Fire-fighting early warning method and system for key operation equipment of transformer substation based on Internet of Things

Abstract

The invention provides a transformer station key operation equipment fire-fighting early warning method and system based on the Internet of Things, and the method comprises the steps: carrying out the electromagnetic interference evaluation of a partial discharge signal in the transmission process of the partial discharge signal of a transformer station transformer; if the electromagnetic interference evaluation result is determined to be qualified, carrying out acquisition accuracy analysis on the partial discharge signal; if the acquisition accuracy analysis result is determined to be qualified, carrying out transmission validity evaluation on the partial discharge signal, otherwise, executing an acquisition optimization measure of the partial discharge signal; if it is determined that the transmission effectiveness evaluation result is qualified, transmission qualification verification is carried out on the partial discharge signals, and otherwise, transmission process optimization measures of the partial discharge signals are executed; and when the transmission qualification verification result is determined to be unqualified, performing fire-fighting early warning deviation evaluation on the partial discharge signal, and starting corresponding fire-fighting measures. Therefore, the accuracy and timeliness of fire hazard identification are improved, and early response and accurate disposal of the fire hazard of the key operation equipment of the transformer substation are realized.

Description

Technical Field

[0001] This application relates to the field of early warning and control technology for substation operating equipment, and in particular to a fire early warning method and system for key operating equipment in substations based on the Internet of Things. Background Technology

[0002] On key operating equipment in substations, such as transformers, high-voltage switchgear, reactors, and capacitors, IoT sensing devices such as partial discharge sensors can be deployed to collect operating status data such as partial discharge signals. This data can be transmitted to the fire monitoring center via wireless network. Big data analysis and deep machine learning can be used to identify abnormal features and trigger multi-level early warnings and fire-fighting linkages when fire hazards are detected, thus achieving early warning and response.

[0003] In existing technologies, transformer partial discharge signals and other monitoring data can be acquired in real time and transmitted to a remote visualization cloud platform, integrating various monitoring systems such as traditional fire alarms, smart fire protection, environmental monitoring, and thermal imaging to achieve multi-dimensional analysis and early warning. Existing technologies have improved signal processing and anomaly identification capabilities through high-sensitivity acquisition, filtering and amplification, frequency domain analysis, and big data correlation analysis.

[0004] However, in the high-voltage electromagnetic interference environment of substations, oil-immersed high-voltage electrical equipment is susceptible to electromagnetic interference and transmission loss when transmitting partial discharge ultra-high frequency signals, resulting in signal weakening or distortion. Some fault signals cannot be captured in time, which in turn causes delays in fire early warning and reduces the timeliness and reliability of the early warning. Summary of the Invention

[0005] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency in the prior art where delayed fire warnings reduce the timeliness and reliability of warnings.

[0006] Firstly, this application provides a fire early warning method for key operating equipment in substations based on the Internet of Things (IoT), the method comprising:

[0007] Electromagnetic interference assessment of partial discharge signals during transmission from transformers in substations;

[0008] If the electromagnetic interference assessment results are deemed satisfactory, the accuracy of the partial discharge signal acquisition will be analyzed; otherwise, a fire alarm will be triggered.

[0009] If the accuracy analysis results of the data acquisition are deemed satisfactory, the transmission effectiveness of the partial discharge signal will be evaluated; otherwise, optimization measures for the acquisition of the partial discharge signal will be implemented.

[0010] If the transmission effectiveness assessment result is deemed satisfactory, the transmission qualification of the partial discharge signal will be verified; otherwise, optimization measures for the transmission process of the partial discharge signal will be implemented.

[0011] When the transmission qualification verification result is determined to be unqualified, a fire warning deviation assessment is performed on the partial discharge signal, and corresponding fire protection measures are initiated.

[0012] In one embodiment, the step of determining whether the electromagnetic interference assessment result is acceptable includes:

[0013] Deviation quantization is performed between the narrowband peak value and the preset narrowband peak value of the partial discharge signal;

[0014] When the deviation quantification result is less than the electromagnetic interference safety threshold, the electromagnetic interference assessment result is qualified.

[0015] In one embodiment, the step of determining whether the data collection accuracy analysis results are acceptable includes:

[0016] The offset of partial discharge parameters is obtained by quantizing the proportion of partial discharge signal parameters and standard partial discharge signal parameters.

[0017] The partial discharge parameter offset and the weighted data of partial discharge characteristics are weighted and fused to obtain the partial discharge signal deviation index.

[0018] When the partial discharge signal deviation index is less than the partial discharge signal accuracy threshold, the acquisition accuracy analysis result is qualified.

[0019] In one embodiment, the step of implementing partial discharge signal acquisition optimization measures includes:

[0020] Based on the current voltage of the transformer and the partial discharge signal deviation index, obtain the signal change rate gain adjustment ratio;

[0021] If the signal rate of change gain adjustment ratio exceeds the preset maximum gain threshold, an alarm for abnormal filtering parameters will be sent; otherwise, the signal rate of change gain adjustment ratio will be used as the qualified gain adjustment value.

[0022] After gradually increasing the signal change rate gain of the partial discharge signal by adjusting the amplitude corresponding to the qualified gain value, if the partial discharge signal deviation index is not greater than the partial discharge signal accuracy threshold, the transmission effectiveness of the partial discharge signal is evaluated.

[0023] Otherwise, the partial discharge signal sampling rate adjustment ratio is obtained based on the highest frequency of the partial discharge signal and the partial discharge signal deviation index;

[0024] If the partial discharge signal sampling rate adjustment ratio exceeds the preset gain threshold range, an abnormal sampling rate adjustment alarm will be sent; otherwise, the partial discharge signal sampling rate adjustment ratio will be used as the qualified partial discharge signal sampling rate adjustment value.

[0025] If the sampling rate of the partial discharge signal is gradually increased by adjusting the sampling rate of the qualified partial discharge signal to the corresponding amplitude, and the partial discharge signal deviation index is greater than the partial discharge signal accuracy threshold, a sampling rate adjustment failure alarm is triggered; otherwise, the transmission effectiveness of the partial discharge signal is evaluated.

[0026] In one embodiment, the step of determining whether the transmission validity assessment result is qualified includes:

[0027] Determine the evaluation index for the transmission effectiveness deviation of partial discharge signals;

[0028] When the transmission effectiveness deviation assessment index is within the transmission effectiveness safety threshold range, the transmission effectiveness assessment result is qualified.

[0029] In one embodiment, the step of implementing measures to optimize the transmission process of partial discharge signals includes:

[0030] If the transmission effectiveness deviation evaluation index is greater than the preset transmission effectiveness evaluation threshold, the partial discharge signal will be used as the first-level transmission data; otherwise, the partial discharge signal will be used as the second-level transmission data.

[0031] After performing first-level data transmission on first-level transmission data and second-level data transmission on second-level transmission data, if the transmission effectiveness deviation evaluation index is not within the transmission effectiveness safety threshold range, a partial transmission optimization failure alarm is sent; otherwise, a partial transmission qualification verification is performed on the partial discharge signal.

[0032] In one embodiment, the step of determining that the transmission qualification verification result is unqualified includes:

[0033] Obtain the fire response deviation index of fire-fighting equipment in substations;

[0034] If the fire response deviation index is not within the preset transmission delay safety threshold range, the transmission qualification verification result is unqualified.

[0035] In one embodiment, the steps of assessing the fire warning deviation of the partial discharge signal and activating the corresponding fire-fighting measures include:

[0036] If the fire response deviation index is greater than the preset transmission delay safety threshold, then the first-level fire protection measures will be activated.

