Electrolytic bath ground insulation fault detection method based on voltage phase

Abstract

The invention relates to the technical field of insulation fault detection, and particularly discloses a voltage phase-based electrolytic bath ground insulation fault detection method, which comprises the following steps of: firstly, loading an alternating current excitation signal to each electrolytic bath in an electrolytic bath sequence, and simultaneously collecting state parameters of each electrolytic bath; then, a neural network model based on deep learning is used for conducting embedded coding and global dynamic aggregation on the state parameters of all the electrolytic cells so as to capture the overall operation state mode of the electrolytic cell sequence, and then state parameter embedded representation of the detected electrolytic cells serves as query features; and based on the semantic offset between the detected electrolytic cell and the overall operation state mode of the electrolytic cell sequence, whether the detected electrolytic cell has the ground insulation fault or not is intelligently identified. Therefore, accurate judgment on the ground insulation fault of the electrolytic cell can be realized, and the adaptability to different working condition fluctuations and noise environments is improved, so that the risk of misjudgment or missed judgment is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of insulation fault detection, and more specifically, to a method for detecting insulation fault of an electrolytic cell to ground based on voltage phase. Background Art

[0002] The electrolytic cell is an important device used for electrochemical reactions in the chemical and metallurgical industries. Its performance is directly related to production efficiency and product quality. During operation, the insulation performance of the electrolytic cell to the ground is one of the necessary conditions to ensure safe operation and maintain efficient production. Once the electrolytic cell has an insulation failure to the ground, it may cause current leakage, affect the stability and safety of the electrolysis process, and may even cause a safety accident.

[0003] Traditional methods for detecting electrolytic cell insulation faults to the ground mainly include insulation resistance measurement method, leakage current detection method, etc. However, these methods have many limitations. The insulation resistance measurement method requires the electrolytic cell to be powered off for offline measurement, which cannot achieve real-time online monitoring, and the measurement results are easily affected by environmental factors and have low accuracy. The leakage current detection method has low detection sensitivity for tiny leakage currents, making it difficult to detect problems in time at the early stage of a fault.

[0004] In recent years, although some improved detection methods have emerged, for example, the invention patent with publication number CN116106697A discloses a voltage phase-based electrolytic cell insulation fault detection method, which mainly loads an AC excitation signal to the electrolytic cell, and synchronously measures the cell AC voltage of each electrolytic cell, the cell-to-ground AC voltage, the phase difference between the AC voltages, and the AC voltage difference, and determines whether there is an insulation fault to the ground based on the preset voltage setting value, pressure difference setting value, and phase difference setting value.

[0005] However, although this method achieves the purpose of fault detection to a certain extent, it mainly relies on fixed voltage setting values, pressure difference setting values ​​and phase difference setting values ​​for fault judgment. The analysis and processing of parameters are relatively limited, and the overall operating status mode of the electrolytic cell is not fully considered. It is easily affected by operating fluctuations and noise, resulting in misjudgment or missed judgment.

[0006] Therefore, an optimized voltage phase-based electrolytic cell insulation fault detection method is expected. Summary of the invention

[0007] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method for detecting the insulation fault of an electrolytic cell to the ground based on the voltage phase, which first loads an AC excitation signal to each electrolytic cell in the electrolytic cell sequence, and collects the state parameters of each electrolytic cell at the same time, and then uses a neural network model based on deep learning to embed the state parameters of each electrolytic cell and perform global dynamic aggregation to capture the overall operating state mode of the electrolytic cell sequence, and then uses the embedded representation of the state parameters of the detected electrolytic cell as a query feature, based on the semantic offset between it and the overall operating state mode of the electrolytic cell sequence, so as to intelligently identify whether the detected electrolytic cell has an insulation fault to the ground. In this way, accurate judgment of the insulation fault of the electrolytic cell to the ground can be achieved, and the adaptability to different operating fluctuations and noise environments is improved, thereby effectively reducing the risk of misjudgment or missed judgment.

