Moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and ring main unit

Abstract

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and in particular to a moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication and a ring main unit. Background Art

[0002] The field of power monitoring technology involves real-time monitoring of power grid operating status, data acquisition and transmission, and anomaly warning technology systems. Its core lies in establishing a closed-loop feedback mechanism through distributed sensing devices and central processing units to achieve continuous tracking and analysis of the operating parameters of distribution network equipment. This field includes three major infrastructures: data acquisition terminals, communication transmission networks, and central control platforms. Data acquisition terminals are deployed at key nodes within ring main units, communication transmission networks are networked using optical fiber or power carriers, and central control platforms integrate data analysis and status assessment algorithms. Among them, the traditional ring main unit online monitoring system refers to a detection unit composed of temperature sensors, humidity sensors, and current transformers. It uses RS485 buses or ZigBee wireless modules to achieve data feedback, relies on the Modbus communication protocol to establish a connection with the host computer, and uses the threshold comparison method to determine insulation performance degradation. Its technical implementation relies on the basic hardware framework composed of analog signal acquisition circuits, AD conversion chips, and relay output units.

[0003] Traditional monitoring systems rely on fixed threshold comparison methods to judge insulation performance, which makes it difficult to adapt to dynamically changing grid loads and environmental conditions. They use a single sensor data independent analysis mode and lack a collaborative verification mechanism for multi-dimensional parameters. The hardware architecture based on analog signal acquisition and AD conversion has data analysis delays. The communication method of the RS485 bus and ZigBee wireless module is susceptible to interference in complex electromagnetic environments. The open-loop control mode used by the relay output unit causes lag in equipment adjustment. The static strategy weight cannot be dynamically adjusted according to real-time operating conditions. The independent operation mode of temperature and humidity sensors and current transformers weakens the correlation analysis ability between environmental factors and electrical parameters. The one-way transmission mechanism between the data acquisition terminal and the central platform lacks a feedback optimization path, resulting in limited overall system response efficiency and fault identification accuracy. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a moisture-proof and environmentally friendly ring main unit online monitoring system and a ring main unit with continuous fault indication.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: an online monitoring system for moisture-proof and environmentally friendly ring network cabinets with continuous fault indication includes: The electrical parameter dynamic analysis module is used to obtain operating current waveform data and bus power time series data through the embedded monitoring unit. The current fluctuation trend parameters and power response difference parameters are input into the dynamic time warping algorithm with set dynamic time warping window constraints for behavior matching. The module then generates primary fault characteristics and transmits them to the environmental collaborative verification module. An environmental collaborative verification module is used to call the temperature rise rate data and the condensation critical index data through the primary fault characteristics, generate a linkage response instruction through linear regression calculation and fuzzy logic judgment model processing, and transmit it to the closed-loop control execution module; Closed-loop control execution module, used to activate the exhaust device and dehumidification equipment parameters according to the linkage response instruction, using the set proportional gain , integration time , differential time The PID control model adjusts the speed of the device, collects feedback signals to generate equipment feedback signals and transmits them to the behavior path optimization module; The behavior path optimization module is used to extract the current stability parameters and power attenuation coefficient through the feedback signal of the device, adjust the strategy weight through the genetic algorithm of difference calculation and setting fitness function, generate parameter correction coefficients and transmit them back to the electric parameter dynamic analysis module.

[0006] As a further solution of the present invention, the primary fault characteristics are specifically waveform distortion rate, phase offset, and harmonic distribution spectrum; the linkage response instructions include temperature gradient threshold, dew point deviation coefficient, and insulation medium loss; the equipment feedback signal specifically refers to speed fluctuation amplitude, humidity convergence rate, and airflow balance index; the parameter correction coefficient includes current harmonic suppression rate, power recovery slope, and impedance matching.

[0007] As a further solution of the present invention, the fitness function is ;in, represents the quantized coefficient of the proportional gain, represents the integration time constant, represents the differential time constant, represents the optimization weight coefficient of the i-th current stability parameter, represents the ideal threshold value of the i-th current stability parameter; The dynamic time warping window constraint is set to a maximum bending coefficient not exceeding 0.2.

[0008] As a further solution of the present invention, the electrical parameter dynamic analysis module includes: The operation data acquisition submodule collects the output signals of the three-phase current sensor and the pulse signal of the bus power transmitter through the embedded monitoring unit, uses an anti-aliasing filter to eliminate high-frequency noise interference, samples the original signal at 10kHz intervals, and completes multi-channel time stamp synchronization calibration to generate waveform sampling sequences and power timing sequences; The dynamic parameter calculation submodule calculates the second-order difference value of the current amplitude between adjacent sampling points based on the waveform sampling sequence, establishes a sliding standard deviation curve as a current fluctuation trend parameter, calculates the active power change gradient based on the power time series using an overlapping window with a window length of 1 second, and generates a dynamic trend coefficient and a response difference; The behavior matching analysis submodule performs Z-score normalization on the dynamic trend coefficient along the time axis, performs range normalization conversion on the response difference, uses a dynamic time warping algorithm to calculate the cumulative distance matrix between the standardized sequence and multiple templates in the standard pattern library, obtains the minimum bending cost value by backtracking the optimal path, and generates a primary fault feature set.

[0009] As a further embodiment of the present invention, the Z-score normalization process adopts the formula ; in, represents the original dynamic trend coefficient data point, Indicates the arithmetic mean of the dynamic trend coefficient within the current sampling window. Indicates the standard deviation of the dynamic trend coefficient within the current sampling window.

[0010] As a further solution of the present invention, the environment collaborative verification module includes: The data collaboration submodule calls the temperature rise rate data and condensation critical index data in the primary fault characteristics, aligns the time series of the two sets of data through timestamp matching, calculates the covariance matrix based on the data change per minute, and constructs the correlation relationship between the data through a dynamic weight allocation algorithm to generate a collaborative feature matrix; The model fusion submodule is based on the collaborative feature matrix and adopts the formula: ; The environmental adaptability evaluation value is obtained by calculation, and fuzzy membership matching is performed with the preset condensation formation threshold to generate a fusion evaluation coefficient; in, represents the environmental adaptability assessment value, represents the temperature rise rate weight factor and its value range is [0.5,1.2], Indicates the temperature change inside the cabinet per minute, unit: ℃ / min, It represents the critical compensation coefficient of condensation and is positively correlated with the thermal conductivity of the cabinet material. Indicates the absolute value of the change in ambient humidity, unit: %RH, Represents the environmental compensation factor and its value is ,in is the temperature sensor measurement error, unit: °C, is the humidity sensor measurement error, unit: %RH, Indicates the maximum temperature value inside the cabinet during the monitoring period. Indicates the lowest temperature inside the cabinet during the monitoring period, unit: °C; The instruction generation submodule uses a sliding window algorithm to extract the coefficient change trend characteristics based on the fusion evaluation coefficient, and establishes a mapping relationship with the equipment protection level through a preset threshold gradient table. When the coefficient exceeds the dynamic adjustment threshold within three consecutive monitoring cycles, the multi-level response strategy generator is activated and a linkage response instruction is output.