[0037] If the fire response deviation index is not greater than the preset transmission delay safety threshold, then the secondary fire protection measures will be activated.

[0038] Secondly, this application provides a fire early warning device for key operating equipment in a substation based on the Internet of Things, the device comprising:

[0039] The electromagnetic interference assessment module is used to assess the electromagnetic interference of partial discharge signals during the transmission of partial discharge signals in substation transformers.

[0040] The accuracy analysis module is used to analyze the accuracy of partial discharge signals if the electromagnetic interference assessment result is deemed acceptable; otherwise, it triggers a fire alarm.

[0041] The transmission effectiveness assessment module is used to assess the transmission effectiveness of the partial discharge signal if the accuracy analysis results of the acquisition are deemed satisfactory; otherwise, it implements acquisition optimization measures for the partial discharge signal.

[0042] The transmission qualification verification module is used to verify the transmission qualification of the partial discharge signal if the transmission effectiveness assessment result is qualified; otherwise, it performs optimization measures for the transmission process of the partial discharge signal.

[0043] The fire warning deviation assessment module is used to assess the fire warning deviation of the partial discharge signal and activate the corresponding fire protection measures when the transmission qualification verification result is determined to be unqualified.

[0044] Thirdly, this application provides a fire early warning system for key operating equipment in a substation based on the Internet of Things, including: one or more processors, and a memory;

[0045] The memory stores computer-readable instructions, which, when executed by one or more processors, perform the steps of any of the IoT-based fire early warning methods for critical operating equipment in substations as described in the above embodiments.

[0046] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0047] The IoT-based fire early warning method and system for critical substation operating equipment provided in this application sequentially performs electromagnetic interference assessment, acquisition accuracy analysis, transmission effectiveness assessment, and transmission qualification verification during the partial discharge signal transmission process. When any step fails to meet the requirements, a fire early warning is triggered or targeted acquisition and transmission optimization measures are implemented, along with a fire early warning deviation assessment. This allows for early identification and elimination of factors such as signal attenuation and distortion in the high-voltage electromagnetic interference environment of the substation, ensuring the integrity and reliability of the partial discharge signal. This not only improves the accuracy and timeliness of fire hazard identification and avoids fire early warning delays caused by signal quality degradation, but also enables early response and precise handling of fires affecting critical substation operating equipment, thereby effectively ensuring the safe and stable operation of the substation. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A flowchart illustrating the fire early warning method for key operating equipment in a substation based on the Internet of Things, provided in this application embodiment;

[0050] Figure 2 A logic diagram for adaptive filtering gain optimization of partial discharge signals in a fire early warning method for key operating equipment in substations based on the Internet of Things provided in this application embodiment;

[0051] Figure 3 A schematic diagram of the basic framework of the IoT-based fire early warning method for key operating equipment in substations provided in this application embodiment;

[0052] Figure 4 A schematic diagram of the structure of a fire early warning device for key operating equipment in a substation based on the Internet of Things provided in this application embodiment;

[0053] Figure 5 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] This application provides a fire early warning method for critical operating equipment in substations based on the Internet of Things (IoT). The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server and server clusters. Figure 1 As shown, the method may include the following steps:

[0056] S101: During the transmission of partial discharge signals in substation transformers, electromagnetic interference is assessed on the partial discharge signals.

[0057] Transformers are key operating equipment installed in substations for converting electrical voltage levels. Partial discharge signals are transient pulse electromagnetic signals generated by insulation defects or electric field concentrations within equipment, used to characterize changes in insulation condition. Electromagnetic interference assessment refers to the process by which computer equipment with data processing capabilities processes and analyzes collected partial discharge signals to determine whether the signals are affected by high-voltage electromagnetic interference.

[0058] Specifically, after receiving the raw signal uploaded by the transformer partial discharge sensor, the computer equipment can first perform filtering, amplification, and time-domain and frequency-domain feature extraction to obtain interference characteristic parameters such as signal-to-noise ratio, spectral distortion rate, and pulse repetition rate, and then compare them with preset interference thresholds.

[0059] The reason for performing electromagnetic interference assessment during partial discharge signal transmission is to promptly identify whether the signal is interfered with and the degree of interference in the complex high-voltage electromagnetic environment of the substation. This prevents fault characteristics from being inaccurately captured due to signal distortion, attenuation, or loss. Through this assessment, quality control can be performed before the signal enters subsequent analysis and early warning stages. If necessary, optimization measures or early warnings can be triggered to ensure the integrity and reliability of the data transmitted to the fire monitoring center. This fundamentally improves the accuracy of fire hazard identification and the timeliness of early warnings, ensuring the safe and stable operation of the substation.

[0060] S102: If the electromagnetic interference assessment result is deemed satisfactory, the accuracy of the partial discharge signal acquisition will be analyzed; otherwise, a fire alarm will be triggered.

[0061] Among them, the partial discharge signal electromagnetic interference assessment result is used to reflect the degree of interference caused by cable transmission loss to the collected transformer partial discharge signal under the electromagnetic interference environment of the substation; the acquisition accuracy analysis refers to the detection of the signal that has been judged to be qualified for interference in terms of sampling integrity, timing continuity, amplitude accuracy, etc.; the fire early warning refers to the alarm information sent by the computer equipment to the fire monitoring center when serious abnormalities are found in the collected data.

[0062] The computer equipment first performs an electromagnetic interference assessment on the received transformer partial discharge signal to determine whether the signal is significantly interfered with in the high-voltage electromagnetic environment of the substation. The assessment process may include calculating indicators such as signal-to-noise ratio, spectral integrity, and signal waveform distortion, and comparing them with preset thresholds or model outputs to determine the signal interference level.

[0063] When the electromagnetic interference assessment results are satisfactory, the computer equipment continues to analyze the accuracy of the partial discharge signal acquisition. This analysis includes checking the signal's data integrity, sampling time continuity, and amplitude consistency to ensure that the signal can be used for subsequent anomaly feature extraction and early warning judgment. If necessary, simple end-side corrections or retests are performed to further ensure signal quality.

[0064] When the electromagnetic interference assessment results are unqualified, the computer equipment will immediately trigger a fire alarm and upload the abnormal signal and diagnostic information to the fire monitoring center so that maintenance personnel can take timely on-site measures, such as inspection, interference shielding, or adjustment of the collected parameters.

[0065] Therefore, performing an accuracy analysis of the data acquisition after the electromagnetic interference assessment is qualified allows for secondary quality control before the signal enters the subsequent diagnosis process, ensuring the authenticity and integrity of the uploaded signal. At the same time, triggering a fire alarm in a timely manner when an uncorrectable anomaly is detected can significantly reduce missed detections and false alarms, improve the accuracy and response speed of fire hazard identification in substations, and thus improve the reliability and timeliness of the overall monitoring system.

[0066] S103: If the accuracy analysis results of the acquisition are deemed satisfactory, the transmission effectiveness of the partial discharge signal shall be evaluated; otherwise, optimization measures for the acquisition of the partial discharge signal shall be implemented.

[0067] Among them, transmission effectiveness assessment refers to detecting indicators such as integrity, packet loss rate, and latency of partial discharge signals during transmission from the acquisition end to the fire monitoring center; acquisition optimization measures refer to the automatic adjustment of sampling parameters, gain, or resampling by computer equipment when there are abnormalities or deficiencies in signal acquisition, in order to improve signal quality and availability.