[0008] According to one aspect of the present application, a method for detecting an electrolytic cell-to-ground insulation fault based on a voltage phase is provided, comprising: Loading an AC excitation signal to each electrolytic cell in the electrolytic cell sequence and determining a state parameter of each electrolytic cell in the electrolytic cell sequence; Vectorizing and encoding the state parameters of each electrolytic cell to obtain a sequence of electrolytic cell state parameter embedded encoding vectors; Performing feature dynamic aggregation based on potential energy distribution on the sequence of electrolytic cell state parameter embedded coding vectors to obtain a significant aggregation coding vector of the electrolytic cell sequence state parameters; Extracting the electrolytic cell state parameter embedded coding vector of the detected electrolytic cell from the sequence of electrolytic cell state parameter embedded coding vectors as the electrolytic cell state parameter query coding vector; A semantic offset measurement is performed on the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector to determine whether the detected electrolytic cell has a ground insulation fault.

[0009] Compared with the prior art, the voltage phase-based electrolytic cell insulation fault detection method provided by the present application first loads an AC excitation signal to each electrolytic cell in the electrolytic cell sequence, collects the state parameters of each electrolytic cell at the same time, and then uses a neural network model based on deep learning to embed the state parameters of each electrolytic cell and perform global dynamic aggregation to capture the overall operating state mode of the electrolytic cell sequence. Then, the state parameter embedding representation of the detected electrolytic cell is used as a query feature, and based on the semantic offset between it and the overall operating state mode of the electrolytic cell sequence, it is used to intelligently identify whether the detected electrolytic cell has an insulation fault to the ground. In this way, accurate judgment of the electrolytic cell insulation fault to the ground can be achieved, and the adaptability to different operating condition fluctuations and noise environments is improved, thereby effectively reducing the risk of misjudgment or missed judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 The present invention is a flowchart of a method for detecting an electrolytic cell-to-ground insulation fault based on a voltage phase according to an embodiment of the present application.

[0012] Figure 2 It is a data flow diagram of a method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to an embodiment of the present application.

[0013] Figure 3 This is a flowchart of sub-step S3 of the method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to an embodiment of the present application.

[0014] Figure 4 This is a flowchart of sub-step S31 of the method for detecting electrolytic cell-to-ground insulation fault based on voltage phase according to an embodiment of the present application.

[0015] Figure 5 This is a flowchart of sub-step S5 of the method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0018] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] It is worth noting that in this application, all actions to obtain data are carried out in compliance with the relevant data protection laws and policies of the country where the data is located, and with the authorization given by the owner of the corresponding device.

[0021] As mentioned in the background technology above, patent CN116106697A proposes a method for detecting insulation faults of electrolytic cells to the ground based on voltage phase, which mainly loads an AC excitation signal to the electrolytic cell, and synchronously measures the cell AC voltage of each electrolytic cell, the cell-to-ground AC voltage, the phase difference between the AC voltages, and the AC voltage difference, and determines whether there is an insulation fault to the ground based on the preset voltage setting value, pressure difference setting value, and phase difference setting value.

[0022] Although the above-mentioned electrolytic cell insulation fault detection method based on voltage phase has achieved the purpose of fault detection to a certain extent, it mainly relies on fixed voltage setting values, pressure difference setting values ​​and phase difference setting values ​​for fault judgment, and the analysis and processing of parameters are relatively limited, and the overall operating state mode of the electrolytic cell is not fully considered. It is easily affected by operating condition fluctuations and noise, resulting in misjudgment or missed judgment. In response to this technical problem, the present application proposes an optimized electrolytic cell insulation fault detection method based on voltage phase, which first loads an AC excitation signal to each electrolytic cell in the electrolytic cell sequence, and collects the state parameters of each electrolytic cell at the same time, and then uses a neural network model based on deep learning to embed code and globally dynamically aggregate the state parameters of each electrolytic cell to capture the overall operating state mode of the electrolytic cell sequence, and then uses the state parameter embedding representation of the detected electrolytic cell as a query feature, based on the semantic offset between it and the overall operating state mode of the electrolytic cell sequence, so as to intelligently identify whether the detected electrolytic cell has an insulation fault to the ground. In this way, accurate judgment of the insulation fault of the electrolytic cell to the ground can be achieved, and the adaptability to different operating fluctuations and noise environments is improved, thereby effectively reducing the risk of misjudgment or missed judgment.