[0011] As a further solution of the present invention, the closed-loop control execution module includes: The instruction parsing submodule parses the device identification code and parameter setting byte in the linkage response instruction, matches the exhaust device RS485 communication address with the dehumidification device Modbus register number, and generates a device control instruction set; The PID adjustment submodule collects the number of pulses fed back by the motor encoder according to the target speed value in the control instruction set of the device and calculates the real-time speed deviation. It uses a comprehensive calculation method of proportional error amplification, integral accumulated error compensation, and differential change rate prediction to output the duty cycle adjustment of the inverter pulse width modulation wave after 24-bit AD conversion implemented by the ADS1256 chip to generate the speed adjustment value. The feedback acquisition submodule obtains the exhaust device rotor speed pulse signal through the Hall effect sensor, uses the current transformer to collect the effective value of the dehumidification equipment working current, performs 24-bit AD conversion and data frame packaging on the equipment operating parameters after the speed adjustment is executed, and generates the equipment feedback signal; The resolution of the 24-bit AD conversion is set to 0.1 rpm; The data frame encapsulation format complies with the IEC60870-5-104 protocol standard.

[0012] As a further solution of the present invention, the behavior path optimization module includes: The signal feature extraction submodule collects the current waveform sampling values ​​and power output timing data in the feedback signal of the device, sets the window width to an integer multiple of the sampling period for sliding interception, calculates the current range to mean ratio in each window as a stability indicator, and calculates the mean attenuation amplitude of adjacent peaks of the power curve as an attenuation indicator to generate a current stability parameter set and a power attenuation coefficient set; The strategy weight adjustment submodule constructs a difference absolute value matrix corresponding to multiple elements of the current stability parameter set and the power attenuation coefficient set, initializes a weight vector and sets a constraint on the number of iterations, uses the minimum value of the weighted sum of the matrix row vectors as a screening condition when calculating the individual fitness in each genetic algorithm iteration, updates the population parameters through a crossover mutation operation, and generates an optimized weight vector; The parameter reverse transfer submodule performs a point multiplication operation on the optimized weight vector and the current stability parameter set, and superimposes the power attenuation coefficient set to construct a three-dimensional correction value calculation space with the X-axis as the current harmonic amount, the Y-axis as the power attenuation rate, and the Z-axis as the temperature rise gradient. The back propagation mechanism is used to map the spatial coordinates to the input layer nodes of the electrical parameter analysis module to generate the parameter correction coefficient.

[0013] As a further solution of the present invention, the current harmonic amount is defined as the total distortion rate of the 3rd to 21st harmonic components; The power attenuation rate represents the percentage of decrease in active power per unit time; The temperature rise gradient represents the absolute value of the rate of change of the temperature inside the cabinet per minute.

[0014] The moisture-proof and insulated ring main unit with fault display is used to monitor the ring main unit online. The moisture-proof and insulated ring main unit with fault display is used to monitor the ring main unit online based on the moisture-proof and environmentally friendly ring main unit with continuous fault indication. The system includes: Double-layer insulated cabinet, with the inner wall coated with nano-scale hydrophobic coating and the interlayer filled with aerogel insulation material; Embedded monitoring unit, fixed at the busbar connection and integrating Hall current sensor and voltage differential probe; The temperature and humidity sensor array is evenly spaced and distributed at six spatial quadrant monitoring points inside the cabinet; A fault indicator is installed at the observation window of the busbar room and is connected to the closed-loop control execution module via the RS485 bus. The fault indicator is powered directly by the ring main cabinet or is equipped with a rechargeable and dischargeable lithium battery with a capacity of ≥2000mAh; An axial flow exhaust device is installed on the top of the cabinet and is connected to the closed-loop control execution module via a CAN bus that complies with the ISO11898-2 standard; The rotary dehumidification equipment is installed at the air inlet at the bottom of the cabinet and the impeller speed is linearly related to the output value of the PID control model; The contact angle of the nanoscale hydrophobic coating is ≥150°; The thermal conductivity of the aerogel thermal insulation material is ≤0.02W / (m·K); The measurement accuracy of the Hall current sensor is ±0.5% FS; The common mode rejection ratio of the voltage differential probe is ≥120dB.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, a dynamic time warping algorithm is used to match the behavior of the current fluctuation trend and the power response difference. The linear regression calculation of the temperature rise rate and the condensation critical index is combined with fuzzy logic judgment to form a multi-dimensional data collaborative verification mechanism. The PID control model is used to adjust the equipment operating parameters in real time. The genetic algorithm is used to dynamically optimize the strategy weights and reversely correct the feature extraction process to achieve simultaneous improvement in the fault feature recognition accuracy and equipment response speed. A closed-loop feedback mechanism is established to continuously optimize the system operation status, enhance the coupling analysis capability between environmental factors and electrical parameters, effectively reduce the probability of misjudgment caused by single threshold judgment, improve the real-time and accuracy of equipment adjustment through dynamic parameter correction, strengthen the system's adaptive adjustment capability under complex working conditions, form a complete optimization closed loop of data acquisition, feature analysis, and execution feedback, and significantly improve the reliability of insulation performance degradation judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the overall structure of the moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication of the present invention; Figure 2 This is a flow chart of the internal execution of the electrical parameter dynamic analysis module of the present invention; Figure 3 This is a flow chart of the internal execution of the environment collaborative verification module of the present invention; Figure 4 This is a flow chart of the internal execution of the closed-loop control execution module of the present invention; Figure 5 This is a flow chart of the internal execution of the behavior path optimization module of the present invention. DETAILED DESCRIPTION

[0017] To make the purpose, technical solutions and advantages of the present invention clearer, the following is a detailed description of the technical solutions based on software implementation in conjunction with the system architecture diagram and embodiments. It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present invention and do not constitute a limitation on the scope of protection.

