State optimization method of embedded pole for primary and secondary fusion circuit breaker

Abstract

The invention discloses a state optimization method of a solid-sealed polar pole for a primary and secondary fusion circuit breaker. The invention aims to solve the problem of circuit breaker misoperation caused by a traditional fixed threshold criterion. According to the method, the temperature gradient, the mechanical vibration frequency, the strain distribution and the electric field intensity data of the solid-sealed polar pole are collected in real time through a multi-physics field coupling monitoring network, and the comprehensive degradation index (CDI) is constructed by combining spatial-temporal feature extraction of partial discharge signals. The feature weight is dynamically optimized by adopting an improved particle swarm algorithm, the degradation trend is predicted through a bidirectional long-short-term memory network (LSTM), and a dynamic early warning threshold value (DWT) and a critical threshold value (CWT) are generated. Efficient data processing and model iterative optimization are realized, and meanwhile, mechanical strain, electric field distortion and a discharge evolution path are visualized through a three-dimensional digital twin model. Actual measurement shows that the fault misjudgment rate is reduced to be lower than 5%, the early warning response time is shortened to be within 10 seconds, and the local discharge capacity is greatly reduced through the silver nanowire self-healing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a state optimization method for a sealed pole for a primary-secondary fusion circuit breaker. Background Art

[0002] When a single-phase grounding fault occurs in a power system, it may cause multiple safety hazards: first, the fault current will directly threaten the insulation performance of the power distribution and substation equipment, causing equipment damage or even fire; second, the system voltage imbalance will affect the stable operation of the regional power grid, resulting in a decline in power supply quality; more seriously, dangerous step voltages may be generated around the fault point, endangering the lives of people and causing casualties to livestock. To deal with such risks, modern power systems generally use primary and secondary fusion pole-mounted circuit breakers as core protection devices. This device effectively isolates the fault area and ensures system safety through real-time state switching functions (such as switching from closed to open when a fault occurs).

[0003] The current technical solution mainly relies on zero-sequence current / voltage monitoring to realize fault judgment: by continuously collecting line zero-sequence current (I0) and zero-sequence voltage (U0) data, and statically comparing them with preset fixed thresholds (such as I0=10A or U0=30V), the circuit breaker is triggered when the monitoring value exceeds the threshold. However, this solution has significant limitations-fixed thresholds are difficult to adapt to complex working conditions. For example, in cable lines with large capacitive currents, zero-sequence currents of up to 8A may be generated during normal operation; and in the case of transient overvoltage scenarios caused by lightning strikes, the zero-sequence voltage may briefly exceed 25V but is not a real ground fault. Such scenarios can easily cause misjudgment of protection devices, resulting in frequent false tripping of circuit breakers, which not only reduces power supply reliability, but may also cause economic losses due to unnecessary power outages.

[0004] The essence of the false operation problem lies in the lack of dynamic adaptability of the traditional threshold judgment method: it can neither distinguish between real faults and transient interference, nor cope with the time-varying characteristics of line parameters (such as changes in insulation resistance caused by seasonal temperature rise). Therefore, developing an adaptive protection algorithm with intelligent discrimination capabilities to fundamentally improve the accuracy of fault identification has become a technical bottleneck that needs to be broken through in the construction of smart distribution networks. Summary of the invention

[0005] 1. Technical issues to be resolved To solve the above problems, the present invention proposes a state optimization method for a sealed pole for a primary-secondary fusion circuit breaker, which aims to solve the problem of relying on zero-sequence current / voltage monitoring to realize fault judgment and false operation in the prior art. The essence of this method is that the traditional threshold judgment method lacks dynamic adaptability.

[0006] (II) Technical solution A state optimization method for a sealed pole for a primary-secondary fusion circuit breaker of the present invention comprises: Obtain multi-physics field coupling monitoring data inside the sealed pole, the electric field intensity distribution data on the pole surface and inside, and capture the spatiotemporal characteristics of the partial discharge signal of the sealed pole; The multi-physical field coupling monitoring data includes temperature gradient, mechanical vibration frequency and strain distribution, the multi-physical field coupling monitoring data is analyzed to extract a first feature data set, and a comprehensive degradation index is constructed by weighted fusion of the first feature data set to obtain a prediction result; Based on historical data and the prediction results, a long short-term memory network is used to predict the degradation trend in the future period, a dynamic warning threshold and a critical threshold are generated, and an action instruction is generated through the dynamic warning threshold and the critical threshold.

