating under low load or stable load, apply micro-perturbations to the corresponding cable through the edge computing device; S22, based on micro-perturbation, monitors the transient response of the voltage at both ends of the cable and the transient response of the current through the cable; S23, analyze the characteristics of the transient response, and deduce the resistivity and heat capacity parameters of the cable in reverse; S24 updates the internally stored cable characteristic data based on resistivity and heat fusion parameters; S25: When the output voltage drops to a preset threshold, start abnormal scenario analysis, and estimate the temperature rise and expected voltage drop of the cable by combining the current, local ambient temperature and updated cable characteristic data.

[0041] Specifically, during periods when the streetlight unit is operating under low or stable load, a micro-perturbation is applied to the corresponding cable via an edge computing device. This micro-perturbation refers to a small-amplitude, brief electrical signal injection, insufficient to affect the normal operation of the streetlight unit or cause grid instability, but sufficient to generate a measurable transient response in the cable. Choosing to apply the perturbation during low or stable load periods avoids interference from the complex grid dynamics during high loads, ensuring the clarity and analyzability of the transient response. The edge computing device can be understood as intelligent hardware deployed near or inside the streetlight unit, possessing the capabilities for data acquisition, preliminary processing, and control command execution, enabling precise control over the application of the micro-perturbation.

[0042] Furthermore, based on the applied micro-perturbation, the transient response of the voltage at both ends of the cable and the transient response of the current flowing through the cable are monitored. Transient response refers to the dynamic changes in the voltage and current of the cable during the short-term transition from one steady state to another or the return to a steady state after being subjected to an external disturbance. These transient responses contain information about the electrical and thermal characteristics of the cable.

[0043] Subsequently, the characteristics of the transient response are analyzed, and the resistivity and thermal capacity parameters of the cable are derived in reverse. The characteristics of the transient response may include, but are not limited to, the signal rise time, fall time, peak value, and full width at half maximum (FWHM). By establishing a mathematical model or lookup table between the transient response characteristics and the cable's physical parameters, the current resistivity and thermal capacity parameters of the cable can be calculated from the monitored transient response. Resistivity reflects the conductivity of the cable conductor, while thermal capacity reflects the cable's ability to absorb and store heat; both are crucial for accurately estimating the cable's temperature rise and voltage drop.

[0044] Based on the derived resistivity and thermal capacity parameters, the internally stored cable characteristic data is updated. This means that the system no longer relies solely on initially preset static parameters, but periodically or on-demand uses real-time measurement data to calibrate and update the cable model, making it closer to the actual operating state of the cable.

[0045] Finally, when the output voltage drops to a preset threshold, anomaly analysis is initiated. At this point, by combining current, local ambient temperature, and updated cable characteristic data, the expected temperature rise and voltage drop of the cable are estimated. Using updated cable characteristic data for estimation significantly improves the accuracy of the results, thus providing a more reliable basis for subsequent determination of the cause of the voltage drop and the formulation of adjustment measures.

[0046] Optionally, the steps of analyzing the characteristics of the transient response and deriving the resistivity and thermal capacity parameters of the cable include: Before applying the micro-perturbation, baseline noise samples are taken of the voltage at both ends of the cable and the current through the cable to obtain the environmental noise characteristics; When monitoring transient response, the transient response is compared with the characteristics of environmental noise, the environmental noise components are identified and filtered out, and the filtered transient response signal is obtained. By analyzing the characteristics of the filtered transient response signal, the resistivity and thermal capacity parameters of the cable are derived in reverse.

[0047] Specifically, baseline noise sampling refers to the continuous monitoring and data acquisition of the voltage at both ends of the cable and the current flowing through the cable for a period of time before the edge computing device applies micro-perturbations to the corresponding cable. Its purpose is to capture and quantify the background noise level and characteristics of the current environment, such as the frequency distribution, amplitude range, and randomness of the noise. In this way, environmental noise characteristics can be obtained, providing a benchmark for subsequent noise filtering.

[0048] In monitoring transient response, the transient response is compared with environmental noise characteristics to identify and filter out environmental noise components, resulting in a filtered transient response signal. This can be understood as a signal processing procedure: by comparing the real-time acquired transient response signal with pre-acquired environmental noise characteristics, techniques such as digital filtering, adaptive noise cancellation, or spectral analysis are used to separate and remove the noise components superimposed on the transient response signal. The aim is to improve the signal-to-noise ratio of the transient response signal and ensure the accuracy of subsequent analysis.