[0018] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are defined based on the architecture diagrams or flow charts corresponding to the embodiments. This expression is intended solely to clarify the logical relationships between the various elements of the technical solution and does not limit the physical deployment form. The term "plurality" encompasses two or more technical units, including but not limited to scalable elements such as multiple data nodes, processing threads, service instances, or functional components. The specific number will be determined based on the actual business scenario and requires special explanation.

[0019] See also Figure 1 and Figure 2 The present invention provides a technical solution: an online monitoring system for moisture-proof and environmentally friendly ring network cabinets with continuous fault indication includes: The electrical parameter dynamic analysis module is used to obtain operating current waveform data and bus power time series data through the embedded monitoring unit. The current fluctuation trend parameters and power response difference parameters are input into the dynamic time warping algorithm with set dynamic time warping window constraints for behavior matching. The module then generates primary fault characteristics and transmits them to the environmental collaborative verification module. The primary fault characteristics are specifically waveform distortion rate, phase offset, and harmonic distribution spectrum; The electrical parameter dynamic analysis module includes: The operation data acquisition submodule collects the output signals of the three-phase current sensor and the pulse signal of the bus power transmitter through the embedded monitoring unit, uses an anti-aliasing filter to eliminate high-frequency noise interference, samples the original signal at 10kHz intervals, and completes multi-channel time stamp synchronization calibration to generate waveform sampling sequences and power timing sequences; The operating data acquisition submodule utilizes an embedded monitoring unit (EMU) deployed on a moisture-proof, insulated ring main unit (RMC) within a 10kV power distribution room. This unit integrates three-phase current sensors and a busbar power transmitter. The three-phase current sensors utilize open-type Hall effect current sensors with a range of 0-200A and a Class 0.5 accuracy. The busbar power transmitter utilizes the ATT7022EU energy metering chip, which outputs a pulse signal proportional to active power. During a specific monitoring cycle, the current sensor for phase A detected an actual operating current of approximately 105A, phase B approximately 102A, and phase C approximately 104A. To eliminate high-frequency noise introduced by the on-site electromagnetic environment, the signals are hardware-filtered using a second-order Butterworth low-pass filter with a cutoff frequency of 4.5kHz before entering the embedded monitoring unit. This filter effectively suppresses aliasing effects that might be introduced by the 10kHz sampling frequency. The ADC (analog-to-digital converter) within the monitoring unit then synchronously samples the three-phase current analog signals and the pulse signals from the power transmitter at a fixed frequency of 10kHz. For example, at time t = 0.0001s, the instantaneous value of the collected phase A current is 105.2A, and the power pulse count is 5. To ensure absolute temporal alignment of multi-channel data, the system uses NTP (Network Time Protocol) to periodically synchronize the monitoring unit and assign a high-precision timestamp to each sampling point, with a timestamp synchronization accuracy of 1 microsecond. As a result, within a complete sampling cycle (e.g., 1 second), three waveform sampling sequences, each containing 10,000 data points, are generated, as well as a power timing sequence that records the total pulse counts output by the power transmitter during that cycle and the corresponding time points.

[0020] The dynamic parameter calculation submodule calculates the second-order difference of the current amplitude between adjacent sampling points based on the waveform sampling sequence, establishes a sliding standard deviation curve as the current fluctuation trend parameter, calculates the active power change gradient based on the power time series using an overlapping window with a window length of 1 second, and generates a dynamic trend coefficient and response difference; After receiving the waveform sampling sequence and power timing sequence, the dynamic parameter calculation submodule first processes the waveform sampling sequence. Taking the A phase current as an example, assuming that the current amplitudes obtained at three consecutive sampling points (t1, t2, t3) are , , . Calculate its second-order difference value, that is . The second-order difference calculation is performed on the 10,000 sampling point sequence within the entire 1 second to obtain a second-order difference sequence. Next, a sliding window with a width of 50 sampling points (i.e. 5 milliseconds) is set to calculate the standard deviation of all second-order difference values ​​in the window. For example, the first window covers the 1st to the 50th second-order difference values, and its standard deviation is calculated as ; Then the window slides back one sampling point, covering the 2nd to 51st values, and the calculation is This is repeated until the end of the sequence, forming a sliding standard deviation curve composed of multiple standard deviation values. This curve is the current fluctuation trend parameter, and its numerical change directly reflects the severity of the current fluctuation. For the power time series, an overlapping window with a window length of 1 second and a step length of 0.5 seconds (i.e., 50% overlap) is used to calculate the active power change gradient. Assume that in the nth 1-second window, the active power value at the starting moment is , the end time is , then the active power change gradient of the window is The gradient value is used as the dynamic trend coefficient. At the same time, the difference between the maximum and minimum power values ​​in the window is calculated, for example , , the difference as the response difference amount.

[0021] The behavior matching analysis submodule performs Z-score normalization on the dynamic trend coefficient along the time axis, performs range normalization conversion on the response difference, and uses a dynamic time warping algorithm to calculate the cumulative distance matrix between the standardized sequence and multiple templates in the standard pattern library. By backtracking the optimal path, the minimum bending cost value is obtained to generate a primary fault feature set. The dynamic time warping window constraint is set to a maximum bending coefficient not exceeding 0.2; Z-score standardization is performed using the formula ; in, represents the original dynamic trend coefficient data point, Indicates the arithmetic mean of the dynamic trend coefficient within the current sampling window. Indicates the standard deviation of the dynamic trend coefficient within the current sampling window.

[0022] The behavior matching analysis submodule obtains continuous dynamic trend coefficients (power change gradients) and response differences. Assume that within 10 consecutive calculation windows, the dynamic trend coefficient sequence is: [2.0, 2.1, 1.9, 2.5, 2.3, 2.2, 1.8, 1.7, 1.9, 2.0] kW / s. First, calculate the arithmetic mean of this sequence. kW / s, standard deviation kW / s. Then, the Z-score normalization formula is used Process each data point. For example, process the fourth data point 2.5: After processing, a standardized dynamic trend coefficient sequence is obtained. At the same time, for the response difference sequence, such as [3.0, 3.2, 2.9, 4.0, 3.5, 3.3, 2.8, 2.7, 2.9, 3.1] kW, its maximum value 4.0 and minimum value 2.7 are found, and the range normalization transformation is performed. For example, the fourth data point 4.0 is transformed: . The two sets of processed sequences are used as input and matched with the templates in the standard pattern library using the dynamic time warping (DTW) algorithm. The standard pattern library pre-stores standardized sequence templates under a variety of typical working conditions (such as normal operation, slight overload, and precursors to ground faults). The DTW algorithm constructs a cumulative distance matrix, where the matrix element (i, j) represents the Euclidean distance between the i-th point of the input sequence and the j-th point of the template sequence. During the calculation process, the maximum bending coefficient (i.e., the width constraint of the Sakoe-ChibaBand) is set to no more than 0.2, which means that when looking for the optimal path, the search range of the path point (i, j) is limited to This prevents excessive time alignment distortion within the banded region. This coefficient is based on the following experimental verification: 50 sets of sample data known to be in normal operation and 50 sets of sample data known to be in early insulation degradation were selected, and the DTW algorithm was run with the maximum bending coefficient set to 0.1, 0.15, 0.2, 0.25, and 0.3, respectively.