[0007] In the present invention, the analyzing the multi-physical field coupling monitoring data to extract a first feature data set includes: The temperature gradient signal is subjected to empirical mode decomposition to extract the intrinsic mode function, the short-time Fourier transform energy spectrum entropy is calculated for the mechanical vibration frequency signal, the covariance matrix is ​​constructed for the strain distribution and the maximum eigenvalue is extracted to form a multi-dimensional time-frequency domain fusion feature vector.

[0008] The present invention also includes a degradation state visualization reconstruction step, mapping the multi-physical field coupling monitoring data to the three-dimensional digital twin model of the sealed pole, and displaying the spatiotemporal evolution trajectory of the mechanical strain concentration area, the electric field distortion high-risk area and the local discharge pulse through the superposition of thermal maps, and marking the dynamic threshold exceeding area.

[0009] In the present invention, the temporal and spatial characteristics of the local discharge signal of the sealed pole are captured, including: using an improved matching pursuit algorithm to sparsely decompose the ultrasonic signal, the basis function library includes Gaussian modulated sine waves and impulse response waveforms, and the spatial coordinates of the discharge source and the energy diffusion path are located by delay-Doppler joint parameter estimation.

[0010] In the present invention, the method is implemented based on the edge-cloud collaborative computing framework, the edge node performs data preprocessing and feature extraction, the cloud platform performs iterative training of the degradation model, and the compressed degradation index sequence is transmitted between the two through a feature-level differential privacy encryption channel.

[0011] In the present invention, the calculation formula of the comprehensive degradation index is: ,in Indicates the weight of the heat accumulation index, reflecting the effect of temperature on the aging of the pole represents the heat accumulation index, represents the vibration offset, represents the weight of the vibration offset, characterizing the contribution of mechanical stress to performance degradation, represents the electric field distortion rate, Represents the weight of the electric field distortion rate, which measures the impact of abnormal electric field distribution on insulation performance. is the partial discharge energy entropy, The weight of partial discharge energy entropy quantifies the association between discharge pattern complexity and insulation defects; Heat accumulation index Reflects the heat accumulation effect of the pole in long-term operation, and is used to predict the thermal aging of insulation materials and partial discharge energy entropy The higher the entropy value, the more complex the discharge pattern and the greater the risk of insulation defects.

[0012] In the present invention, the weight coefficient , , , Through dynamic adjustment using an improved particle swarm optimization algorithm, its fitness function integrates the KL divergence of historical fault samples and the environmental correction factor of real-time working conditions. The weight adjustment cycle is an integer multiple of the preset time window and meets the constraints. .

[0013] The present invention also includes a fault tracing analysis module. When a critical threshold exceeding event is detected, the historical multi-physical field data is automatically associated to generate a fault evolution tree diagram, and the contribution weight of each physical field parameter is calculated through a causal reasoning engine, and the dominant failure factor and associated confidence are output. The contribution weight is calculated based on the Shapley value algorithm.

[0014] In the present invention, the action instruction includes a hierarchical response strategy: When the first level command is triggered, the pole is in a healthy state and only records data; When the secondary instruction is triggered, the partial discharge suppression module is activated to reduce the electric field distortion by adjusting the potential distribution on the pole surface; When the third-level command is triggered, the mechanical stress compensation device is activated, using piezoelectric ceramics to actively offset vibration energy and notify the operation and maintenance personnel to intervene urgently; The level of the action instruction is determined by the warning threshold and the critical threshold.

[0015] (III) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: (1) In the present invention, the feature weights are optimized based on the improved particle swarm algorithm, and the confidence interval predicted by the LSTM network is combined to generate a dynamic threshold, which can effectively distinguish between real faults and transient interference, and the misjudgment rate is greatly improved; the three-level linkage protection mechanism (data recording-potential adjustment-mechanical compensation) can achieve accurate fault isolation, and the response time is shorter than that of the fixed threshold strategy, avoiding economic losses caused by unnecessary power outages. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 Schematic diagram of the logical structure of the optimization method. DETAILED DESCRIPTION Example

[0018] like Figure 1 The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker provided by the present invention comprises the following steps: S100, obtaining multi-physical field coupling monitoring data inside the sealed pole, and electric field intensity distribution data on the pole surface and inside, and capturing the spatiotemporal characteristics of the partial discharge signal of the sealed pole.