[0049] In practical applications, the characteristics of the filtered transient response signal are analyzed to deduce the cable's resistivity and thermal capacity parameters. This means that after removing noise interference, the pure transient response signal is analyzed in depth, such as extracting key features like rise time, fall time, peak value, and full width at half maximum (FWHM). These features have specific physical relationships with the cable's physical parameters (such as resistivity and thermal capacity). The resistivity and thermal capacity parameters can be calculated from these features using mathematical models, lookup tables, or machine learning algorithms. The goal is to obtain accurate cable characteristic data, providing a reliable basis for subsequent estimations of cable temperature rise and expected voltage drop.

[0050] In some preferred embodiments, this application is implemented as follows: First, during periods when the streetlight unit is operating under low or stable load, the edge computing device continuously samples the voltage at both ends of the cable and the current flowing through it before applying any micro-perturbations. For example, it samples continuously for 10 seconds at a frequency of 10 kHz to obtain baseline noise data. This data is then sent to the signal processing module for statistical analysis, such as calculating the mean, standard deviation, and spectral distribution, thereby establishing a noise characteristic model of the current environment.

[0051] Subsequently, the edge computing device applies a preset micro-perturbation signal, such as a short pulse current, to the cable. At the same time, high-precision sensors monitor the transient response of the voltage at both ends of the cable and the current flowing through it in real time.

[0052] After detecting the transient response signal, the signal processing module uses the previously established environmental noise characteristic model and employs an adaptive filter (such as a Kalman filter or Wiener filter) to process the transient response signal in real time. This filter dynamically adjusts its parameters according to the environmental noise characteristics to identify and filter out noise components in the transient response signal to the greatest extent possible, thereby obtaining a clean, filtered transient response signal.

[0053] Finally, feature extraction is performed on the filtered transient response signal, such as calculating its rise time, fall time, peak amplitude, and signal energy. These extracted features are then fed into a pre-trained machine learning model (e.g., a support vector machine or neural network) that establishes a mapping relationship between transient response features and cable resistivity and thermal capacity parameters based on extensive offline simulation or experimental data. Using this model, the resistivity and thermal capacity parameters of the current cable can be accurately derived and updated in the internally stored cable characteristic data for subsequent anomaly scenario analysis.

[0054] Optionally, the steps of analyzing the characteristics of the filtered transient response signal and deriving the resistivity and thermal capacity parameters of the cable include: The filtered transient response signal is downsampled to obtain the downsampled signal. Feature extraction is performed on the downsampled signal to extract peak value, rise time, fall time, and signal half-width at half maximum (FWHM). Based on peak value, rise time, fall time, and signal half-width, the resistivity and thermal capacity parameters of the cable are obtained by looking up a pre-stored mapping table between feature points and cable parameters established by offline simulation or experiment.

[0055] Specifically, downsampling the filtered transient response signal refers to reducing the amount of data by lowering the signal's sampling rate while preserving as much key information as possible. Methods such as averaging, decimation, or interpolation can be used. The aim is to reduce the computational complexity of subsequent processing and improve processing efficiency.

[0056] Feature extraction from the downsampled signal involves identifying and quantifying key parameters that represent the cable's physical characteristics from the simplified signal. Specifically, this includes extracting the signal's peak value (the maximum amplitude of the transient response); rise time (the time required for the signal to rise from a low threshold to a high threshold); fall time (the time required for the signal to fall from a high threshold to a low threshold); and the half-width at half-maximum (HWHM) of the signal (the duration when the signal amplitude reaches half its peak value). These feature points are chosen because they have a direct or indirect physical correlation with the cable's resistivity and thermal capacity parameters, effectively reflecting the cable's dynamic response characteristics.

[0057] In practical applications, the resistivity and thermal capacity parameters of the cable are obtained by looking up a table based on the extracted peak value, rise time, fall time, and signal half-width at half-maximum. This requires pre-storing a mapping table between feature points and cable parameters established through offline simulation or experimentation. This mapping table is established by conducting numerous experiments or simulations on different types and states of cables under controlled conditions, recording the correspondence between their transient response feature points (such as peak value, rise time, etc.) and the corresponding actual resistivity and thermal capacity parameters. In actual operation, the system only needs to use the real-time extracted feature points as the lookup key to quickly obtain the corresponding cable resistivity and thermal capacity parameters by searching in this mapping table.

[0058] Optionally, before applying the micro-perturbation, the step of sampling the voltage at both ends of the cable and the current through the cable to obtain the environmental noise characteristics includes: The voltage at both ends of the cable and the current flowing through the cable are continuously sampled to obtain continuous sampling data; The continuous sampling data is divided into several time windows; Statistical analysis was performed on the data for each time window to obtain the noise statistical characteristics of each time window; Based on the noise statistical characteristics of each time window, abnormal noise peaks are identified and removed to obtain the environmental background noise characteristics; During the monitoring of transient response, transient response data is collected in real time. By combining the newly identified environmental background noise characteristics, the noise filtering parameters are adjusted and the transient response is filtered to obtain the filtered transient response signal.