[0023] Figure 1The present invention is a flowchart of a method for detecting an electrolytic cell-to-ground insulation fault based on a voltage phase according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a method for detecting an insulation fault of an electrolytic cell to ground based on voltage phase according to an embodiment of the present application. Figure 1 and Figure 2 As shown, the method for detecting the insulation fault of an electrolytic cell to the ground based on voltage phase includes the following steps: S1, loading an AC excitation signal for each electrolytic cell in an electrolytic cell sequence, and determining the state parameters of each electrolytic cell in the electrolytic cell sequence; S2, vectorizing and encoding the state parameters of each electrolytic cell to obtain a sequence of electrolytic cell state parameter embedded coding vectors; S3, performing feature dynamic aggregation based on potential energy distribution on the sequence of electrolytic cell state parameter embedded coding vectors to obtain a significant aggregation coding vector of the electrolytic cell sequence state parameters; S4, extracting the electrolytic cell state parameter embedded coding vector of the detected electrolytic cell from the sequence of electrolytic cell state parameter embedded coding vectors as an electrolytic cell state parameter query coding vector; S5, performing semantic offset measurement on the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector to determine whether the detected electrolytic cell has an insulation fault to the ground.

[0024] In the above-mentioned electrolytic cell-to-ground insulation fault detection method based on voltage phase, the step S1 is to load an AC excitation signal to each electrolytic cell in the electrolytic cell sequence, and determine the state parameters of each electrolytic cell in the electrolytic cell sequence. Wherein, the state parameters include the cell AC voltage of each electrolytic cell, the cell-to-ground AC voltage of each electrolytic cell, the phase difference between the cell AC voltage of each electrolytic cell and the cell-to-ground AC voltage, and the AC voltage difference between the cell AC voltage of the electrolytic cell and the cell AC voltage of the next electrolytic cell. It should be understood that by applying an AC excitation signal, the dynamic behavior response of the electrolytic cell in actual work can be more accurately simulated. In an ideal fault-free situation, the voltage and current inside the electrolytic cell are in phase or have a very small phase difference (depending on the specific design of the electrolytic cell, the materials used, and the operating conditions). At this time, the electrolytic cell is well insulated from the ground, and no abnormal current path is generated. When the electrolytic cell has an insulation fault to the ground, such as damage or aging of the insulating material, an additional path will be formed between the electrolytic cell and the ground. This path usually has capacitive or inductive characteristics, which will lead to a new current path, thereby changing the original current distribution and phase relationship. Based on this, the present application evaluates whether the electrolytic cell has an insulation fault to the ground by collecting the cell AC voltage of each electrolytic cell (i.e., the voltage across the two electrodes of the electrolytic cell), the cell-to-ground AC voltage of each electrolytic cell (i.e., the voltage of the electrolytic cell relative to the ground), the phase difference between the cell AC voltage of each electrolytic cell and the cell-to-ground AC voltage, and the AC voltage difference between the cell AC voltage of the electrolytic cell and the cell AC voltage of the next electrolytic cell. Among them, the cell AC voltage directly reflects the energy input during the electrolysis process and is one of the basic conditions for ensuring the normal progress of the electrolysis reaction. The cell-to-ground AC voltage reveals the insulation condition between the electrolytic cell and the earth. The phase difference between the cell AC voltage and the cell-to-ground AC voltage reflects the impedance characteristics in the circuit, which helps to analyze whether there are abnormal capacitive or inductive paths in the circuit. The cell AC voltage difference can provide the voltage balance between adjacent electrolytic cells, which helps to identify whether there is a local short circuit or ground fault. Through the comprehensive analysis of the above status parameters, the insulation condition of the electrolytic cell can be evaluated more comprehensively, thereby improving the accuracy of fault detection.

[0025] In order to ensure the accuracy and real-time performance of the electrolytic cell-to-ground insulation fault detection, the cell AC voltage, cell-to-ground AC voltage, phase difference between cell voltages, and AC voltage difference between adjacent cells must be accurately collected. The following is a detailed description of the specific collection method: First, in terms of hardware selection and installation, high-precision sensors suitable for industrial environments are needed to measure the required voltage parameters. For the measurement of the tank AC voltage and the tank-to-ground AC voltage, voltage dividers or current transformers and isolated voltage sensors are used to ensure safety and reduce the impact of electromagnetic interference. Phase difference measurement relies on phase detection devices such as digital phase-locked loops (DLLs) or other special equipment, while the voltage difference between adjacent tanks is directly measured through a differential amplifier to ensure signal linearity and accuracy. Plan the sensor network according to the actual arrangement of the electrolytic cells, ensure that there is enough space around each electrolytic cell to arrange the sensors, and the connection lines are as short as possible to reduce noise introduction. All signal transmission lines are shielded, and communication protocols with strong anti-interference capabilities (such as RS485, CAN bus) are selected. If necessary, optical fiber transmission is considered to avoid the influence of strong electric fields. Install filters between the sensor and the data acquisition platform to filter out high-frequency noise and other irrelevant components to improve signal quality.