[0023] Table 1 DTW maximum bending coefficient selection experimental data table As shown in Table 1, the discrimination index is defined as the difference between the average distance between faulty samples and the average distance between normal samples. When the coefficient increases from 0.15 to 0.20, the discrimination index jumps significantly from 27.6 to 41.7. However, when it increases from 0.20 to 0.30, the discrimination index improves slowly, but the computational complexity increases significantly. Considering both recognition effectiveness and computational efficiency, a maximum bending coefficient of 0.2 is selected. A backtracking algorithm is used to find a path from (1,1) to (N,M) in the constrained cumulative distance matrix that minimizes the sum of the matrix elements it passes through (i.e., the cumulative distance). This minimum cumulative distance is the minimum bending cost. If this value falls below the preset fault determination threshold (for example, a matching distance with the ground fault precursor template of less than 20), the fault is determined to be a primary fault. At this point, the system immediately performs a fast Fourier transform (FFT) on the current 1-second waveform sampling sequence, calculates its waveform distortion rate (THD), extracts the amplitude and phase of each harmonic (especially the 3rd, 5th, and 7th harmonics) to form a harmonic distribution spectrum, and calculates the phase offset between the three-phase currents, which together constitute the primary fault feature set.

[0024] See also Figure 1 and Figure 3 ,Environmental collaborative verification module, which is used to call the temperature rise rate data and condensation critical index data through primary fault characteristics, and generate linkage response instructions through linear regression calculation and fuzzy logic judgment model processing and pass them to the closed-loop control execution module; The linkage response instructions include temperature gradient threshold, dew point deviation coefficient, and insulation medium loss; The environmental collaborative verification module includes: The data collaboration submodule uses the temperature rise rate data and condensation critical index data from the primary fault characteristics, aligns the time series of the two sets of data through timestamp matching, calculates the covariance matrix based on the minute-by-minute data changes, and constructs the correlation between the data using a dynamic weight allocation algorithm to generate a collaborative feature matrix. The data coordination submodule receives the primary fault signature transmitted by the electrical parameter dynamic analysis module. Assume that these signatures are: waveform distortion rate (THD) = 8.5%, phase offset = 2.1°, and a prominent fifth harmonic component in the harmonic distribution spectrum. This signature triggers the environmental collaborative verification process. The system immediately calls upon two sets of environmental data stored in the local database, closely associated with the current timestamp: temperature rise rate data collected by multiple PT100 temperature sensors within the ring main unit; and condensation critical index data derived from a high-precision temperature and humidity sensor combined with a dew point calculation model. For example, at the timestamp 2025-06-2010:30:15, the module retrieves the temperature rise rate sequence for the past five minutes: [0.2, 0.3, 0.4, 0.5, 0.6]°C / min, and the corresponding condensation critical index sequence: [0.75, 0.78, 0.81, 0.85, 0.88]. (The index is a dimensionless value between 0 and 1, with values ​​closer to 1 indicating closer to the dew point.) By matching the timestamps, we ensure that the two sets of data are precisely aligned on the time axis. Then, we calculate the covariance matrix of the two sets of sequences based on the data changes per minute. Assume that the temperature rise rate change sequence is , the variation sequence of condensation critical index is , then calculate For example, the calculated covariance value is 0.018, indicating that there is a positive correlation between temperature rise and condensation risk. Then, the system uses a dynamic weight allocation algorithm to build the correlation between the data. The algorithm assigns weights to the temperature rise rate and the condensation critical index based on historical data and current working conditions. In the context of the current primary fault characteristics (large harmonics), the increased internal heating is the main contradiction, so the weight of the temperature rise rate is is set to 0.65, and the weight of the condensation critical index is set to 0.35. Finally, the two sets of weighted time series data are integrated into a collaborative feature matrix for the next stage of model fusion analysis.

[0025] The model fusion submodule is based on the collaborative feature matrix and uses the formula: ; The environmental adaptability evaluation value is obtained by calculation, and fuzzy membership matching is performed with the preset condensation formation threshold to generate a fusion evaluation coefficient; in, represents the environmental adaptability assessment value, represents the temperature rise rate weight factor and its value range is [0.5,1.2], Indicates the temperature change inside the cabinet per minute, unit: ℃ / min, It represents the critical compensation coefficient of condensation and is positively correlated with the thermal conductivity of the cabinet material. Indicates the absolute value of the change in ambient humidity, unit: %RH, Represents the environmental compensation factor and its value is ,in is the temperature sensor measurement error, unit: °C, is the humidity sensor measurement error, unit: %RH, Indicates the maximum temperature value inside the cabinet during the monitoring period. Indicates the lowest temperature inside the cabinet during the monitoring period, unit: °C; The model fusion submodule performs environmental fitness evaluation based on the received collaborative feature matrix This operation strictly follows the formula: .

[0026] The meaning and value determination process of each parameter in the formula are as follows: It is the environmental adaptability assessment value, which is a dimensionless indicator that comprehensively measures the risks of internal heating and external condensation. is the temperature rise rate weighting factor, and its value range is [0.5, 1.2]. This range is obtained through a large number of experiments on the temperature rise characteristics of different insulation materials and cabinet structures under different loads. Since the detected harmonics are large, indicating a potential overheating risk, a higher value is selected. . is the temperature change inside the cabinet per minute. According to the sensor data, the temperature in the current minute rises from 35.1°C to 35.7°C. ℃ / min. is the critical compensation coefficient for condensation, which is positively correlated with the thermal conductivity of the cabinet material. This ring main unit is made of stainless steel (SUS304), which has a thermal conductivity of approximately 16.3W / (m·K). With reference to the standard material (such as ordinary carbon steel, which has a thermal conductivity of approximately 50W / (m·K)), the base compensation coefficient is 1.0. Set to . is the absolute value of the change in ambient humidity. Through the humidity sensor, the relative humidity in the current minute changes from 78%RH to 80%RH. %RH. is the environmental compensation factor, which is calculated as follows: .in, is the measurement error of the temperature sensor. ℃; is the measurement error of the humidity sensor. %RH. Therefore, . and The highest and lowest temperatures inside the cabinet during the current monitoring period (e.g. the past hour) are recorded as ℃, ℃. Substitute all the above parameter values ​​into the formula for calculation: .