[0059] Specifically, continuous sampling of the voltage at both ends of a cable and the current flowing through it refers to the uninterrupted acquisition of real-time data streams of cable voltage and current using high-precision sensors or measuring equipment at a preset sampling frequency. The purpose is to capture all electrical signals in the cable's operating environment, including normal operating signals and various noise components. This yields continuously sampled data, which contains detailed operational information about the cable at different points in time. This continuously sampled data is divided into several time windows. For example, the continuously acquired data can be segmented according to fixed time intervals (e.g., per second, per minute, or shorter intervals), with each segment constituting a time window. This segmentation allows for independent analysis of noise characteristics within specific time periods, thereby better capturing the dynamic changes in noise.

[0060] Furthermore, statistical analysis is performed on the data for each time window to obtain the noise statistical characteristics of each time window. Statistical analysis may include calculating statistics such as mean, variance, standard deviation, kurtosis, and skewness. These statistics can quantify the intensity, distribution, and frequency characteristics of noise within the time window. The purpose is to provide a quantitative basis for subsequent noise identification and filtering. Based on this, abnormal noise peaks are identified and removed according to the noise statistical characteristics of each time window, thereby obtaining the environmental background noise characteristics. Abnormal noise peaks may be caused by transient interference (such as switching operations, arc discharges, etc.), and these peaks do not belong to stable environmental background noise. By setting a statistical threshold (for example, data points exceeding the mean plus three standard deviations), these transient anomalies can be effectively identified and removed, thereby obtaining purer and more representative environmental background noise characteristics.

[0061] During transient response monitoring, the transient response is acquired in real time. This means that when a micro-perturbation is applied to the cable and causes a transient response, the relevant voltage and current data are immediately recorded. Simultaneously, combined with the latest identified environmental background noise characteristics, noise filtering parameters are adjusted and the transient response is filtered to obtain the filtered transient response signal. This means that the system dynamically adjusts the parameters of the filter (e.g., a digital filter) based on the real-time updated environmental background noise characteristics to ensure that environmental noise components in the transient response signal are most effectively suppressed and removed under the current noise environment. This adaptive filtering mechanism significantly improves the purity of the transient response signal.

[0062] As a specific implementation, suppose that in a street light network microgrid, the voltage at both ends of the cable and the current flowing through the cable are continuously sampled at a frequency of 1000 times per second (1kHz). These continuously sampled data are divided into time windows of 10 seconds each. Within each time window, the system performs statistical analysis on the voltage and current data, calculating their mean and standard deviation. For example, if the mean voltage data within a certain time window is 220V and the standard deviation is 0.5V, and several instantaneous voltage spikes (e.g., transient interference caused by the activation of nearby electric vehicle charging stations) are identified and eliminated through instantaneous power change rate calculation, then the statistical characteristics of the remaining data will be used to characterize the environmental background noise within that time window. When the system needs to apply micro-perturbations and monitor transient response, it acquires transient response data in real time. Simultaneously, the system acquires the updated environmental background noise characteristics from the most recent one or several time windows and dynamically adjusts parameters such as the cutoff frequency and gain of the digital low-pass filter or notch filter based on these characteristics. For example, if an increase in noise intensity is detected in a certain frequency range, the filter parameters will be adjusted to more effectively suppress noise at that frequency. In this way, even when the intensity or frequency distribution of ambient noise changes (e.g., the noise difference between busy daytime traffic and quiet nighttime), the transient response signal can be accurately filtered, ensuring the accuracy of subsequent cable parameter derivation.

[0063] Optionally, the steps of performing statistical analysis on the data for each time window to obtain the noise statistical characteristics of each time window include: Before performing statistical analysis on the data for each time window, the instantaneous power change rate is calculated for the data for each time window. Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data; In the statistical analysis, transient abnormal noise data is excluded, and statistical analysis is performed on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window.