[0026] In the actual installation process, the hardware selection is not limited to the types mentioned above. Considering the differences in requirements for different application scenarios, customized selection may also be required for specific conditions. For example, for electrolytic cells working in high temperature environments, sensors made of high temperature resistant materials should be selected; while in corrosive environments, products with anti-corrosion design should be used. In addition, in order to ensure long-term stable operation, the sensor should have good reliability and long life characteristics, and be able to withstand harsh working conditions without being easily damaged. During installation, attention should also be paid to the location layout of the sensor to ensure that it can effectively cover the target measurement area without hindering the operation and maintenance of other equipment. For some special locations such as electrolytic cells in narrow spaces, miniaturized or flexible sensors may be required to adapt to complex structures. At the same time, grounding during installation is very important, and all sensors should be properly grounded to avoid introducing unnecessary noise and improve the accuracy of measurement results.

[0027] Next, in the design of the data acquisition system, a centralized or distributed data acquisition platform is built, equipped with a high-performance processor and a large-capacity storage medium to ensure that it can handle a large number of high-frequency sampling needs. The platform supports various types of input interfaces (analog, digital, etc.), which is convenient for connecting to different types of sensors and has good scalability to adapt to the monitoring points that may be added in the future. It is key to achieve simultaneous sampling of all channels, and ensure time consistency through hardware triggering or software timing, which is especially important for calculating phase difference. Set a reasonable sampling frequency and period. For parameters with large transient changes such as phase difference, use a higher sampling rate; for relatively stable parameters, the sampling frequency can be appropriately reduced to save resources.

[0028] When building a data acquisition platform, in addition to considering performance and compatibility, attention should also be paid to the reliability and redundant design of the system. In order to prevent a single point of failure from causing the failure of the entire system, backup units such as backup power supplies and redundant controllers can be set up at important nodes. In addition, the use of a modular architecture can make the system easier to maintain and upgrade. When a component has a problem, it can be quickly replaced without affecting the overall function. The data transmission path should also have a certain fault tolerance, such as using twisted pair or optical fiber and other media with good anti-interference characteristics, and configuring an automatic retransmission mechanism to deal with occasional data loss. For occasions with strong remote monitoring needs, wireless communication modules can also be deployed, but it is necessary to ensure that they meet industrial standards and provide sufficient bandwidth and security.

[0029] Entering the data collection process stage, before starting formal data collection, the system is initialized, including calibrating all sensors, checking the integrity of the connection lines, configuring the parameters of the data collection platform, etc., to ensure that everything is ready. Conduct preliminary tests to verify that the sensors are working properly, the data collection platform can correctly receive and record data, and adjust any problems found until they reach the best state. After starting the data collection program, read data from each sensor at a predetermined time interval and store it in the local database. At the same time, add a timestamp mark to each record to track the trend of changes during subsequent analysis. Back up the collected data regularly to prevent data loss due to unexpected situations. For important data, it can also be uploaded to the cloud for off-site storage to further ensure data security. Establish a real-time monitoring interface to display the current cell AC voltage, cell-to-ground AC voltage, phase difference and adjacent cell voltage difference of each electrolytic cell, so that operators can intuitively understand the system operation status. Set the alarm threshold, and automatically issue an alarm when a parameter exceeds the set range, reminding relevant personnel to deal with potential problems in a timely manner to avoid the expansion of faults.