[0027] The benefit of the formula is that by comparing the difference between the temperature rise effect (the first term in the numerator) and the humidity change effect (the second term in the numerator), and normalizing it with the temperature extremes and sensor accuracy during the monitoring period (the denominator), it can dynamically and sensitively assess the degree of internal environmental deterioration caused by electrical faults. Compared with the judgment of a single temperature or humidity threshold, it has higher reliability and early warning capabilities. The calculated environmental adaptability assessment value A fuzzy membership match is performed with the preset condensation formation threshold. This threshold is not a single value but a graded range: [0, 0.02] indicates safety, [0.02, 0.05] indicates concern, [0.05, 0.1] indicates warning, and values ​​greater than 0.1 indicate danger. The current value of 0.039 falls within the "concern" range. The membership function calculates the fusion evaluation coefficient to be 0.45 (between 0 and 1).

[0028] The instruction generation submodule uses a sliding window algorithm to extract the coefficient change trend characteristics based on the fusion evaluation coefficient, and establishes a mapping relationship with the equipment protection level through a preset threshold gradient table. When the coefficient exceeds the dynamic adjustment threshold within three consecutive monitoring cycles, the multi-level response strategy generator is activated and a linkage response instruction is output.

[0029] Based on the obtained fusion evaluation coefficient of 0.45, the instruction generation submodule uses a sliding window algorithm with a length of 3 to extract the coefficient's changing trend. Assume that the fusion evaluation coefficients for the previous two monitoring cycles were 0.38 and 0.41, respectively, and the current value is 0.45, with the sequence [0.38, 0.41, 0.45] showing a clear upward trend. The system has a built-in threshold gradient table that maps fusion evaluation coefficients to device protection levels. For example, a coefficient between 0.3 and 0.5 corresponds to a "Level 2 response," while a coefficient between 0.5 and 0.7 corresponds to a "Level 1 response." The current coefficient of 0.45 and its upward trend meet the requirement of exceeding the dynamically adjusted threshold for three consecutive monitoring cycles. This dynamically adjusted threshold is automatically adjusted based on historical environmental data and device health status, and its current value is 0.40. Since these three consecutive values ​​have exceeded 0.40, the system activates the multi-level response strategy generator. Based on the preset Level 2 response strategy, the generator outputs specific linkage response instructions. This instruction includes temporarily lowering the temperature gradient threshold within the cabinet to 1.5°C / min (previously 2.0°C / min), increasing the safety margin for the dew point deviation coefficient to 1.2 (previously 1.0), and estimating a 0.1% increment in the dielectric loss tangent based on the harmonics in the primary fault signature. These parameters together constitute a coordinated response instruction, which is transmitted to the closed-loop control execution module.

[0030] See also Figure 1 and Figure 4 , closed-loop control execution module, used to activate the exhaust device and dehumidification equipment parameters according to the linkage response instruction, using the set proportional gain , integration time , differential time The PID control model adjusts the speed of the device, collects feedback signals to generate equipment feedback signals and transmits them to the behavior path optimization module; in, represents the quantized coefficient of the proportional gain, represents the integration time constant, represents the differential time constant; The equipment feedback signal specifically refers to the speed fluctuation amplitude, humidity convergence rate, and airflow balance index; The closed-loop control execution module includes: The instruction parsing submodule parses the device identification code and parameter setting bytes in the linkage response instruction, matches the exhaust device RS485 communication address with the dehumidification device Modbus register number, and generates a device control instruction set; The command parsing submodule receives a linkage response command containing the temperature gradient threshold, dew point deviation coefficient, and dielectric loss. This command is a binary data stream. The submodule first parses it according to a predefined protocol format, extracting the device identification code and parameter setting bytes. For example, the first eight bits of the command, "0x11," are parsed as the device identification code for exhaust unit A, whose RS485 communication address is 0x0A. The following eight bits, "0x21," are parsed as the device identification code for dehumidifier B, whose Modbus register address is 40001. The subsequent byte sequence, "0x05DC0x4140," corresponds to the specific parameter settings. Using its internal device driver library, the submodule translates this parsed information into control commands recognized by the corresponding device. For example, for exhaust unit A, a control frame conforming to its proprietary RS485 protocol is generated, with the command content "Start, target speed 1500 rpm." For dehumidifier B, a standard Modbus RTU write command is generated, writing the setting value to register 40001, such as starting dehumidification and setting the target humidity decrease rate. These instructions are integrated into a device control instruction set and are ready to be issued.

[0031] The PID adjustment submodule collects the motor encoder feedback pulse count based on the target speed value in the device control instruction set and calculates the real-time speed deviation. It uses a comprehensive calculation method that amplifies the proportional error, compensates for the integral cumulative error, and predicts the differential change rate. It outputs the inverter pulse width modulation wave duty cycle adjustment after 24-bit AD conversion implemented by the ADS1256 chip to generate the speed adjustment value. The PID regulator module is the control core, and its proportional gain , integration time , differential time The determination of this set of parameters has undergone a rigorous experimental verification process. On an experimental platform with the same size and air duct characteristics as the actual ring main unit, the critical proportionality method (Ziegler-Nichols method) was first used for tuning. The integral and differential effects were disconnected, and only proportional control was used, gradually increasing Until the system produces equal amplitude oscillation, record the gain at this time as the critical gain , the oscillation period is the critical period According to the Ziegler-Nichols tuning rule, the initial recommended values ​​of the PID parameters are calculated: , , Then, fine-tuning was performed on this basis, and the overshoot and adjustment time were observed through step response experiments. The response speed is slow, so the differential time is gradually increased. When the step input is 0.8s, the system shows excellent dynamic performance with fast response (adjustment time is 0.8s) and small overshoot (less than 5%).