[0064] Specifically, instantaneous power change rate calculation refers to real-time or near-real-time processing of voltage and current data collected within each time window to calculate the instantaneous power, and further calculate the rate of change of instantaneous power over time. This rate of change can effectively reflect sudden, transient disturbances in the power grid or environment. For example, the instantaneous power change rate can be obtained by calculating the ratio of the power difference between adjacent sampling points to the time interval. Instantaneous abnormal noise data can be understood as data points where power changes drastically within a short period. These data points are usually not within the normal background noise range, but are caused by external interference or transient equipment behavior. By marking data points where the instantaneous power change rate exceeds a preset threshold as instantaneous abnormal noise data, these abnormal spikes or pulse signals can be effectively identified. The preset threshold can be set and adjusted according to the noise characteristics of the actual power grid environment, equipment sensitivity, and empirical values. In practical applications, excluding transient outlier noise data in statistical analysis and then performing statistical analysis on the data after excluding transient outlier noise refers to removing data points marked as transient outlier noise from the dataset when calculating the noise statistical characteristics (such as mean, variance, standard deviation, etc.) for each time window. This aims to ensure that the sample data for statistical analysis is more representative, can more accurately reflect the true characteristics of environmental background noise, and avoid the interference of outliers on the statistical results.

[0065] In some preferred embodiments, it is assumed that the voltage and current data of the street light unit are continuously sampled within a certain time window. Under normal circumstances, these data will exhibit some background noise fluctuations. However, if a short-duration but high-amplitude voltage or current spike occurs within this time window due to the startup of nearby equipment or a momentary disturbance in the power grid, this will cause a drastic change in instantaneous power. The solution of this application first calculates the instantaneous power change rate of all sampled data within the time window. When the instantaneous power change rate of a certain data point is detected to be far exceeding a preset threshold, for example, the threshold is set to 100W / ms, and the calculated change rate of a certain data point is 500W / ms, then the data point will be immediately marked as instantaneous abnormal noise data. In subsequent statistical analyses such as mean and standard deviation of the data in this time window, this marked abnormal data point will be excluded. For example, if there are 1000 sampling points in this time window, and 5 of them are marked as instantaneous abnormal noise data, then the statistical analysis will only be based on the remaining 995 normal data points. In this way, the obtained noise mean and standard deviation will more accurately reflect the true background noise level within the time window, without being severely inflated or distorted by the five outlier data points.

[0066] Optionally, the step of marking data points whose instantaneous power change rate exceeds a preset threshold as instantaneous anomalous noise data includes: After calculating the instantaneous power change rate for each time window, the mean and standard deviation of all instantaneous power change rates within the corresponding time window are calculated. Set a preset threshold based on the mean and standard deviation; Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data.

[0067] The instantaneous power change rate refers to the fluctuation amplitude of power within a very short time interval, reflecting rapid changes in system load or environmental disturbances. After calculating the instantaneous power change rate for each time window, a statistical description of the power fluctuation characteristics within that time window can be obtained by calculating the mean and standard deviation of all instantaneous power change rates within that time window. The mean reflects the average level of the instantaneous power change rate, while the standard deviation measures the dispersion of these change rates relative to the mean, i.e., volatility. Based on these statistics, preset thresholds can be dynamically set. For example, the preset threshold can be set to the mean plus or minus a certain number of standard deviations (e.g., mean ± 3 standard deviations) to cover the vast majority of normal power fluctuations, and data points exceeding this range are considered instantaneous abnormal noise data.

[0068] Optionally, the steps of excluding transient abnormal noise data in the statistical analysis and performing statistical analysis on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window include: After excluding transient abnormal noise data in the statistical analysis, the validity of the remaining data is evaluated to obtain validity evaluation information; Based on the validity assessment information, determine whether the amount of remaining data meets the preset minimum sample size for statistical analysis, and obtain the data amount judgment result; If the data volume judgment result is negative, the sampling duration of the current time window is extended until the minimum sample size is met or the sampling duration reaches the preset maximum extension duration. If the data volume judgment result is yes, then perform statistical analysis on the remaining data that meets the minimum sample size to obtain the noise statistical characteristics of each time window.

[0069] Specifically, after excluding transient abnormal noise data in statistical analysis, the remaining data needs to be evaluated for validity. Validity evaluation information may include, but is not limited to, the quantity of remaining data, the uniformity of data distribution, and the existence of consecutive missing data segments. The purpose is to ensure that the data used for subsequent statistical analysis has sufficient quality and quantity. The preset minimum sample size for statistical analysis refers to the minimum number of data points required for reliable statistical analysis. This minimum sample size can be set based on statistical principles, empirical values, or the system's accuracy requirements for noise feature identification. For example, it can be set to 30 data points to satisfy the approximate condition of the law of large numbers. In practical applications, if the judgment result is negative, i.e., the remaining data is insufficient, the sampling duration of the current time window will be extended. Extending the sampling duration means continuing to collect data based on the current time window to obtain more valid data points. The extension operation will continue until the preset minimum sample size for statistical analysis is met, or the preset maximum extension duration is reached. The preset maximum extension duration is to prevent indefinite extension of sampling and ensure the timeliness of the system response. For example, the maximum extension duration can be set to twice the duration of the current time window. If the judgment result is yes, that is, the remaining data volume meets the minimum sample size requirement, then statistical analysis is directly performed on these remaining data that meet the minimum sample size requirement to obtain the noise statistical characteristics of each time window.