[0019] A high-precision sensor network is embedded inside the epoxy resin packaging layer of the solid-sealed pole. Specifically, six groups of fiber grating sensor groups are arranged equidistantly along the pole axis, each group contains three orthogonal gratings to monitor the mechanical strain in the X / Y / Z axis respectively. The wavelength resolution of each grating is 0.1pm, the temperature measurement accuracy is ±0.5°C, and high-speed sampling of 2000 times per second is achieved through frequency division multiplexing technology.

[0020] Specifically, the multi-physics field coupling monitoring data includes temperature gradient, mechanical vibration frequency and strain distribution. For electric field monitoring, 24 micro capacitive coupling probes are arranged in a ring-shaped pattern around the conductor with a spacing of 5 mm. The probe surface is coated with a nano-scale insulating coating to avoid electric field interference. The original voltage signal collected in real time is processed by a differential amplifier circuit, and the three-dimensional electric field intensity distribution map is reconstructed by a finite element inversion algorithm with a spatial resolution better than 1 mm³.

[0021] In terms of partial discharge detection, a 32-channel ultrasonic sensor array is evenly arranged on the outer surface of the pole. Each unit uses a 40kHz narrowband filter to eliminate environmental noise, and the coordinates of the discharge source are determined by the time difference positioning method. The time and frequency characteristics of the discharge pulse are extracted in combination with the Morlet wavelet transform. The characteristic dimensions include 12 parameters such as pulse amplitude, rise time, and frequency band energy ratio.

[0022] S200, analyzing the multi-physical field coupling monitoring data to extract a first feature data set, and constructing a comprehensive degradation index by weighted fusion of the first feature data set to obtain a prediction result.

[0023] In the data processing layer, it is necessary to build edge computing nodes to realize real-time fusion of multi-source data. After the temperature data is filtered by Kalman filtering to eliminate transient noise, the axial temperature gradient is calculated. and radial heat accumulation index ,in The axial temperature gradient is obtained by integrating the deviation of the temperature curve from the baseline value over the past 30 minutes. Used to diagnose local hot spots in real time and guide operation and maintenance personnel to locate fault areas.

[0024] After the main frequency component of the mechanical vibration signal is extracted by fast Fourier transform, the main frequency offset Δf is calculated using a sliding window. The window length is 10 seconds and the step length is 1 second. When Δf exceeds the baseline value by 15%, the strain energy density (SED) calculation module is triggered. The SED value is calculated according to the formula Dynamic updates, including is the isotropic strain, is the corresponding stress. Electric field distortion rate It is defined as the cosine similarity between the current electric field distribution and the standard distribution, which is obtained by normalizing the reconstructed electric field vector with the standard template after the dot product operation. The calculation cycle is 5 times per second.

[0025] Partial discharge energy entropy Then the Shannon entropy is calculated by the frequency domain energy of all discharge pulses within 1 minute, and the formula is: ,in is the energy proportion of each frequency band.

[0026] The dynamic weight adjustment mechanism of the comprehensive degradation index (CDI) adopts an improved particle swarm algorithm. , , , As the basic weight, the objective function is set to minimize the mean square error between historical fault data and CDI values. When the algorithm is running, the latest fault case library is downloaded from the cloud every 15 minutes, and 200 particles are generated through parallel calculation for iterative optimization. The inertia weight decreases linearly from 0.9 to 0.4, and the acceleration constants c1=1.5 and c2=1.8. After 50 generations of iteration, the optimal weight combination is output. For example, under the condition of increased humidity in the rainy season, the system automatically increases the partial discharge weight δ to 0.35, and reduces the mechanical vibration weight β to 0.15 to adapt to the impact of environmental changes.

[0027] S300, based on historical data and the prediction results, using a long short-term memory network to predict the degradation trend in the future period, generate a dynamic warning threshold and a critical threshold, and generate an action instruction through the dynamic warning threshold and the critical threshold.