[0068] After the computer equipment completes the accuracy analysis of the partial discharge signal acquisition and determines the results to be acceptable, it will further evaluate the signal transmission effectiveness. This evaluation may include assessing the integrity, packet loss rate, latency, and waveform stability of the signal during wireless network transmission to ensure reliable signal transmission from the transformer to the fire monitoring center and its usability for subsequent anomaly analysis. During the transmission effectiveness evaluation, the computer equipment can quantify signal transmission quality by comparing the number of sent and received data packets, timestamp continuity, and checksums. Simultaneously, the equipment can record network conditions and transmission anomaly information, providing data support for subsequent optimization.

[0069] When the accuracy analysis results of the data acquisition are unsatisfactory, the computer equipment will automatically implement acquisition optimization measures, including adjusting sampling parameters, increasing signal gain, repeating sampling, or enabling redundant acquisition channels, thereby improving signal quality and ensuring data availability.

[0070] By first assessing the accuracy of the acquired data, the computer equipment can determine whether the signal quality meets the requirements for subsequent transmission and anomaly analysis. For qualified signals, transmission effectiveness evaluation ensures the integrity and reliability of data during transmission, thus avoiding misjudgments or delays in early warnings due to network packet loss or latency. For unqualified signals, automatic acquisition optimization measures can promptly improve signal quality, ensuring that critical partial discharge information is captured and uploaded. Overall, this step effectively improves the reliability of signal acquisition and transmission in high-voltage electromagnetic environments, ensuring the timeliness and accuracy of fire early warnings, and enhancing the overall stability and security of the substation's critical equipment monitoring system.

[0071] S104: If the transmission effectiveness assessment result is deemed satisfactory, the transmission qualification of the partial discharge signal shall be verified; otherwise, optimization measures for the transmission process of the partial discharge signal shall be implemented.

[0072] Transmission qualification verification refers to further verifying whether the signal remains stable, intact, and usable during actual transmission, provided that the transmission effectiveness assessment is qualified. Transmission process optimization measures refer to the automatic actions taken by computer equipment when the transmission effectiveness assessment is unqualified, such as signal retransmission, adding redundancy, or adjusting the transmission channel, in order to improve the signal transmission quality.

[0073] After assessing the transmission effectiveness of the partial discharge signal and determining that the result is acceptable, the computer equipment will further verify the signal's transmission compliance. This verification may include comparing the integrity of data packets at the sending and receiving ends, the continuity of timestamps, and whether the signal amplitude remains stable, thereby confirming the final availability of the signal during transmission. During the transmission compliance verification process, the computer equipment can record the network conditions and anomaly information along the signal transmission path, and use algorithms to analyze whether there are minor delays, packet losses, or distortions, quantitatively judging the signal quality. If the verification result shows that the signal is completely qualified, it can be directly used for subsequent anomaly feature extraction and fire alarm judgment.

[0074] When the transmission effectiveness assessment result is unsatisfactory, the computer equipment will perform transmission process optimization measures, such as increasing data redundancy, adjusting the transmission frequency, retransmitting abnormal data packets, or using a more reliable transmission channel, to restore the integrity and stability of the signal.

[0075] By first assessing the transmission effectiveness, the computer equipment can identify whether there are problems such as signal loss, delay, or distortion during transmission. For qualified signals, transmission qualification verification can further ensure the stability and reliability of the signal at the transmission end, thereby ensuring the accuracy of subsequent anomaly analysis and fire early warning judgment. For unqualified signals, implementing transmission process optimization measures can promptly repair transmission anomalies and avoid the loss or delay of key partial discharge information, thereby improving the timeliness, reliability, and overall safety of the substation fire monitoring system.

[0076] S105: When the transmission qualification verification result is determined to be unqualified, a fire warning deviation assessment shall be performed on the partial discharge signal, and corresponding fire protection measures shall be initiated.

[0077] Among them, fire early warning deviation assessment refers to the quantitative analysis of the impact of abnormal or deviated signal transmission on fire early warning judgment, including the assessment of indicators such as signal amplitude, frequency, waveform and delay; fire protection measures refer to the operation of computer equipment automatically or remotely triggering alarm, sprinkler, automatic fire extinguishing device activation or sending alarm information to operation and maintenance personnel when potential risks are found in the deviation assessment.

[0078] After the computer equipment completes the partial discharge signal transmission qualification verification and determines that the result is unqualified, it will perform a fire warning deviation assessment on the signal. The deviation assessment may include analyzing the signal amplitude, frequency, waveform, and abnormal differences that occur during acquisition and transmission, and comparing the assessment results with preset safety thresholds or historical reference data to determine the potential impact of the current signal deviation on the fire warning judgment.

[0079] During the deviation assessment process, computer equipment can calculate deviation indicators such as signal delay, amplitude attenuation rate, or abnormal pulse frequency through algorithms, and perform quantitative analysis on situations that may lead to misjudgment or missed judgment. Simultaneously, the equipment can generate warning data packets and send them to the fire monitoring center so that maintenance personnel can quickly obtain abnormal information.

[0080] When the deviation assessment results show a significant deviation, the computer equipment will activate the corresponding fire protection measures, including triggering alarms, activating automatic fire extinguishing devices, turning on the sprinkler system, or sending alarm information such as text messages and emails to maintenance personnel, so as to respond promptly to potential fire hazards.

[0081] By assessing the fire warning deviation of partial discharge signals that fail the transmission qualification verification, the computer equipment can promptly determine the potential impact of signal anomalies on the warning results and immediately activate corresponding fire-fighting measures when deviations are detected that may lead to misjudgments or omissions. This operation ensures that even in the event of signal transmission anomalies, an effective emergency response can be taken quickly, preventing fire hazards from being overlooked or delayed in handling, thereby improving the safety of key equipment in substations, the reliability of fire monitoring systems, and the timeliness of warnings.

[0082] In the above embodiments, by sequentially performing electromagnetic interference assessment, acquisition accuracy analysis, transmission effectiveness assessment, and transmission qualification verification during the partial discharge signal transmission process, and triggering fire alarms or implementing targeted acquisition and transmission optimization measures and fire alarm deviation assessments when any link fails to meet the requirements, it is possible to identify and eliminate influencing factors such as signal attenuation and distortion in advance under the high-voltage electromagnetic interference environment of the substation, ensuring the integrity and reliability of the partial discharge signal. This not only improves the accuracy and timeliness of fire hazard identification and avoids fire alarm delays caused by signal quality degradation, but also enables early response and precise handling of fires in critical operating equipment of the substation, thereby effectively ensuring the safe and stable operation of the substation.

[0083] In one embodiment, the step of determining whether the electromagnetic interference assessment result is acceptable includes:

[0084] Deviation quantization is performed between the narrowband peak value and the preset narrowband peak value of the partial discharge signal;

[0085] When the deviation quantification result is less than the electromagnetic interference safety threshold, the electromagnetic interference assessment result is qualified.

[0086] Among them, the narrowband peak value refers to the local maximum amplitude value of the partial discharge signal in a specific frequency band, which is obtained by collecting multiple samples from an ultra-high frequency sensor and taking the average value; the preset narrowband peak value is a reference peak value set according to historical data and safety standards, used for deviation comparison; the electromagnetic interference safety threshold is a preset allowable deviation range. When the deviation quantification result is less than this threshold, it is considered that the signal transmission is not significantly interfered with.

[0087] Specifically, the computer equipment uses ultra-high frequency sensors deployed on the transformers in the substation to monitor the narrowband peak values ​​of the partial discharge signal in each characteristic frequency band in real time. It then averages the peak values ​​collected multiple times to obtain the current narrowband peak value of the transformer's partial discharge signal. Subsequently, the computer equipment performs a difference calculation between this narrowband peak value and a preset narrowband peak value to quantify the deviation of the partial discharge signal.