[0070] In some preferred embodiments, it is assumed that the original sampling data for a time window contains 100 data points. After calculating the instantaneous power change rate and marking instantaneous abnormal noise data, 80 data points are marked as instantaneous abnormal noise data and excluded. At this point, only 20 valid data points remain. If the preset minimum sample size for statistical analysis is 30 data points, then these 20 data points will not meet the minimum sample size requirement. According to the scheme of this application, the system will determine that the data volume is insufficient and extend the sampling duration of the current time window. For example, the system can continue sampling until another 15 valid data points are collected, so that the total number of valid data points reaches 35 (exceeding the minimum sample size of 30). During this process, if the extended sampling duration reaches the preset maximum extension duration (e.g., twice the original time window duration), but still does not meet the minimum sample size, the system will use the currently available maximum valid data volume for statistical analysis and may mark the noise characteristics of the time window as "low confidence". Once the minimum sample size is met, the system performs statistical analysis on these 35 valid data points to obtain more accurate and reliable noise statistical characteristics for each time window.

[0071] Optionally, the steps of performing statistical analysis on the remaining data that meets the minimum sample size to obtain the noise statistical characteristics of each time window include: Collect the noise statistics of the current street light unit and adjacent street light units during the same time period; The noise statistical characteristics of the current street light unit are correlated with those of adjacent street light units to identify local correlations in the noise statistical characteristics. Based on local correlation, the noise statistical characteristics of the current street light unit are corrected to obtain the noise statistical characteristics of each time window.

[0072] Specifically, when performing statistical analysis on the remaining data that meets the minimum sample size, the noise statistical characteristics of the street light unit currently undergoing noise analysis are first collected. Simultaneously, to obtain more comprehensive noise information, the noise statistical characteristics of other street light units geographically adjacent to the current street light unit are also collected during the same time period. These noise statistical characteristics of adjacent street light units can be used as a reference to assess the representativeness and accuracy of the noise characteristics of the current street light unit.

[0073] The analysis involves correlating the noise statistics of the current streetlight unit with those of adjacent streetlight units to identify local correlations in these statistics. This correlation analysis can be achieved using various statistical methods, such as correlation coefficient calculation, cluster analysis, or pattern recognition. The analysis determines whether the noise characteristics of the current streetlight unit exhibit consistency or a specific correlation pattern with those of adjacent units. For example, if multiple adjacent units exhibit a specific noise pattern (such as high-frequency interference or harmonics at specific frequencies) within the same time period, this can be considered a localized environmental noise characteristic.

[0074] In practical applications, the noise statistical characteristics of the current streetlight unit are corrected based on the identified local correlations. The purpose of this correction is to eliminate or reduce data bias caused by noise factors that are specific to the current unit but not universal, while enhancing the identification of universal environmental noise characteristics. For example, if a noise peak in the current unit does not appear in neighboring units, this peak may be considered a local anomaly and weakened or removed during the correction process. Conversely, if a noise feature is prevalent in multiple neighboring units, the weight of this feature may be enhanced, resulting in more accurate and representative noise statistical characteristics for each time window.

[0075] This application also proposes a microgrid zero-carbon operation control system based on a street light network, used to execute microgrid zero-carbon operation control based on a street light network, combined with... Figure 3 As shown, the microgrid zero-carbon operation control system 1 based on street light networks includes: The anomaly analysis trigger module 11 is used to monitor the output voltage and current of the street light unit; when the output voltage drops to a preset threshold, it triggers anomaly scenario analysis. The data change estimation module 12 is used to estimate the temperature rise and expected voltage drop of the cable by combining the current, local ambient temperature and preset cable physical parameters during the abnormal scenario analysis process. The voltage drop judgment execution module 13 is used to compare the voltage drop value of the street lamp unit with the expected voltage drop value, and combine it with the temperature rise value of the cable to determine whether the voltage drop is caused by the high load thermal effect, and obtain the voltage drop judgment result. The adjustment measure execution module 14 is used to take adjustment measures to alleviate the voltage drop if the voltage drop judgment result is yes. The voltage recovery and maintenance module 15 is used to continuously monitor the operating status of the street light unit. If the output voltage is restored to a stable state, it will continue to operate normally. The isolation operation implementation module 16 is used to perform isolation operations when the output voltage fails to return to stability within a preset time window after adjustment measures are taken, or when a hard fault in the equipment is confirmed.