[0028] Specifically, historical data includes multi-physics field coupling monitoring data (such as temperature gradient, mechanical vibration frequency, strain distribution) and extracted comprehensive degradation index (CDI). First, the original data is cleaned to remove outliers and noise, and the dimensions are unified through Z-score normalization. Time series data is segmented according to fixed time windows (such as every hour) to form input-output pairs, such as the CDI sequence of the past 24 hours as input and the CDI sequence of the next 6 hours as output target.

[0029] The dynamic threshold generation module adopts a two-layer LSTM network structure. The input layer contains 16-dimensional features such as CDI time series data, ambient temperature and humidity, and load current. The hidden layer is set with 128 neurons, and the output layer predicts the CDI change curve within the next hour. The training data comes from 120,000 sets of samples collected in 3 years. The Adam optimizer is used for model training. The initial learning rate is 0.001 and decays by 50% every 10 rounds.

[0030] The LSTM output is randomly perturbed multiple times, such as adding Gaussian noise, to generate the probability distribution of future CDI. The dynamic warning threshold (DWT) is set to the 95th percentile of the predicted value, and the critical threshold (CWT) is set to the 99th percentile. For example, if the mean CDI predicted for the next hour is 75 and the standard deviation is 5, then the DWT is 75 + 1.645×5 ≈ 83.2 and the CWT is 75 + 2.326×5 ≈ 86.6. The threshold is updated every 5 minutes to ensure that it adapts to the latest data trends.

[0031] The prediction results are simulated by Monte Carlo to generate confidence intervals. The dynamic warning threshold DWT takes the 95% quantile of the predicted value, and the critical threshold CWT takes the 99% quantile. When it is detected that CDI exceeds DWT for three consecutive times, the system activates the potential control module and applies reverse voltage through 64 micro-electrodes distributed on the surface of the pole. The adjustment amount is dynamically calculated according to the electric field distortion rate. The maximum adjustment voltage is ±5kV, and the response time is less than 20ms.

[0032] Action instructions include a graded response strategy: Level 1 instruction (CDI ≥ DWT and < CWT), when the level 1 instruction is triggered, the pole is in a healthy state and only data is recorded; Secondary instruction (CDI ≥ CWT): When the secondary instruction is triggered, the partial discharge suppression module is activated to reduce the electric field distortion by adjusting the potential distribution on the pole surface; Level 3 instruction (exceeding CWT three times in a row): When the level 3 instruction is triggered, the mechanical stress compensation device is activated, using piezoelectric ceramics to actively offset the vibration energy, and notifying the operation and maintenance personnel to intervene urgently.

[0033] In particular, when the third level instruction is reached, the self-healing function of the sealed pole can also be set and activated.

[0034] In the mechanical stress compensation link, the piezoelectric ceramic array built into the support structure generates anti-phase mechanical waves according to the vibration spectrum characteristics. The control system analyzes the main frequency component of the vibration in real time, generates a drive signal synchronously through a digital phase-locked loop, and outputs it to the piezoelectric ceramic through a high-voltage amplifier. The amplitude adjustment accuracy reaches 0.1μm, effectively attenuating the vibration energy by more than 60%. When the CDI breaks through the CWT, the system immediately activates multi-level interlocking protection: first, a level 4 alarm signal is sent to the SCADA system, and the standby cooling fan is synchronously started to reduce the temperature by 10°C, and an axial preload is applied to the pole through a hydraulic mechanism to relieve mechanical deformation.

[0035] The self-healing function is achieved through microcapsules embedded in epoxy resin. The capsule shell is made of polyurethane material and encapsulates a silicon-based repair fluid containing silver nanowires. When the local discharge energy entropy exceeds the threshold for 5 consecutive minutes, the high-frequency electric field triggers the capsule to rupture. Driven by the electric field, the repair fluid migrates to the discharge area in a directional manner and bridges the insulation defects through silver nanowires. Actual measurements show that the local discharge can be reduced to less than 15% of the initial value within 30 seconds. Operation and maintenance personnel can view real-time three-dimensional status maps through AR glasses. The system automatically marks high-risk areas and provides maintenance priority recommendations, reducing the average troubleshooting time to 40% of traditional methods.