[0088] During the deviation quantification process, the computer equipment can simultaneously record the peak value changes and deviations of different frequency bands, and compare them with historical data or safety thresholds. If the deviation quantification result is less than the preset electromagnetic interference safety threshold, the computer equipment will determine that the electromagnetic interference assessment result is qualified and upload the partial discharge data collected this time to the fire monitoring center. The uploaded data can then be used for partial discharge acquisition accuracy analysis to further determine signal integrity and reliability, thereby supporting subsequent transmission effectiveness assessment and fire early warning judgment.

[0089] By quantifying the deviation between the narrowband peak value and the preset peak value of the partial discharge signal, and combining this with electromagnetic interference assessment results, computer equipment can accurately identify interference caused by cable transmission loss, keenly capture subtle abnormal features in the signal, and improve the response capability of the partial discharge signal to changes in the electromagnetic environment. Simultaneously, this method effectively avoids the risk of invalid data transmission and misjudgment caused by electromagnetic interference, ensuring that critical equipment such as transformers can safely and promptly trigger fire alarms in high-voltage electromagnetic environments, thereby significantly improving the safety, stability, and monitoring reliability of substation operation.

[0090] In one embodiment, the step of determining whether the data collection accuracy analysis results are acceptable includes:

[0091] The offset of partial discharge parameters is obtained by quantizing the proportion of partial discharge signal parameters and standard partial discharge signal parameters.

[0092] The partial discharge parameter offset and the weighted data of partial discharge characteristics are weighted and fused to obtain the partial discharge signal deviation index.

[0093] When the partial discharge signal deviation index is less than the partial discharge signal accuracy threshold, the acquisition accuracy analysis result is qualified.

[0094] The standard partial discharge signal parameters are represented by the average value of partial discharge signal parameters over a historical time period, and the proportion quantification means performing a ratio calculation. Partial discharge signal parameters include transformer discharge charge deviation, transformer discharge repetition rate deviation, transformer discharge phase deviation, and qualified electromagnetic interference assessment score; standard partial discharge signal parameters include standard transformer discharge charge deviation, standard transformer discharge repetition rate deviation, standard transformer discharge phase deviation, and a preset electromagnetic interference assessment score safety threshold; partial discharge parameter offsets include transformer discharge charge deviation rate, transformer discharge repetition rate deviation rate, transformer discharge phase deviation rate, and electromagnetic interference assessment score deviation rate; the partial discharge signal deviation index is used to reflect the combined impact of standard partial discharge signal parameters and partial discharge signal parameters on the qualification of partial discharge signal acquisition.

[0095] Specifically, the expression for the transformer discharge charge deviation rate is:

[0096]

[0097] H = 1, 2, ..., G, where H represents the preset analysis time period number, G represents the total number of preset analysis time periods, and A 1 (H) represents the transformer discharge charge deviation rate in the Hth preset analysis time period, A(H) represents the transformer discharge charge deviation in the Hth preset analysis time period, and A(0) represents the standard transformer discharge charge deviation. The total charge of the transformer partial discharge signal within the preset time period is monitored by a high-frequency current transformer, and the absolute value of the difference between the transformer partial discharge signal charge and the preset discharge signal charge is taken as the transformer discharge charge deviation. The preset discharge signal charge is represented by the average value of the total charge of the transformer partial discharge signal in the historical time period. The units of the transformer discharge charge deviation and the standard transformer discharge charge deviation are both coulombs (C).

[0098] The expression for the transformer discharge repetition rate deviation rate is:

[0099]

[0100] B 2 (H) represents the transformer discharge repetition rate deviation rate in the Hth preset analysis time period, B(H) represents the transformer discharge repetition rate deviation in the Hth preset analysis time period, and B(0) represents the standard transformer discharge repetition rate. The number of partial discharge signals actually collected within the preset time period is monitored by a high-speed data acquisition card in conjunction with a pulse counter. The absolute value of the difference between this number and the average number of partial discharge signals in the historical time period in the database is taken as the transformer discharge repetition rate deviation. The units of the transformer discharge repetition rate deviation and the standard transformer discharge repetition rate are both times / second.

[0101] Specifically, the expression for the transformer discharge phase deviation rate is:

[0102]

[0103] C 3 (H) represents the transformer discharge phase deviation rate during the Hth preset analysis time period, C(H) represents the transformer discharge phase deviation during the Hth preset analysis time period, and C(0) represents the standard transformer discharge phase. The phase of each partial discharge signal within the preset time period is monitored by a phase synchronization acquisition device and a digital oscilloscope. The absolute value of the difference between the average value and the phase of the partial discharge signal in the historical time period in the database is taken as the transformer discharge phase deviation. The units of transformer discharge phase deviation and standard transformer discharge phase are both degrees.

[0104] Specifically, the expression for the electromagnetic interference assessment score deviation rate is:

[0105]

[0106] D 4 (H) represents the deviation rate of the partial discharge electromagnetic interference assessment score in the Hth preset analysis time period, D(H) represents the qualified electromagnetic interference assessment score, and D(0) represents the preset electromagnetic interference assessment score safety threshold. The qualified electromagnetic interference assessment score is represented by the electromagnetic interference assessment score that is less than the electromagnetic interference safety threshold. The electromagnetic interference assessment score is quantified based on the electromagnetic interference assessment results of the partial discharge signal.

[0107] The partial discharge signal deviation index is obtained by weighted fusion of partial discharge parameter offset and partial discharge feature weighted data.

[0108] The partial discharge signal deviation index is obtained through the following method:

[0109] (H)

[0110] In the formula, PDSI(H) represents the partial discharge signal deviation index of the Hth preset analysis time period, w1 represents the preset transformer discharge charge deviation weight coefficient, w2 represents the preset transformer discharge repetition rate deviation weight coefficient, w3 represents the preset transformer discharge phase deviation weight coefficient, and w4 represents the preset electromagnetic interference evaluation score deviation weight coefficient.

[0111] If the partial discharge signal deviation index is less than the partial discharge signal accuracy threshold, then the effectiveness of the partial discharge signal transmission is evaluated; otherwise, partial discharge signal acquisition optimization measures are taken. This helps to improve the reliability of the communication link and ensure high-fidelity acquisition and accurate analysis of transformer partial discharge signals under strong electromagnetic interference.

[0112] It should be added that the preset partial discharge feature weighting data includes the transformer discharge charge deviation weighting coefficient, the transformer discharge repetition rate deviation weighting coefficient, the transformer discharge phase deviation weighting coefficient, and the electromagnetic interference assessment score deviation weighting coefficient, which are used to reflect the degree of influence of partial discharge signal parameters on the qualification of partial discharge signal acquisition. This application provides a mapping group, which is obtained from a database and contains mapping sets, and is pre-set by professional technicians. The mapping relationships in the mapping set can be one-to-one or many-to-one. Specifically, partial discharge signal parameters and preset partial discharge feature weighting data are mapped one-to-one or many-to-one, where the preset partial discharge feature weighting data is represented by the proportion of the corresponding partial discharge signal parameters, with a value range of 0-1. By inputting real-time partial discharge signal parameters into the corresponding mapping group, the corresponding preset partial discharge feature weighting data can be obtained, thereby accurately quantifying the degree of influence of partial discharge parameter offset parameters on the qualification of partial discharge signal acquisition, effectively improving the accuracy and adaptability of signal acquisition quality assessment.