[0076] The term "streetlight unit" as used in this application refers to the basic node constituting a streetlight network. It typically integrates photovoltaic power generation devices, energy storage units, environmental sensing equipment, and local data processing nodes, enabling energy self-sufficiency and interaction with the microgrid. A "microgrid" is an autonomous system composed of distributed power sources, loads, energy storage systems, and control devices, capable of self-control, protection, and management. It can operate either in parallel with the main grid or in isolation. "Zero-carbon operation" refers to the minimization or even zeroing of carbon emissions during microgrid operation through optimized energy dispatch and the utilization of renewable energy. "Abnormal scenario analysis" refers to a series of diagnostic and assessment processes initiated when the system detects an abnormal situation (such as a voltage drop), aiming to identify the root cause of the anomaly. "High-load thermal effect" refers to the phenomenon where, when a cable carries a large current for an extended period, Joule heating occurs due to the cable's resistance, leading to increased cable temperature, increased resistance, and a voltage drop. The "regional control center" is the core control unit responsible for the operation, management, and dispatch of the entire streetlight microgrid system, capable of receiving data from each streetlight unit and issuing control commands. "Isolation operation" refers to disconnecting the faulty street light unit from the microgrid when a hard fault in the equipment is confirmed or the voltage has not returned to stability after adjustment measures are taken, in order to prevent the fault from spreading and protect the operation of other parts of the system.

[0077] The system in this application achieves zero-carbon operation control of the street light network microgrid through the coordinated work of various functional modules.

[0078] Specifically, the anomaly analysis trigger module can be configured to be integrated into the local controller of each street light unit. It contains voltage and current sensors to collect the output voltage and current of the street light unit in real time. For example, the voltage sensor can use a voltage divider resistor network combined with an analog-to-digital converter (ADC) to collect the output voltage value of the street light unit in real time; the current sensor can use a Hall effect sensor or a shunt resistor to measure the current flowing through the street light unit in real time. The data collected by these sensors can be periodically uploaded to the local controller or edge computing device of the street light unit for preliminary processing. This module can use a hardware comparator circuit or embedded software logic. When the detected output voltage drop reaches a preset threshold, such as when it is below 90% of the rated voltage for several consecutive sampling periods, it sends a trigger signal to the data change estimation module to initiate anomaly scenario analysis. This preset threshold can be configured according to power grid operation standards and actual application scenarios to balance the system's sensitivity and false alarm rate. As one implementation, this module can determine the voltage drop solely through software logic, and its threshold can be configured remotely.

[0079] The data change estimation module can be implemented as a processing unit on the local edge computing device of the streetlight unit. This processing unit receives trigger signals from the anomaly analysis trigger module, real-time current data, and local ambient temperature data. Pre-defined cable physical parameters, such as cable length, cross-sectional area, material resistivity, and thermal capacity, can be pre-stored in the processing unit's memory. The module estimates the cable's temperature rise by executing pre-programmed thermodynamic and electrical model algorithms, such as calculating the cable's heat output based on Joule's law, and then combining this with the cable's thermal capacity and heat dissipation coefficient. Simultaneously, based on the cable's resistivity changing with temperature and the estimated temperature rise, the increase in cable resistance can be calculated, thereby estimating the expected voltage drop due to the increased cable resistance. As one implementation, this module can rely solely on pre-defined fixed cable parameters for estimation without real-time parameter updates.

[0080] The voltage drop judgment module can be configured as a core processing unit in the regional control center. This unit receives the actual voltage drop value from the streetlight unit, the expected voltage drop value estimated by the data change estimation module, and the cable temperature rise value. The module compares the actual voltage drop value with the expected voltage drop value, and combines this with the cable temperature rise value to perform logical judgments to determine whether the voltage drop is caused by high load thermal effects. Specifically, if the actual voltage drop value matches the expected voltage drop value within a certain error range, and the estimated cable temperature rise value is significant, it can be preliminarily determined that the voltage drop is caused by high load thermal effects. For example, a tolerance range can be set; if the difference between the actual voltage drop value and the expected voltage drop value is less than this tolerance, and the cable temperature rise exceeds a certain safety threshold, the judgment result is "yes". As one implementation, this module can make judgments based solely on the magnitude of the voltage drop, without fully considering the cable temperature rise.