[0030] During the initialization phase, in addition to basic hardware calibration and connection checks, a comprehensive evaluation of the synchronization performance of the entire system is required. Since multi-channel simultaneous sampling is involved, even a slight time deviation may affect the accuracy of the final calculation result. Therefore, it is recommended to use a precision clock source to uniformly control the sampling time of all sensors to ensure data consistency and reliability. In addition, it is necessary to optimize and adjust various configuration parameters of the data acquisition platform, such as sampling frequency, filtering algorithm, etc., to find the best combination that best suits the current application scenario. This not only helps to improve data quality, but also effectively extends the service life of the system. During the initial operation, closely monitor the changes in various indicators, record possible problems and their solutions, and accumulate experience for subsequent routine maintenance.

[0031] In the process of continuous data collection, it is crucial to maintain an efficient and stable data flow. To this end, a complete data management strategy should be established, covering all aspects from raw data collection to final application. First, in the front-end collection link, ensure that each sensor can stably output high-quality data at the preset frequency. Secondly, in the intermediate transmission link, optimize the network topology to reduce the occurrence of delays and packet loss. Finally, in the back-end storage link, use efficient compression algorithms and indexing technologies to save storage space and facilitate fast retrieval. In this way, not only can the overall quality of the data be improved, but it also helps to discover potential risks in a timely manner and take preventive measures in advance. At the same time, as the amount of data grows, regularly clean up expired or useless data to ensure that the system is always in the best working condition.

[0032] In summary, through the above-mentioned integrated method, the accurate collection of the cell AC voltage, cell-to-ground AC voltage, phase difference and adjacent cell voltage difference of each electrolytic cell can be effectively achieved, providing reliable data support for the safe and stable operation of the electrolytic cell.

[0033] In the above-mentioned method for detecting the insulation fault of the electrolytic cell to the ground based on the voltage phase, the step S2 vectorizes the state parameters of each electrolytic cell to obtain a sequence of electrolytic cell state parameter embedded coding vectors. It should be understood that the present application takes into account that the original state parameters (such as voltage, phase difference, etc.) are usually multidimensional data with different data scales and units. Therefore, in order to realize the comprehensive processing and analysis of multidimensional data, the present application further uses the state parameter embedded coding matrix to vectorize the state parameters of each electrolytic cell, so as to map the parameters of different scales and units to a common feature space, dig out the intrinsic connection and pattern between the multidimensional state parameters, and convert the multidimensional heterogeneous data of each electrolytic cell into a low-dimensional dense vector representation, thereby obtaining a sequence of electrolytic cell state parameter embedded coding vectors, so as to realize the comprehensive characterization of the operating state of each electrolytic cell.

[0034] In the above-mentioned voltage phase-based electrolytic cell-to-ground insulation fault detection method, the step S3 performs feature dynamic aggregation based on potential energy distribution on the sequence of electrolytic cell state parameter embedded coding vectors to obtain a significantly aggregated coding vector of the electrolytic cell sequence state parameters. It should be understood that considering that the electrolytic cell is a complex industrial system, its operating state not only depends on the behavior of a single electrolytic cell, but also needs to consider the collaborative working mode of the entire electrolytic cell sequence. Therefore, the present application further performs feature aggregation analysis on the sequence of electrolytic cell state parameter embedded coding vectors to capture the interactions and overall operating rules between the electrolytic cells in the electrolytic cell sequence, thereby achieving global modeling of the operating state of the entire electrolytic cell sequence, and providing a more comprehensive perspective for the accurate identification of electrolytic cell ground insulation faults. Among them, Figure 3FIG. 4 is a flowchart of sub-step S3 of the method for detecting an insulation fault of an electrolytic cell to ground based on a voltage phase according to an embodiment of the present application. Figure 3 As shown, the step S3 includes the steps of: S31, calculating the pseudo-anchor aggregation center representation vector of the electrolytic cell sequence state parameters based on the characteristic static potential energy distribution of the sequence of the electrolytic cell state parameter embedded coding vectors; S32, calculating the aggregation movement direction of each electrolytic cell state parameter embedded coding vector in the sequence of the electrolytic cell state parameter embedded coding vectors relative to the pseudo-anchor aggregation center representation vector of the electrolytic cell sequence state parameters to obtain a sequence of electrolytic cell state parameter aggregation movement directions; S33, based on the sequence of electrolytic cell state parameter aggregation movement directions, dynamically aggregating the sequence of the electrolytic cell state parameter embedded coding vectors toward the pseudo-anchor aggregation center representation vector of the electrolytic cell sequence state parameters to obtain the electrolytic cell sequence state parameter significant aggregation coding vector.