[0113] In this embodiment, the partial discharge parameter offsets are not isolated but interconnected, requiring correlation analysis to describe their combined effects. Specifically, the larger the partial discharge parameter offset, the greater its impact on the qualification of partial discharge signal acquisition, and the more significant the coupling effect between different parameter offsets, leading to a non-linear superposition and amplification of partial discharge signal distortion. This correlation between partial discharge parameter offsets is quantified through weighted fusion, enabling each partial discharge parameter offset to form a synergistic mapping relationship in the partial discharge signal deviation index, thus avoiding evaluation bias caused by single-parameter analysis.

[0114] Correlation analysis of partial discharge signal parameters can accurately capture the comprehensive impact of multiple parameters on the qualification of partial discharge signal acquisition. The transformer discharge charge deviation rate and discharge repetition rate deviation rate are generally positively correlated; that is, the larger the transformer discharge charge deviation rate, the greater the deviation between the total charge generated by partial discharge per unit time and the total charge of transformer partial discharge signals over historical periods. This may lead to abnormal fluctuations in the frequency of partial discharge, resulting in a corresponding increase in the transformer discharge repetition rate deviation rate. When the electromagnetic interference assessment score deviation rate increases, it indicates that the interference from the external high-voltage electromagnetic environment on partial discharge signal acquisition is intensified. This interference directly affects the accuracy of the phase synchronization acquisition device, leading to an increase in the transformer discharge phase deviation rate. When the transformer discharge charge deviation increases, the electric field distribution during the partial discharge process changes, indirectly causing the transformer discharge phase distribution to deviate from the standard state, thus reducing the qualification of partial discharge signal acquisition and decreasing the qualified electromagnetic interference assessment score. Correlation analysis between parameters helps to quantify the qualification of partial discharge signal acquisition and improve the comprehensiveness of partial discharge signal quality assessment.

[0115] In one embodiment, the step of implementing partial discharge signal acquisition optimization measures includes:

[0116] Based on the current voltage of the transformer and the partial discharge signal deviation index, obtain the signal change rate gain adjustment ratio;

[0117] If the signal rate of change gain adjustment ratio exceeds the preset maximum gain threshold, an alarm for abnormal filtering parameters will be sent; otherwise, the signal rate of change gain adjustment ratio will be used as the qualified gain adjustment value.

[0118] After gradually increasing the signal change rate gain of the partial discharge signal by adjusting the amplitude corresponding to the qualified gain value, if the partial discharge signal deviation index is not greater than the partial discharge signal accuracy threshold, the transmission effectiveness of the partial discharge signal is evaluated.

[0119] Otherwise, the partial discharge signal sampling rate adjustment ratio is obtained based on the highest frequency of the partial discharge signal and the partial discharge signal deviation index;

[0120] If the partial discharge signal sampling rate adjustment ratio exceeds the preset gain threshold range, an abnormal sampling rate adjustment alarm will be sent; otherwise, the partial discharge signal sampling rate adjustment ratio will be used as the qualified partial discharge signal sampling rate adjustment value.

[0121] If the sampling rate of the partial discharge signal is gradually increased by adjusting the sampling rate of the qualified partial discharge signal to the corresponding amplitude, and the partial discharge signal deviation index is greater than the partial discharge signal accuracy threshold, a sampling rate adjustment failure alarm is triggered; otherwise, the transmission effectiveness of the partial discharge signal is evaluated.

[0122] Specifically, the adaptive filter gain optimization process is as follows: The current transformer voltage and partial discharge signal deviation index are input into the database. The corresponding signal change rate gain adjustment ratio is obtained from the database. The database contains a mapping set reflecting the mapping relationship between different transformer operating voltages, partial discharge signal deviation indices, and the optimal signal change rate gain adjustment ratio. It is determined whether the signal change rate gain adjustment ratio exceeds a preset maximum gain threshold. If so, an abnormal filter parameter alarm is sent; otherwise, the corresponding signal change rate gain adjustment ratio is marked as a qualified gain adjustment value. The preset maximum gain threshold is pre-set by designated personnel. The signal change rate gain is gradually increased by the magnitude corresponding to the qualified gain adjustment value. This helps to avoid partial discharge signal distortion and feature aberration caused by sudden gain changes through dynamic adaptive bandpass filtering optimization, thereby effectively improving the reliability and timeliness of transformer identification. After adaptive filter gain optimization, the partial discharge signal deviation index is re-acquired and judged. If the partial discharge signal deviation index is still greater than the partial discharge signal accuracy upload threshold, an abnormal gain alarm is sent. The partial discharge signal accuracy upload threshold is represented by the average value of the partial discharge signal deviation index over a historical time period. Otherwise, dynamic adjustment and optimization of the partial discharge signal sampling rate is initiated.

[0123] Specifically, the process of dynamically adjusting and optimizing the sampling rate of partial discharge signals is as follows: The highest frequency and deviation index of the partial discharge signal are input into a database. The sampling rate adjustment ratio is obtained from the database. The database contains a mapping set reflecting the mapping relationship between the highest frequency of the partial discharge signal, the deviation index, and the corresponding optimal sampling rate adjustment ratio. It is determined whether the sampling rate adjustment ratio exceeds a preset gain threshold range. If so, a sampling rate adjustment anomaly alarm is sent; otherwise, the corresponding sampling rate adjustment ratio is marked as a qualified partial discharge signal sampling rate adjustment value. The preset gain threshold range is pre-set by designated personnel and includes both ends of the range. The sampling rate of the partial discharge signal is gradually increased by the magnitude corresponding to the qualified sampling rate adjustment value. This helps to dynamically match the signal bandwidth and avoid the loss of feature information due to insufficient sampling, thereby ensuring that the characteristic changes of weak discharge signals can be captured in the early stages of transformer defect development. After dynamic adjustment and optimization of the sampling rate, the deviation index of the partial discharge signal is re-acquired and judged. If the deviation index is still greater than the accuracy upload threshold of the partial discharge signal, a sampling rate adjustment failure alarm is triggered; otherwise, a partial discharge signal transmission effectiveness assessment is performed.

[0124] In this embodiment, by optimizing the adaptive filtering gain and dynamically adjusting the sampling rate of the partial discharge signal, the distortion effect of electromagnetic interference on signal characteristics can be effectively suppressed while ensuring signal strength. By dynamically matching the interference frequency characteristics through adaptive filtering and accurately capturing the high-frequency components of the discharge signal in combination with sampling rate adjustment, signal aliasing and feature loss are avoided, and the signal-to-noise ratio and feature integrity of the partial discharge signal are significantly improved.

[0125] In one example, such as Figure 2 The diagram shown is a logic diagram for adaptive filtering gain optimization of partial discharge signals in a fire early warning method for key operating equipment in a substation based on the Internet of Things, provided in an embodiment of this application. Figure 2 The specific process of adaptive filter gain optimization is as follows: It is determined whether the signal change rate gain adjustment ratio exceeds the preset maximum gain threshold. If so, an abnormal filter parameter alarm is sent; otherwise, the corresponding signal change rate gain adjustment ratio is marked as a qualified gain adjustment value. The signal change rate gain is gradually increased by the magnitude corresponding to the qualified gain adjustment value. After adaptive filter gain optimization, the partial discharge signal deviation index is re-acquired and judged. If the partial discharge signal deviation index is still greater than the partial discharge signal accuracy upload threshold, dynamic adjustment optimization of the partial discharge signal sampling rate is initiated; otherwise, the effectiveness of partial discharge signal transmission is evaluated.