[0081] The adjustment measure execution module can be implemented as a control interface unit of the regional control center. Upon receiving a "yes" judgment from the voltage drop judgment execution module, this unit takes corresponding adjustment measures based on the overall operating status of the microgrid and the energy dispatch strategy. For example, it can issue instructions to adjacent, low-load street light units, requesting them to increase energy output to alleviate the pressure on high-load street light units; or it can adjust the output power of high-load street light units to temporarily reduce their load and reduce cable heating. Furthermore, the regional control center can also smooth load fluctuations by optimizing the charging and discharging strategies of energy storage units, thereby mitigating voltage drops. As one implementation, this module can only execute a preset single adjustment strategy, lacking the ability to dynamically optimize based on real-time grid conditions.

[0082] The voltage recovery and maintenance module can be configured as a continuous monitoring process within the regional control center or the local controller of the streetlight unit. This process continuously monitors the output voltage of the streetlight unit after the adjustment measures execution module takes action. If the output voltage stabilizes—that is, the voltage value returns to the normal range and remains there for a period of time—the module signals that the streetlight unit will continue to operate normally. This indicates that the adjustment measures taken were effective and successfully mitigated the voltage drop problem caused by the high-load thermal effect. As one implementation, this module can perform only short-term voltage recovery monitoring without considering long-term stability assessments.

[0083] The isolation operation implementation module can be implemented as a safety control unit in the regional control center. This unit performs isolation operations upon receiving a "not stabilized" signal from the voltage recovery and maintenance module, or upon confirming a hard equipment fault through other diagnostic methods (e.g., street light unit self-test reports, remote diagnostic tools). A preset time window can be set according to system response speed and fault recovery requirements, such as 5 minutes or 10 minutes. If the voltage has not stabilized within this time window, or if a hard equipment fault is confirmed, the module will perform isolation operations. The isolation operation aims to disconnect the faulty street light unit from the microgrid to prevent fault propagation and protect the stable operation of other healthy street light units and the entire microgrid system. As one implementation, this module can perform isolation only when a hard equipment fault is confirmed, and may not respond promptly to situations where adjustment measures are ineffective.

[0084] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A zero-carbon operation control method for microgrids based on street light networks, characterized in that, include: Monitor the output voltage and current of the street light unit; when the output voltage drops to a preset threshold, trigger an abnormal scenario analysis; During the abnormal scenario analysis, the temperature rise and expected voltage drop of the cable are estimated by combining the current, local ambient temperature, and preset cable physical parameters. The voltage drop of the street light unit is compared with the expected voltage drop, and combined with the temperature rise of the cable, to determine whether the voltage drop is caused by the high load thermal effect, and the voltage drop judgment result is obtained. If the voltage drop assessment result is yes, the regional control center will take adjustment measures to alleviate the voltage drop; The operating status of the street light unit is continuously monitored. If the output voltage returns to a stable state, normal operation continues. If the output voltage fails to stabilize within a preset time window after adjustment measures are taken, or if a hardware fault is confirmed, an isolation operation is performed.

2. The microgrid zero-carbon operation control method based on street light network according to claim 1, characterized in that, The steps for estimating the temperature rise and expected voltage drop of the cable during the abnormal scenario analysis, in conjunction with the current, local ambient temperature, and preset cable physical parameters, include: During the period when the street light unit is operating under low load or stable load, micro-perturbations are applied to the corresponding cable through edge computing devices; Based on micro-perturbations, monitor the transient response of the voltage at both ends of the cable and the transient response of the current flowing through the cable; The characteristics of the transient response were analyzed, and the resistivity and thermal capacity parameters of the cable were derived in reverse. Update the internally stored cable characteristic data based on the resistivity and thermal fusion parameters; When the output voltage drops to a preset threshold, an abnormal scenario analysis is initiated. Combining the current, local ambient temperature, and updated cable characteristic data, the temperature rise and expected voltage drop of the cable are estimated.

3. The microgrid zero-carbon operation control method based on street light network according to claim 2, characterized in that, The steps of analyzing the characteristics of the transient response and deriving the resistivity and thermal capacity parameters of the cable include: Before applying the micro-perturbation, baseline noise samples are taken of the voltage at both ends of the cable and the current through the cable to obtain the environmental noise characteristics; When monitoring the transient response, the transient response is compared with the environmental noise characteristics to identify and filter out the environmental noise components, thereby obtaining the filtered transient response signal; By analyzing the characteristics of the filtered transient response signal, the resistivity and thermal capacity parameters of the cable are derived in reverse.