[0036] The above-described embodiments are merely descriptions of preferred implementations of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made by ordinary persons in the art to the technical solution of the present invention should fall within the protection scope of the present invention, and the technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A method for optimizing the state of a sealed pole for a primary-secondary fusion circuit breaker, characterized in that: include: Obtain multi-physics field coupling monitoring data inside the sealed pole, the electric field intensity distribution data on the pole surface and inside, and capture the spatiotemporal characteristics of the partial discharge signal of the sealed pole; The multi-physical field coupling monitoring data includes temperature gradient, mechanical vibration frequency and strain distribution, the multi-physical field coupling monitoring data is analyzed to extract a first feature data set, and a comprehensive degradation index is constructed by weighted fusion of the first feature data set to obtain a prediction result; Based on historical data and the prediction results, a long short-term memory network is used to predict the degradation trend in the future period, a dynamic warning threshold and a critical threshold are generated, and an action instruction is generated through the dynamic warning threshold and the critical threshold.

2. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 1 is characterized in that: The analyzing the multi-physics field coupling monitoring data to extract a first feature data set comprises: The temperature gradient signal is subjected to empirical mode decomposition to extract the intrinsic mode function, the short-time Fourier transform energy spectrum entropy is calculated for the mechanical vibration frequency signal, the covariance matrix is ​​constructed for the strain distribution and the maximum eigenvalue is extracted to form a multi-dimensional time-frequency domain fusion feature vector.

3. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 2 is characterized in that: It also includes a degradation state visualization reconstruction step, mapping the multi-physical field coupling monitoring data to the three-dimensional digital twin model of the sealed pole, displaying the mechanical strain concentration area, the high-risk area of ​​electric field distortion and the spatiotemporal evolution trajectory of the local discharge pulse through the superposition of thermal maps, and marking the dynamic threshold exceeding area.

4. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 2 or 3, characterized in that: The method of capturing the spatiotemporal characteristics of the partial discharge signal of the sealed pole includes: using an improved matching pursuit algorithm to sparsely decompose the ultrasonic signal, the basis function library includes Gaussian modulated sine waves and impulse response waveforms, and locating the spatial coordinates of the discharge source and the energy diffusion path through time delay-Doppler joint parameter estimation.

5. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 4, characterized in that: The method is implemented based on the edge-cloud collaborative computing framework. The edge node performs data preprocessing and feature extraction, and the cloud platform performs iterative training of the degradation model. The compressed degradation index sequence is transmitted between the two through a feature-level differential privacy encryption channel.

6. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 5, characterized in that: The calculation formula of the comprehensive degradation index is: ,in Indicates the weight of the heat accumulation index, reflecting the effect of temperature on the aging of the pole represents the heat accumulation index, represents the vibration offset, represents the weight of the vibration offset, characterizing the contribution of mechanical stress to performance degradation, represents the electric field distortion rate, Represents the weight of the electric field distortion rate, which measures the impact of abnormal electric field distribution on insulation performance. is the partial discharge energy entropy, The weight of partial discharge energy entropy quantifies the association between discharge pattern complexity and insulation defects; Heat accumulation index Reflects the heat accumulation effect of the pole in long-term operation, and is used to predict the thermal aging of insulation materials and partial discharge energy entropy The higher the entropy value, the more complex the discharge pattern and the greater the risk of insulation defects.

7. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 6, characterized in that: The weight coefficient , , , Through dynamic adjustment using an improved particle swarm optimization algorithm, its fitness function integrates the KL divergence of historical fault samples and the environmental correction factor of real-time working conditions. The weight adjustment cycle is an integer multiple of the preset time window and meets the constraints. .

8. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 7, characterized in that: It also includes a fault tracing analysis module. When a critical threshold exceeding event is detected, it automatically associates historical multi-physical field data to generate a fault evolution tree diagram, and calculates the contribution weight of each physical field parameter through a causal reasoning engine, outputs the dominant failure factor and associated confidence, and the contribution weight is calculated based on the Shapley value algorithm.

9. The state optimization method of the sealed pole for the primary and secondary fusion circuit breaker according to claim 8, characterized in that: The action instructions include a hierarchical response strategy: When the first level command is triggered, the pole is in a healthy state and only records data; When the secondary instruction is triggered, the partial discharge suppression module is activated to reduce the electric field distortion by adjusting the potential distribution on the pole surface; When the third-level command is triggered, the mechanical stress compensation device is activated, using piezoelectric ceramics to actively offset vibration energy and notify the operation and maintenance personnel to intervene urgently; The level of the action instruction is determined by the warning threshold and the critical threshold.

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