[0126] In one embodiment, the step of determining whether the transmission validity assessment result is qualified includes:

[0127] Determine the evaluation index for the transmission effectiveness deviation of partial discharge signals;

[0128] When the transmission effectiveness deviation assessment index is within the transmission effectiveness safety threshold range, the transmission effectiveness assessment result is qualified.

[0129] Specifically, the transmission effectiveness deviation assessment index is obtained; the actual transmission time of each partial discharge signal to the fire monitoring center is monitored using a high-precision time synchronization device, such as GPS (Global Positioning System), and the difference between its average value and the average value of the actual transmission time to the fire monitoring center for historical time periods in the database is used as the transmission effectiveness deviation assessment index, which reflects the time synchronization accuracy and transmission stability during the transmission of partial discharge signals; it is determined whether the transmission effectiveness deviation assessment index is within the transmission effectiveness safety threshold range. If it is, a partial discharge signal transmission qualification prompt is sent; otherwise, transmission effectiveness optimization measures are taken. By conducting partial discharge signal transmission effectiveness assessment, it is helpful to quickly correct transmission delay and packet loss problems, and ensure the timeliness and integrity of signal transmission in the electromagnetic environment. The transmission effectiveness safety threshold range is set by preset personnel, and the preset transmission effectiveness safety threshold range includes both ends of the range.

[0130] In this example, by monitoring the actual transmission time of the partial discharge signal, the time synchronization accuracy and stability during signal transmission can be accurately measured, and the interference and loss of the signal transmission caused by the complex electromagnetic environment can be eliminated in a timely manner. This not only effectively ensures the timeliness and integrity of the partial discharge signal during transmission, but also ensures that the monitoring data can reach the fire monitoring center accurately and in a timely manner, providing reliable data transmission support for the real-time assessment of the substation equipment status and fire early warning.

[0131] In one embodiment, the step of implementing measures to optimize the transmission process of partial discharge signals includes:

[0132] If the transmission effectiveness deviation evaluation index is greater than the preset transmission effectiveness evaluation threshold, the partial discharge signal will be used as the first-level transmission data; otherwise, the partial discharge signal will be used as the second-level transmission data.

[0133] After performing first-level data transmission on first-level transmission data and second-level data transmission on second-level transmission data, if the transmission effectiveness deviation evaluation index is not within the transmission effectiveness safety threshold range, a partial transmission optimization failure alarm is sent; otherwise, a partial transmission qualification verification is performed on the partial discharge signal.

[0134] Specifically, transmission effectiveness optimization measures include transmission link optimization and transmission filtering optimization. The specific process of transmission link optimization is as follows: It is determined whether the transmission effectiveness evaluation index is greater than the preset transmission effectiveness evaluation threshold, where the preset transmission effectiveness evaluation threshold is represented by the average value of the transmission effectiveness evaluation index over a historical time period. If the transmission effectiveness evaluation index is greater than the preset transmission effectiveness evaluation threshold, the corresponding partial discharge data is marked as Level 1 transmission data; otherwise, the corresponding partial discharge data is marked as Level 2 transmission data. A prompt is sent to preset personnel to sequentially transmit Level 1 and Level 2 transmission data. Transmitting Level 1 and Level 2 transmission data sequentially through transmission link optimization is a key strategy to ensure data transmission effectiveness. This helps to prioritize the transmission efficiency and reliability of high-value data and facilitates the rapid and reliable uploading of partial discharge characteristic parameters in complex electromagnetic environments, thereby significantly improving the timeliness and completeness of data received by the fire monitoring center.

[0135] Specifically, the transmission filtering optimization process is as follows: First-level data transmission is performed based on LMS (Least Mean Squares) to improve its effectiveness; second-level data transmission is also performed based on LMS to improve its effectiveness; after transmission effectiveness optimization, the transmission effectiveness deviation evaluation index is re-acquired and judged. If the transmission effectiveness deviation evaluation index is still not within the transmission effectiveness safety threshold range, a local transmission optimization failure alarm is sent; otherwise, local transmission qualification verification is performed. The use of the LMS algorithm helps to dynamically offset narrowband interference and multipath fading effects in the transmission link through an adaptive filtering mechanism. In first-level data transmission, burst impulse noise is suppressed first, and in second-level data transmission, channel equalization parameters are optimized, including step size factor, filter order, and tap coefficient update rate, thereby improving the reliability of transformer fire warning data transmission.

[0136] In this example, the joint analysis of transmission link optimization and transmission filtering optimization helps to ensure the priority transmission of partial discharge signals in the case of time delay fluctuations and data packet loss in the transmission link of transformer partial discharge signals under strong electromagnetic interference environment. This significantly improves the effectiveness of partial discharge signal transmission and provides reliable transmission guarantee for second-level early warning and accurate location of transformer faults.

[0137] In one embodiment, the step of determining that the transmission qualification verification result is unqualified includes:

[0138] Obtain the fire response deviation index of fire-fighting equipment in substations;

[0139] If the fire response deviation index is not within the preset transmission delay safety threshold range, the transmission qualification verification result is unqualified.

[0140] Specifically, the fire response deviation index is obtained. This index is represented by the difference between the reception time of the fire equipment monitored by the timer and the preset reception time. It reflects the degree of deviation between the actual response efficiency of the fire equipment and the preset response efficiency. Through partial transmission qualification verification, it helps to accurately detect potential response anomalies in the fire warning system, promptly identify response lag problems caused by signal transmission delays or environmental interference, and avoid the failure of fire protection measures due to untimely response. The system then determines whether the fire response deviation index is within the preset transmission delay safety threshold range. This preset transmission delay safety threshold range is set by preset personnel and includes both ends of the range. If the fire response deviation index is within the preset transmission delay safety threshold range, the partial discharge signal is uploaded to the data center; otherwise, a fire warning deviation assessment is performed.

[0141] In one embodiment, the steps of assessing the fire warning deviation of the partial discharge signal and activating the corresponding fire-fighting measures include:

[0142] If the fire response deviation index is greater than the preset transmission delay safety threshold, then the first-level fire protection measures will be activated.

[0143] If the fire response deviation index is not greater than the preset transmission delay safety threshold, then the secondary fire protection measures will be activated.

[0144] Specifically, the system determines whether the fire response deviation index is greater than a preset transmission delay safety threshold. If the preset transmission delay safety threshold is not within its range, and the fire response deviation index is greater than the preset transmission delay safety threshold, the corresponding fire warning signal is marked as a Level 1 fire warning signal, and Level 1 fire-fighting measures are activated. Level 1 fire-fighting measures involve sending a Level 1 sprinkler command to the preset sprinkler device. If the fire response deviation index is not greater than the transmission delay safety threshold, the corresponding fire warning signal is marked as a Level 2 fire warning signal, and Level 2 fire-fighting measures are activated. Level 2 fire-fighting measures involve sending a Level 2 sprinkler command to the preset sprinkler device. For example, personnel can set different sprinkler volumes based on the received Level 1 and Level 2 fire warning signals. If the sprinkler volume corresponding to the Level 1 fire warning signal is greater than that corresponding to the Level 2 fire warning signal, it indicates that the risk level corresponding to the Level 1 fire warning signal is greater than that corresponding to the Level 2 fire warning signal, thus improving the efficiency and rationality of fire emergency response.

[0145] In this example, by assessing the fire response deviation index in stages and dynamically adjusting the fire warning level, the timeliness of the fire warning is enhanced in a high-voltage electromagnetic interference environment. This helps to dynamically adjust the triggering priority and execution intensity of fire protection measures by monitoring response delay fluctuations in real time, avoiding warning failure caused by signal transmission delay due to electromagnetic interference. Thus, the effect of improving the timeliness of transformer fire warnings is achieved in a high-voltage electromagnetic interference environment in substations.