4. The zero-carbon operation control method for a microgrid based on a street light network according to claim 3, characterized in that, The steps of analyzing the characteristics of the filtered transient response signal and deriving the resistivity and thermal capacity parameters of the cable include: The filtered transient response signal is downsampled to obtain the downsampled signal. Feature extraction is performed on the downsampled signal to extract peak value, rise time, fall time, and signal half-width at half maximum (FWHM). Based on peak value, rise time, fall time, and signal half-width, the resistivity and thermal capacity parameters of the cable are obtained by looking up a pre-stored mapping table between feature points and cable parameters established by offline simulation or experiment.

5. The microgrid zero-carbon operation control method based on street light network according to claim 3, characterized in that, The step of sampling the voltage at both ends of the cable and the current through the cable to obtain environmental noise characteristics before applying the micro-perturbation includes: The voltage at both ends of the cable and the current flowing through the cable are continuously sampled to obtain continuous sampling data; The continuous sampling data is divided into several time windows; Statistical analysis was performed on the data for each time window to obtain the noise statistical characteristics of each time window; Based on the noise statistical characteristics of each time window, abnormal noise peaks are identified and removed to obtain the environmental background noise characteristics; The transient response is acquired in real time during the monitoring process; By combining the newly identified environmental background noise characteristics, the noise filtering parameters are adjusted and the transient response is filtered to obtain the filtered transient response signal.

6. The microgrid zero-carbon operation control method based on street light network according to claim 5, characterized in that, The step of performing statistical analysis on the data for each time window to obtain the noise statistical characteristics of each time window includes: Before performing statistical analysis on the data for each time window, the instantaneous power change rate is calculated for the data for each time window. Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data; In the statistical analysis, transient abnormal noise data is excluded, and statistical analysis is performed on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window.

7. The microgrid zero-carbon operation control method based on street light network according to claim 6, characterized in that, The step of marking data points whose instantaneous power change rate exceeds a preset threshold as instantaneous abnormal noise data includes: After calculating the instantaneous power change rate for each time window, the mean and standard deviation of all instantaneous power change rates within the corresponding time window are calculated. Based on the mean and the standard deviation, a preset threshold is set; Data points whose instantaneous power change rate exceeds a preset threshold are marked as instantaneous abnormal noise data.

8. A zero-carbon operation control method for a microgrid based on a street light network according to claim 6, characterized in that, The steps of excluding transient abnormal noise data in statistical analysis and performing statistical analysis on the data after excluding transient abnormal noise data to obtain the noise statistical characteristics of each time window include: After excluding transient abnormal noise data in the statistical analysis, the validity of the remaining data is evaluated to obtain validity evaluation information; Based on the validity assessment information, determine whether the amount of remaining data meets the preset minimum sample size for statistical analysis, and obtain the data amount judgment result; If the data volume determination result is negative, the sampling duration of the current time window is extended until the minimum sample volume is met or the sampling duration reaches the preset maximum extension duration. If the data volume judgment result is yes, then statistical analysis is performed on the remaining data that meets the minimum sample size to obtain the noise statistical characteristics of each time window.

9. A zero-carbon operation control method for a microgrid based on a street light network according to claim 8, characterized in that, The step of performing statistical analysis on the remaining data that meets the minimum sample size to obtain the noise statistical characteristics of each time window includes: Collect the noise statistics of the current street light unit and adjacent street light units during the same time period; The noise statistical characteristics of the current street light unit are correlated with those of adjacent street light units to identify local correlations in the noise statistical characteristics. Based on the local correlation, the noise statistical characteristics of the current street light unit are corrected to obtain the noise statistical characteristics of each time window.

10. A zero-carbon operation control system for a microgrid based on a street light network, used to execute zero-carbon operation control of a microgrid based on a street light network, characterized in that, include: An anomaly analysis trigger module is used to monitor the output voltage and current of the street light unit; when the output voltage drops to a preset threshold, an anomaly scenario analysis is triggered. The data change estimation module is used to estimate the temperature rise and expected voltage drop of the cable by combining the current, local ambient temperature and preset cable physical parameters during the abnormal scenario analysis process. The voltage drop judgment execution module is used to compare the voltage drop value of the street lamp unit with the expected voltage drop value, and combine it with the temperature rise value of the cable to determine whether the voltage drop is caused by the high load thermal effect, and obtain the voltage drop judgment result. The adjustment measure execution module is used to take adjustment measures to alleviate the voltage drop if the voltage drop judgment result is yes. The voltage recovery and maintenance module is used to continuously monitor the operating status of the street light unit. If the output voltage stabilizes, it will continue to operate normally. The isolation operation implementation module is used to perform isolation operations when the output voltage fails to return to stability within a preset time window after adjustment measures are taken, or when a hard fault in the equipment is confirmed.

Images