[0032] Table 2 PID parameter step response experimental data table As shown in Table 2, after comprehensive consideration, the final The optimal parameter combination. When the device control instruction set is issued, the PID adjustment submodule uses the exhaust device's target speed of 1500 rpm as the set value (SP). The motor encoder collects the number of feedback pulses in real time. For example, if 20 pulses are collected within 10ms and the encoder is known to have 1000 lines, the real-time speed is (20 / (1000*4))*60*100=300 rpm, which is the process value (PV). Current speed deviation The PID controller immediately performs the calculation: the proportional term output is ; The cumulative historical error of the integral term. Assuming that the integral at the previous moment is 50, the current integral is ; The differential term predicts the change trend. Assuming that the deviation at the last moment is 1150rpm, the current differential term output is The total output after the three items are superimposed is After the ADS1256 chip implements 24-bit AD conversion, this output value is mapped to an adjustment to the duty cycle of the pulse width modulation (PWM) output of the inverter. For example, increasing the duty cycle from 20% to 65% drives the motor to accelerate, which is the speed adjustment.

[0033] The feedback acquisition submodule obtains the exhaust device rotor speed pulse signal through the Hall effect sensor, uses the current transformer to collect the effective value of the dehumidification equipment working current, performs 24-bit AD conversion and data frame packaging on the equipment operating parameters after the speed adjustment is executed, and generates the equipment feedback signal; The resolution of the 24-bit AD conversion is set to 0.1rpm; The data frame encapsulation format complies with the IEC60870-5-104 protocol standard.

[0034] After the control instructions are executed, the feedback acquisition submodule continuously monitors the actual operating status of the equipment. It acquires speed pulse signals from a Hall-effect sensor mounted on the exhaust unit's rotor shaft. Simultaneously, a 0-5A current transformer collects the dehumidifier's operating AC current and calculates its effective value. Assume that after PID control stabilizes, the exhaust unit's speed remains stable at 1498 rpm, with a small fluctuation amplitude of ±5 rpm; the dehumidifier's operating current remains stable at 1.5 A, and the humidity inside the cabinet converges and decreases at a rate of 2% RH per minute. The airflow balance index calculated from data from multiple wind speed sensor arrays is 0.92 (1 is ideal). These operating parameters are digitized using an independent 24-bit A / D converter, with a speed resolution of 0.1 rpm, ensuring high-precision feedback. Finally, all feedback data is encapsulated into a data frame compliant with the IEC60870-5-104 protocol. This frame contains standardized fields such as type identifier, variable structure qualifier, transmission reason, and application service data unit address. This device feedback signal is then uploaded to the behavioral path optimization module.

[0035] See also Figure 1 and Figure 5 , behavior path optimization module, is used to extract current stability parameters and power attenuation coefficients through equipment feedback signals, adjust strategy weights through difference calculation and genetic algorithm with set fitness function, generate parameter correction coefficients and transmit them back to the electrical parameter dynamic analysis module; Parameter correction coefficients include current harmonic suppression rate, power recovery slope, and impedance matching; The fitness function is ; in, represents the optimization weight coefficient of the i-th current stability parameter, represents the ideal threshold value of the i-th current stability parameter; The behavioral path optimization module includes: The signal feature extraction submodule collects the current waveform sampling values ​​and power output timing data from the device feedback signal, sets the window width to an integer multiple of the sampling period for sliding interception, calculates the ratio of the current range to the mean in each window as a stability indicator, and calculates the mean attenuation amplitude of adjacent peaks of the power curve as an attenuation indicator to generate a current stability parameter set and a power attenuation coefficient set. The signal feature extraction submodule receives the device feedback signal data frame transmitted by the closed-loop control execution module. The submodule first parses the data frame to extract the dehumidification device's operating current waveform sampling value and power output timing data. A sliding window is set with a width that is an integer multiple of the AC fundamental wave period. For example, in China's 50Hz power grid, the window width is set to 4 times 20 milliseconds, that is, 80 milliseconds. The current waveform data within the window is intercepted and the maximum current value within the 80 milliseconds is calculated. and minimum value , and the average For example, if a current data is captured, the calculated value is , , The ratio of the range to the mean is , as the stability indicator of the window. By continuously sliding the window, a current stability parameter set is generated. At the same time, the power output timing data is analyzed and the attenuation amplitude between two adjacent peak values ​​on the power curve is statistically analyzed. For example, it is observed that the first peak value of the power after turning on is 350W, and the second peak value is 345W, and the attenuation amplitude is The attenuation amplitudes of multiple consecutive peaks are counted and their arithmetic mean is calculated, which is used as the power attenuation coefficient. Ultimately, a current stability parameter set of [0.067, 0.065, 0.068] and a power attenuation coefficient set of [1.43%, 1.40%, 1.45%] are generated.

[0036] The strategy weight adjustment submodule constructs a matrix of absolute differences between multiple elements of the current stability parameter set and the power attenuation coefficient set, initializes the weight vector, and sets a constraint on the number of iterations. The minimum weighted sum of the matrix row vectors is used as a screening criterion when calculating the fitness of individuals in each genetic algorithm iteration. The population parameters are updated through crossover and mutation operations to generate an optimized weight vector. The goal of the strategy weight adjustment submodule is to optimize the fitness function using genetic algorithms The weight vector in Here, is the current stability parameter extracted in the previous step, and its ideal threshold Set to 0, indicating absolute stability. is the weight coefficient to be optimized. First, construct a difference absolute value matrix, whose rows correspond to different stability parameters and columns correspond to different power attenuation coefficients. Then, initialize a population of 50 individuals, each of which is a weight vector. For example, the weight vector of individual 1 is , the sum of the vector elements is 1. The number of iterations is set to 100. In each iteration, for each weight vector in the population, its individual fitness is calculated. The calculation method is to perform a weighted summation of the weight vector and the current stability parameter set. For example, for and the stable parameter set [0.067, 0.065, 0.068], the calculated value is This value is the fitness of the individual, with smaller values ​​indicating better performance. All individuals are ranked according to their fitness values, and roulette wheel selection is used to select individuals with higher fitness (e.g., the top 50%) to advance to the next generation. Crossover operations (e.g., single-point crossover) and mutation operations (e.g., randomly changing a weight value and renormalizing it) are performed on the selected parent individuals to generate a new offspring population. After repeating this process 100 times, the weight vector corresponding to the individual with the lowest fitness is selected as the optimized weight vector. For example, the final optimized weight vector is [0.6, 0.2, 0.2].

[0037] The parameter reverse transfer submodule performs a dot product operation on the optimized weight vector and the current stability parameter set, and superimposes the power attenuation coefficient set to construct a three-dimensional correction value calculation space with the X-axis as the current harmonic quantity, the Y-axis as the power attenuation rate, and the Z-axis as the temperature rise gradient. The backpropagation mechanism is used to map the spatial coordinates to the input layer nodes of the electrical parameter analysis module to generate the parameter correction coefficients. The current harmonic content is defined as the total distortion rate of the 3rd to 21st harmonic components; The power attenuation rate indicates the percentage of active power decrease per unit time; The temperature rise gradient indicates the absolute value of the rate of change of the internal temperature of the cabinet per minute.