[0146] like Figure 3 The diagram shown is a basic framework schematic of the IoT-based fire early warning method for key operating equipment in substations provided in this application embodiment. Figure 3It can be seen that by monitoring the electromagnetic interference assessment results of partial discharge signals, if the electromagnetic interference assessment result of the partial discharge signal is not less than the electromagnetic interference safety threshold, a fire alarm is triggered; otherwise, the collected partial discharge data is uploaded to the fire monitoring center, and the accuracy of partial discharge acquisition is analyzed. If the deviation index of the partial discharge signal is not less than the accuracy threshold of the partial discharge signal, partial discharge signal acquisition optimization measures are taken, including adaptive filter gain optimization and dynamic adjustment optimization of the partial discharge signal sampling rate; otherwise, the effectiveness of partial discharge signal transmission is assessed. If the transmission effectiveness deviation assessment index is not within the transmission effectiveness safety threshold range, transmission effectiveness optimization measures are taken, namely transmission link optimization and transmission filtering optimization; otherwise, the qualification verification of partial signal transmission is performed. If the monitored fire response deviation index is within the preset transmission delay safety threshold range, the partial discharge signal is uploaded to the data center; otherwise, a fire warning deviation assessment is performed. If the fire response deviation index is greater than the preset transmission delay safety threshold, the corresponding fire warning signal is marked as a level one fire warning signal, and level one fire protection measures are initiated. If the fire response deviation index is not greater than the transmission delay safety threshold, the corresponding fire warning signal is marked as a level two fire warning signal, and level two fire protection measures are initiated.

[0147] The following describes the IoT-based fire early warning device for critical operating equipment in substations provided in the embodiments of this application. The IoT-based fire early warning device for critical operating equipment in substations described below can be referred to in correspondence with the IoT-based fire early warning method for critical operating equipment in substations described above. Figure 4 As shown, this application provides a fire early warning device for key operating equipment in a substation based on the Internet of Things. The device includes:

[0148] Electromagnetic interference assessment module 201 is used to assess the electromagnetic interference of partial discharge signals during the transmission of partial discharge signals from transformers in substations.

[0149] The accuracy analysis module 202 is used to perform accuracy analysis on the partial discharge signal if the electromagnetic interference assessment result is determined to be qualified; otherwise, it triggers a fire alarm.

[0150] The transmission effectiveness assessment module 203 is used to assess the transmission effectiveness of the partial discharge signal if the accuracy analysis result of the acquisition is qualified; otherwise, it performs acquisition optimization measures for the partial discharge signal.

[0151] The transmission qualification verification module 204 is used to verify the transmission qualification of the partial discharge signal if the transmission effectiveness assessment result is qualified; otherwise, it performs optimization measures for the transmission process of the partial discharge signal.

[0152] The fire warning deviation assessment module 205 is used to assess the fire warning deviation of the partial discharge signal and activate the corresponding fire protection measures when the transmission qualification verification result is determined to be unqualified.

[0153] In one embodiment, the data acquisition accuracy analysis module 202 includes:

[0154] The deviation quantization unit is used to perform deviation quantization between the narrowband peak value and the preset narrowband peak value of the partial discharge signal;

[0155] The electromagnetic interference assessment result determination unit is used to determine whether the electromagnetic interference assessment result is qualified when the deviation quantification result is less than the electromagnetic interference safety threshold.

[0156] In one embodiment, the transmission validity assessment module 203 includes:

[0157] The partial discharge parameter offset acquisition unit is used to obtain the partial discharge parameter offset by quantizing the ratio of the partial discharge signal parameters of the partial discharge signal to the standard partial discharge signal parameters.

[0158] The partial discharge signal deviation index determination unit is used to perform weighted fusion of partial discharge parameter offset and partial discharge feature weighted data to obtain the partial discharge signal deviation index.

[0159] The accuracy analysis result determination unit is used to determine the accuracy analysis result as long as the partial discharge signal deviation index is less than the partial discharge signal accuracy threshold.

[0160] In one embodiment, the transmission validity assessment module 203 includes:

[0161] The signal change rate gain adjustment ratio acquisition unit is used to acquire the signal change rate gain adjustment ratio based on the current voltage of the transformer and the partial discharge signal deviation index.

[0162] The qualified gain adjustment value determination unit is used to send an alarm for abnormal filtering parameters if the gain adjustment ratio of the signal rate of change exceeds the preset maximum gain threshold; otherwise, the gain adjustment ratio of the signal rate of change is used as the qualified gain adjustment value.

[0163] The signal rate of change gain amplification unit is used to gradually increase the signal rate of change gain of the partial discharge signal by the amplitude corresponding to the qualified gain adjustment value. If the partial discharge signal deviation index is not greater than the partial discharge signal accuracy threshold, the transmission effectiveness of the partial discharge signal is evaluated.

[0164] The partial discharge signal sampling rate adjustment ratio acquisition unit is used to otherwise acquire the partial discharge signal sampling rate adjustment ratio based on the highest frequency of the partial discharge signal and the partial discharge signal deviation index.

[0165] The qualified partial discharge signal sampling rate adjustment value determination unit is used to send a sampling rate adjustment abnormal alarm if the sampling rate adjustment ratio of the partial discharge signal exceeds the preset gain threshold range; otherwise, the sampling rate adjustment ratio of the partial discharge signal is used as the qualified partial discharge signal sampling rate adjustment value.

[0166] The signal sampling rate increase unit is used to gradually increase the signal sampling rate of the partial discharge signal by the amplitude corresponding to the qualified partial discharge signal sampling rate adjustment value. If the partial discharge signal deviation index is greater than the partial discharge signal accuracy threshold, a sampling rate adjustment failure alarm is triggered; otherwise, the transmission effectiveness of the partial discharge signal is evaluated.

[0167] In one embodiment, the transmission qualification verification module 204 includes:

[0168] The transmission effectiveness deviation assessment index determination unit is used to determine the transmission effectiveness deviation assessment index of the partial discharge signal.

[0169] The transmission effectiveness assessment result determination unit is used to determine the transmission effectiveness assessment result as qualified when the transmission effectiveness deviation assessment index is within the transmission effectiveness safety threshold range.

[0170] In one embodiment, the transmission qualification verification module 204 includes:

[0171] The data transmission level determination unit is used to determine whether the partial discharge signal is used as first-level data if the transmission effectiveness deviation evaluation index is greater than the preset transmission effectiveness evaluation threshold, and otherwise, the partial discharge signal is used as second-level data.

[0172] The data transmission unit is used to perform first-level data transmission on first-level transmission data and second-level data transmission on second-level transmission data. If the transmission effectiveness deviation evaluation index is not within the transmission effectiveness safety threshold range, a partial transmission optimization failure alarm is sent; otherwise, the partial discharge signal is verified for partial transmission qualification.

[0173] In one embodiment, the fire warning deviation assessment module 205 includes:

[0174] The fire response deviation index acquisition unit is used to acquire the fire response deviation index of fire-fighting equipment in the substation.

[0175] The transmission qualification verification result determination unit is used to determine the transmission qualification verification result as unqualified if the fire response deviation index is not within the preset transmission delay safety threshold range.

[0176] In one embodiment, the steps of assessing the fire warning deviation of the partial discharge signal and activating the corresponding fire-fighting measures include:

[0177] The Level 1 fire protection measure activation unit is used to activate Level 1 fire protection measures if the fire response deviation index is greater than the preset transmission delay safety threshold.