[0035] Figure 4 FIG. 4 is a flowchart of sub-step S31 of the method for detecting an insulation fault of an electrolytic cell to ground based on a voltage phase according to an embodiment of the present application. Figure 4 As shown, the step S31 includes the steps of: S311, inputting each electrolytic cell state parameter embedding coding vector in the sequence of the electrolytic cell state parameter embedding coding vector into the characteristic static potential energy measurement network to obtain a sequence of electrolytic cell state parameter static potential energy measurement coefficients; S312, inputting the sequence of electrolytic cell state parameter static potential energy measurement coefficients into the characteristic energy level screening gating unit to obtain a sequence of electrolytic cell state parameter static potential energy weight factors; S313, based on the sequence of electrolytic cell state parameter static potential energy weight factors, calculating the weighted sum of the sequence of electrolytic cell state parameter embedding coding vectors to obtain the pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameters.

[0036] More specifically, in a specific example of the present application, the step S311 includes: performing Z-score normalization on the electrolytic cell state parameter embedded coding vector to obtain a standardized electrolytic cell state parameter embedded coding vector; calculating the sum of the cube of each eigenvalue in the standardized electrolytic cell state parameter embedded coding vector divided by the characteristic scale value of the standardized electrolytic cell state parameter embedded coding vector to obtain the electrolytic cell state parameter static potential energy measurement coefficient, which is expressed as follows: in, represents a sequence of embedded coding vectors of the electrolytic cell state parameters, , , and respectively represent the first, second, and third in the sequence of the electrolytic cell state parameter embedded coding vector and The electrolyzer state parameters are embedded in the encoding vector, is the number of feature vectors in the sequence of the electrolytic cell state parameter embedded coding vector, Indicates the The electrolytic cell state parameters are embedded in the encoding vector The eigenvalues ​​at the positions, and Respectively represent the The feature mean and feature variance of the embedding encoding vector of the electrolytic cell state parameters, is the characteristic scale value of the electrolytic cell state parameter embedding coding vector, For the said The static potential energy metric coefficients of the electrolytic cell state parameters are embedded in the encoding vector.

[0037] That is, by measuring the potential energy level of the embedded coding vector of each electrolytic cell state parameter under static conditions, the importance and influence of each electrolytic cell state parameter characteristic in the global state parameter characteristic distribution of the electrolytic cell sequence, as well as the intrinsic correlation between each electrolytic cell, are revealed, thereby obtaining a sequence of static potential energy measurement coefficients of the electrolytic cell state parameters.

[0038] More specifically, the step S312 is expressed as follows: in, Indicates Normalized electrolytic cell state parameter static potential energy measurement coefficient, represents the gated mask function, represents the gating threshold, Indicates The static potential energy weight factor of the electrolytic cell state parameters.

[0039] That is, based on the gating mechanism, the sequence of the obtained electrolytic cell state parameter static potential energy metric coefficients is gated and masked to screen out the individual electrolytic cell state features that contribute more to the overall operation state of the electrolytic cell, while suppressing the individual electrolytic cell state features that contribute less, and generating corresponding weight factors. In this way, the weights of the individual electrolytic cell state parameter features can be dynamically adjusted, so that the model can pay more attention to those feature representations that have a greater impact on the overall operation state.

[0040] More specifically, the step S313 is expressed by the formula: in, The pseudo-anchored aggregation center representation vector represents the state parameters of the electrolyzer sequence.

[0041] That is, based on the obtained weight factor, the sequence of the electrolytic cell state parameter embedded coding vector is weightedly aggregated to reveal the characteristic distribution trend center of the electrolytic cell sequence operating state, and generate a pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameters, thereby providing effective guidance for the subsequent feature aggregation process.

[0042] Specifically, the step S32 calculates the aggregate movement direction of each electrolytic cell state parameter embedded coding vector in the sequence of the electrolytic cell state parameter embedded coding vector relative to the electrolytic cell sequence state parameter pseudo-anchored aggregation center representation vector to obtain a sequence of electrolytic cell state parameter aggregate movement directions. The electrolytic cell state parameter aggregate movement direction is the arc cosine function value between the electrolytic cell state parameter embedded coding vector and the electrolytic cell sequence state parameter pseudo-anchored aggregation center representation vector, which is expressed by the formula: in, represents the magnitude of a vector, is the arccosine function, Indicates the The state parameter embedding coding vector of each electrolytic cell represents the aggregate movement direction of the vector of the pseudo-anchor aggregation center relative to the state parameter of the electrolytic cell sequence.