[0038] The parameter reverse transfer submodule receives the optimized weight vector [0.6, 0.2, 0.2]. It performs a dot product operation on this vector and the latest current stability parameter set [0.067, 0.065, 0.068] to obtain a comprehensive evaluation value: This evaluation value is then superimposed with the power attenuation coefficient (for example, 1.43%) to construct a three-dimensional correction value calculation space. The X-axis of this space represents the current harmonics, defined as the total distortion (THD) of the 3rd to 21st odd harmonic components measured by the electrical parameter dynamic analysis module, for example, 8.5%; the Y-axis represents the power attenuation rate, which is the currently calculated power attenuation coefficient, 1.43%; and the Z-axis represents the temperature rise gradient, which is the absolute value of the rate of change of the cabinet temperature per minute, measured by the environmental collaborative verification module, for example, 0.6°C / min. The current system state point is the coordinates (8.5, 1.43, 0.6). The system uses a backpropagation mapping function, pre-trained through offline modeling or machine learning, to map these three-dimensional spatial coordinates back to the input layer nodes of the electrical parameter dynamic analysis module. This mapping relationship essentially answers the question, "How should the initial fault judgment criteria be adjusted given the current harmonics, power attenuation, and temperature rise conditions?" The mapping results in a set of parameter correction coefficients. For example, the target current harmonic suppression rate is increased by 5% (implying increased sensitivity to harmonics), the expected power recovery slope is adjusted to -0.5% / min (allowing for some power attenuation), and the impedance matching tolerance is relaxed to ±3%. These parameter correction coefficients are then passed back to the electrical parameter dynamic analysis module, which adjusts the thresholds and weights of its internal algorithm during the next monitoring cycle, thus forming a complete, self-optimizing closed-loop control and monitoring system.

[0039] The moisture-proof and insulated ring main unit online monitoring system with fault display is based on the above-mentioned moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication, and includes: Double-layer insulated cabinet, with the inner wall coated with nano-scale hydrophobic coating and the interlayer filled with aerogel insulation material; The contact angle of the nano-scale hydrophobic coating is ≥150°; The thermal conductivity of aerogel insulation material is ≤0.02W / (m·K); Embedded monitoring unit, fixed at the busbar connection and integrating Hall current sensor and voltage differential probe; The measurement accuracy of the Hall current sensor is ±0.5%FS; The common mode rejection ratio of the voltage differential probe is ≥120dB; The temperature and humidity sensor array is evenly spaced and distributed at six spatial quadrant monitoring points inside the cabinet; The fault indicator is installed at the observation window of the busbar room and connected to the closed-loop control execution module through the RS485 bus. The fault indicator adopts direct power supply from the ring main cabinet or is equipped with a rechargeable and dischargeable lithium battery with a capacity of ≥2000mAh; Axial flow exhaust device, installed on the top of the cabinet and connected to the closed-loop control execution module via a CAN bus that complies with the ISO11898-2 standard; The rotary dehumidification equipment is installed at the air inlet at the bottom of the cabinet and the impeller speed is linearly related to the output value of the PID control model.

[0040] The above examples demonstrate preferred implementations of the present invention. Any equivalent adjustments to the technical solution based on software engineering ring main units are within the scope of protection, including but not limited to: implementing algorithmic logic using different programming languages, service-oriented reconstruction of functional modules, adjusting data interaction protocols, optimizing resource scheduling strategies, and other technical improvements. Any implementation plan derived from reasonable modifications to the data processing flow, service call chain, or system architecture level that does not deviate from the core technology of the present invention shall be deemed to be within the scope of protection defined by the claims of the present invention.

Claims

1. Moisture-proof and environmentally friendly ring network cabinet online monitoring system with continuous fault indication, characterized by: The system comprises: The electrical parameter dynamic analysis module is used to obtain operating current waveform data and bus power time series data through the embedded monitoring unit. The current fluctuation trend parameters and power response difference parameters are input into the dynamic time warping algorithm with set dynamic time warping window constraints for behavior matching. The module then generates primary fault characteristics and transmits them to the environmental collaborative verification module. An environmental collaborative verification module is used to call the temperature rise rate data and the condensation critical index data through the primary fault characteristics, generate a linkage response instruction through linear regression calculation and fuzzy logic judgment model processing, and transmit it to the closed-loop control execution module; Closed-loop control execution module, used to activate the exhaust device and dehumidification equipment parameters according to the linkage response instruction, using the set proportional gain , integration time , differential time The PID control model adjusts the speed of the device, collects feedback signals to generate equipment feedback signals and transmits them to the behavior path optimization module; The behavior path optimization module is used to extract the current stability parameters and power attenuation coefficient through the feedback signal of the device, adjust the strategy weight through the genetic algorithm of difference calculation and setting fitness function, generate parameter correction coefficients and transmit them back to the electric parameter dynamic analysis module.

2. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 1 is characterized in that: The primary fault characteristics specifically include waveform distortion rate, phase offset, and harmonic distribution spectrum; the linkage response instructions include temperature gradient threshold, dew point deviation coefficient, and insulation medium loss; the equipment feedback signal specifically refers to speed fluctuation amplitude, humidity convergence rate, and airflow balance index; the parameter correction coefficients include current harmonic suppression rate, power recovery slope, and impedance matching.

3. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 2 is characterized in that: The fitness function is ;in, represents the quantized coefficient of the proportional gain, represents the integration time constant, represents the differential time constant, represents the optimization weight coefficient of the i-th current stability parameter, represents the ideal threshold value of the i-th current stability parameter; The dynamic time warping window constraint is set to a maximum bending coefficient not exceeding 0.

2.

4. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 3 is characterized in that: The electrical parameter dynamic analysis module includes: The operation data acquisition submodule collects the output signals of the three-phase current sensor and the pulse signal of the bus power transmitter through the embedded monitoring unit, uses an anti-aliasing filter to eliminate high-frequency noise interference, samples the original signal at 10kHz intervals, and completes multi-channel time stamp synchronization calibration to generate waveform sampling sequences and power timing sequences; The dynamic parameter calculation submodule calculates the second-order difference value of the current amplitude between adjacent sampling points based on the waveform sampling sequence, establishes a sliding standard deviation curve as a current fluctuation trend parameter, calculates the active power change gradient based on the power time series using an overlapping window with a window length of 1 second, and generates a dynamic trend coefficient and a response difference; The behavior matching analysis submodule performs Z-score normalization on the dynamic trend coefficient along the time axis, performs range normalization conversion on the response difference, uses a dynamic time warping algorithm to calculate the cumulative distance matrix between the standardized sequence and multiple templates in the standard pattern library, obtains the minimum bending cost value by backtracking the optimal path, and generates a primary fault feature set.

5. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 4 is characterized in that: The Z-score normalization process uses the formula ; in, represents the original dynamic trend coefficient data point, Indicates the arithmetic mean of the dynamic trend coefficient within the current sampling window. Indicates the standard deviation of the dynamic trend coefficient within the current sampling window.

6. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 5 is characterized in that: The environment collaborative verification module includes: The data collaboration submodule calls the temperature rise rate data and condensation critical index data in the primary fault characteristics, aligns the time series of the two sets of data through timestamp matching, calculates the covariance matrix based on the data change per minute, and constructs the correlation relationship between the data through a dynamic weight allocation algorithm to generate a collaborative feature matrix; The model fusion submodule is based on the collaborative feature matrix and adopts the formula: ； The environmental adaptability evaluation value is obtained by calculation, and fuzzy membership matching is performed with the preset condensation formation threshold to generate a fusion evaluation coefficient; in, represents the environmental adaptability assessment value, represents the temperature rise rate weight factor and its value range is [0.5,1.2], Indicates the temperature change inside the cabinet per minute, unit: ℃ / min, It represents the critical compensation coefficient of condensation and is positively correlated with the thermal conductivity of the cabinet material. Indicates the absolute value of the change in ambient humidity, unit: %RH, Represents the environmental compensation factor and its value is ,in is the temperature sensor measurement error, unit: °C, is the humidity sensor measurement error, unit: %RH, Indicates the maximum temperature value inside the cabinet during the monitoring period. Indicates the lowest temperature inside the cabinet during the monitoring period, unit: °C; The instruction generation submodule uses a sliding window algorithm to extract the coefficient change trend characteristics based on the fusion evaluation coefficient, and establishes a mapping relationship with the equipment protection level through a preset threshold gradient table. When the coefficient exceeds the dynamic adjustment threshold within three consecutive monitoring cycles, the multi-level response strategy generator is activated and a linkage response instruction is output.

7. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 6 is characterized in that: The closed-loop control execution module includes: The instruction parsing submodule parses the device identification code and parameter setting byte in the linkage response instruction, matches the exhaust device RS485 communication address with the dehumidification device Modbus register number, and generates a device control instruction set; The PID adjustment submodule collects the number of pulses fed back by the motor encoder according to the target speed value in the control instruction set of the device and calculates the real-time speed deviation. It uses a comprehensive calculation method of proportional error amplification, integral accumulated error compensation, and differential change rate prediction to output the duty cycle adjustment of the inverter pulse width modulation wave after 24-bit AD conversion implemented by the ADS1256 chip to generate the speed adjustment value. The feedback acquisition submodule obtains the exhaust device rotor speed pulse signal through the Hall effect sensor, uses the current transformer to collect the effective value of the dehumidification equipment working current, performs 24-bit AD conversion and data frame packaging on the equipment operating parameters after the speed adjustment is executed, and generates the equipment feedback signal; The resolution of the 24-bit AD conversion is set to 0.1 rpm; The data frame encapsulation format complies with the IEC60870-5-104 protocol standard.

8. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 7 is characterized in that: The behavior path optimization module includes: The signal feature extraction submodule collects the current waveform sampling values ​​and power output timing data in the feedback signal of the device, sets the window width to an integer multiple of the sampling period for sliding interception, calculates the current range to mean ratio in each window as a stability indicator, and calculates the mean attenuation amplitude of adjacent peaks of the power curve as an attenuation indicator to generate a current stability parameter set and a power attenuation coefficient set; The strategy weight adjustment submodule constructs a difference absolute value matrix corresponding to multiple elements of the current stability parameter set and the power attenuation coefficient set, initializes a weight vector and sets a constraint on the number of iterations, uses the minimum value of the weighted sum of the matrix row vectors as a screening condition when calculating the individual fitness in each genetic algorithm iteration, updates the population parameters through a crossover mutation operation, and generates an optimized weight vector; The parameter reverse transfer submodule performs a point multiplication operation on the optimized weight vector and the current stability parameter set, and superimposes the power attenuation coefficient set to construct a three-dimensional correction value calculation space with the X-axis as the current harmonic amount, the Y-axis as the power attenuation rate, and the Z-axis as the temperature rise gradient. The back propagation mechanism is used to map the spatial coordinates to the input layer nodes of the electrical parameter analysis module to generate the parameter correction coefficient.

9. The moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to claim 8 is characterized in that: The current harmonics are defined as the total distortion rate of the 3rd to 21st harmonic components; The power attenuation rate represents the percentage of decrease in active power per unit time; The temperature rise gradient represents the absolute value of the rate of change of the temperature inside the cabinet per minute.

10. Moisture-proof insulated ring main unit online monitoring ring main unit with fault display, characterized in that, The ring main unit is used to implement the moisture-proof and environmentally friendly ring main unit online monitoring system with continuous fault indication according to any one of claims 1 to 9, comprising: Double-layer insulated cabinet, with the inner wall coated with nano-scale hydrophobic coating and the interlayer filled with aerogel insulation material; Embedded monitoring unit, fixed at the busbar connection and integrating Hall current sensor and voltage differential probe; The temperature and humidity sensor array is evenly spaced and distributed at six spatial quadrant monitoring points inside the cabinet; A fault indicator is installed at the observation window of the busbar room and is connected to the closed-loop control execution module via the RS485 bus. The fault indicator is powered directly by the ring main cabinet or is equipped with a rechargeable and dischargeable lithium battery with a capacity of ≥2000mAh; An axial flow exhaust device is installed on the top of the cabinet and is connected to the closed-loop control execution module via a CAN bus that complies with the ISO11898-2 standard; The rotary dehumidification equipment is installed at the air inlet at the bottom of the cabinet and the impeller speed is linearly related to the output value of the PID control model; The contact angle of the nanoscale hydrophobic coating is ≥150°; The thermal conductivity of the aerogel thermal insulation material is ≤0.02W / (m·K); The measurement accuracy of the Hall current sensor is ±0.5% FS; The common mode rejection ratio of the voltage differential probe is ≥120dB.

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