[0178] The secondary fire protection measure activation unit is used to activate the secondary fire protection measures if the fire response deviation index is not greater than the preset transmission delay safety threshold.

[0179] In one embodiment, this application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the IoT-based fire early warning method for critical operating equipment in substations as described in any of the above embodiments.

[0180] In one embodiment, this application also provides an Internet of Things (IoT)-based fire early warning system for critical operating equipment in substations. The IoT-based fire early warning system for critical operating equipment in substations stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors perform the steps of the IoT-based fire early warning method for critical operating equipment in substations as described in any of the above embodiments.

[0181] Indicatively, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the internal structure of a fire early warning system for critical operating equipment in a substation based on the Internet of Things (IoT), provided as an embodiment of this application. This IoT-based fire early warning system 300 for critical operating equipment in a substation can be provided as a server. (Refer to...) Figure 5 The IoT-based fire early warning system 300 for critical substation operating equipment includes a processing component 302, which further includes one or more processors, and memory resources represented by a memory 301 for storing instructions, such as applications, that can be executed by the processing component 302. The applications stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the IoT-based fire early warning method for critical substation operating equipment described in any of the above embodiments.

[0182] The IoT-based fire alarm system 300 for critical substation equipment may further include a power supply component 303 configured to perform power management for the IoT-based fire alarm system 300, a wired or wireless network interface 304 configured to connect the IoT-based fire alarm system 300 to a network, and an input / output (I / O) interface 305. The IoT-based fire alarm system 300 can operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0183] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the IoT-based fire early warning system for critical operating equipment in substations to which the present application is applied. A specific IoT-based fire early warning system for critical operating equipment in substations may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0184] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0185] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0186] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fire early warning method for key operating equipment in a substation based on the Internet of Things, characterized in that, The method includes: During the transmission of partial discharge signals from a substation transformer, electromagnetic interference is assessed on the partial discharge signals. If the electromagnetic interference assessment result is deemed satisfactory, the accuracy of the partial discharge signal acquisition will be analyzed; otherwise, a fire alarm will be triggered. If the accuracy analysis results of the acquisition are deemed satisfactory, the transmission effectiveness of the partial discharge signal will be evaluated; otherwise, the acquisition optimization measures for the partial discharge signal will be implemented. If the transmission effectiveness assessment result is deemed satisfactory, the transmission qualification of the partial discharge signal is verified; otherwise, optimization measures for the transmission process of the partial discharge signal are implemented. When the transmission qualification verification result is determined to be unqualified, a fire warning deviation assessment is performed on the partial discharge signal, and corresponding fire protection measures are initiated.

2. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 1, characterized in that, The steps for determining whether the electromagnetic interference assessment result is qualified include: The deviation between the narrowband peak value and the preset narrowband peak value of the partial discharge signal is quantized. When the deviation quantification result is less than the electromagnetic interference safety threshold, the electromagnetic interference assessment result is qualified.

3. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 1, characterized in that, The steps for determining whether the accuracy analysis results of the data collection are acceptable include: The partial discharge signal parameters and standard partial discharge signal parameters of the partial discharge signal are proportionally quantized to obtain the partial discharge parameter offset. The partial discharge parameter offset and the weighted data of partial discharge characteristics are weighted and fused to obtain the partial discharge signal deviation index. When the partial discharge signal deviation index is less than the partial discharge signal accuracy threshold, the acquisition accuracy analysis result is qualified.

4. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 3, characterized in that, The steps for implementing the partial discharge signal acquisition optimization measures include: Based on the current voltage of the transformer and the partial discharge signal deviation index, the signal change rate gain adjustment ratio is obtained; If the signal change rate gain adjustment ratio exceeds the preset maximum gain threshold, an abnormal filter parameter alarm will be sent; otherwise, the signal change rate gain adjustment ratio will be used as a qualified gain adjustment value. After gradually increasing the signal change rate gain of the partial discharge signal by the amplitude corresponding to the qualified gain adjustment value, if the deviation index of the partial discharge signal is not greater than the accuracy threshold of the partial discharge signal, then the transmission effectiveness of the partial discharge signal is evaluated. Otherwise, the partial discharge signal sampling rate adjustment ratio is obtained based on the highest frequency of the partial discharge signal and the partial discharge signal deviation index; If the partial discharge signal sampling rate adjustment ratio exceeds the preset gain threshold range, a sampling rate adjustment abnormality alarm is sent; otherwise, the partial discharge signal sampling rate adjustment ratio is taken as the qualified partial discharge signal sampling rate adjustment value. By gradually increasing the sampling rate of the partial discharge signal according to the amplitude corresponding to the qualified partial discharge signal sampling rate adjustment value, if the partial discharge signal deviation index is greater than the partial discharge signal accuracy threshold, a sampling rate adjustment failure alarm is triggered; otherwise, the transmission effectiveness of the partial discharge signal is evaluated.

5. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 1, characterized in that, The step of determining whether the transmission validity assessment result is qualified includes: Determine the evaluation index for the transmission effectiveness deviation of the partial discharge signal; When the transmission effectiveness deviation evaluation index is within the transmission effectiveness safety threshold range, the transmission effectiveness evaluation result is qualified.

6. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 5, characterized in that, The steps for implementing the partial discharge signal transmission process optimization measures include: If the transmission effectiveness deviation evaluation index is greater than the preset transmission effectiveness evaluation threshold, the partial discharge signal will be used as first-level transmission data; otherwise, the partial discharge signal will be used as second-level transmission data. After performing first-level data transmission on the first-level transmission data and second-level data transmission on the second-level transmission data, if the transmission effectiveness deviation evaluation index is not within the transmission effectiveness safety threshold range, a local transmission optimization failure alarm is sent; otherwise, a local transmission qualification verification is performed on the partial discharge signal.

7. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 1, characterized in that, The step of determining that the transmission qualification verification result is unqualified includes: Obtain the fire response deviation index of the fire-fighting equipment in the substation; If the fire response deviation index is not within the preset transmission delay safety threshold range, the transmission qualification verification result is unqualified.

8. The fire early warning method for key operating equipment in substations based on the Internet of Things according to claim 7, characterized in that, The steps of assessing the fire warning deviation of the partial discharge signal and activating the corresponding fire protection measures include: If the fire response deviation index is greater than the preset transmission delay safety threshold, then the first-level fire protection measures are activated; If the fire response deviation index is not greater than the preset transmission delay safety threshold, then level two fire protection measures will be activated.

9. A fire early warning device for key operating equipment in a substation based on the Internet of Things, characterized in that, The device includes: An electromagnetic interference assessment module is used to assess the electromagnetic interference of a partial discharge signal during its transmission in a substation transformer. The accuracy analysis module is used to perform accuracy analysis on the partial discharge signal if the electromagnetic interference assessment result is deemed acceptable; otherwise, it triggers a fire alarm. The transmission effectiveness assessment module is used to assess the transmission effectiveness of the partial discharge signal if the accuracy analysis result of the acquisition is qualified; otherwise, it executes acquisition optimization measures for the partial discharge signal. The transmission qualification verification module is used to verify the transmission qualification of the partial discharge signal if the transmission effectiveness assessment result is qualified; otherwise, it performs optimization measures for the transmission process of the partial discharge signal. The fire warning deviation assessment module is used to assess the fire warning deviation of the partial discharge signal and activate the corresponding fire protection measures when the transmission qualification verification result is determined to be unqualified.

10. A fire early warning system for key operating equipment in a substation based on the Internet of Things, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the Internet of Things-based fire early warning method for critical operating equipment in substations as described in any one of claims 1 to 8.

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