[0043] That is, by calculating the aggregation movement direction of the embedded coding vector of each electrolytic cell state parameter relative to the pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameter, the change trend of each electrolytic cell state feature relative to the center point is quantified, revealing the dynamic relationship and evolution path of each electrolytic cell state parameter feature in the global feature space, so as to identify the relative relationship between features and group behavior patterns.

[0044] Specifically, the step S33 is expressed by the formula: in, represents the sine function, and denote the weight matrix and bias term respectively, A significant aggregation encoding vector representing the state parameters of the electrolyzer sequence.

[0045] That is, based on the aggregation movement direction and characteristic static potential energy measurement coefficient of each electrolytic cell state parameter embedded coding vector, each electrolytic cell state parameter embedded coding vector is dynamically aggregated toward the pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameter to obtain a significant aggregation coding vector of the electrolytic cell sequence state parameter, thereby achieving an accurate description of the overall operating state mode of the electrolytic cell sequence.

[0046] In the above-mentioned method for detecting the insulation fault of the electrolytic cell to the ground based on the voltage phase, the step S4 extracts the electrolytic cell state parameter embedded coding vector of the detected electrolytic cell from the sequence of the electrolytic cell state parameter embedded coding vector as the electrolytic cell state parameter query coding vector. It should be understood that, considering that in a large electrolytic cell sequence, the performance of each electrolytic cell may be different. Therefore, in order to make a more accurate individual evaluation of the electrolytic cell, the present application further extracts the electrolytic cell state parameter embedded coding vector of the detected electrolytic cell from the sequence of the electrolytic cell state parameter embedded coding vector, and uses it as the electrolytic cell state parameter query coding vector, and compares and analyzes it with the overall operating state mode of the electrolytic cell sequence to determine the performance of the detected electrolytic cell relative to the entire sequence. In this way, by taking the overall operating state mode of the electrolytic cell sequence as a benchmark, the influence of factors such as operating condition fluctuations and noise interference on the operating state of a single electrolytic cell can be effectively avoided, thereby improving the accuracy of fault detection.

[0047] In the above-mentioned method for detecting the insulation fault of the electrolytic cell to the ground based on the voltage phase, the step S5 performs semantic offset measurement on the query coding vector of the electrolytic cell state parameter and the significant aggregation coding vector of the electrolytic cell sequence state parameter to determine whether the detected electrolytic cell has an insulation fault to the ground. Figure 5 FIG. 5 is a flowchart of sub-step S5 of the method for detecting an insulation fault of an electrolytic cell to ground based on a voltage phase according to an embodiment of the present application. Figure 5 As shown, the step S5 includes the steps of: S51, calculating the semantic deviation between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector; S52, determining whether the detected electrolytic cell has an insulation fault to the ground based on the comparison between the semantic deviation and a preset threshold.

[0048] Specifically, the step S51 calculates the semantic deviation between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significantly aggregated coding vector. In a specific example of the present application, the semantic deviation is the Euclidean distance value between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significantly aggregated coding vector. It should be understood that the electrolytic cell sequence state parameter significantly aggregated coding vector reveals the group behavior pattern and average level of the electrolytic cell sequence, while the electrolytic cell state parameter query coding vector represents the individual state of the detected electrolytic cell. By calculating the semantic deviation between the two, a direct comparison can be established between the individual level and the group level, and the degree of difference between the detected electrolytic cell and the overall sequence state can be quantified, thereby helping to identify electrolytic cells that deviate from the normal operating mode.

[0049] Here, in the case where the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector respectively represent the semantic significant aggregation coding features of the state parameters of all electrolytic cells and the low-dimensional semantic embedding coding features of the state parameters of a single electrolytic cell, when calculating the semantic deviation between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector, the inconsistency in feature density and feature order between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector will cause the electrolytic cell state parameter query coding vector to have a feature distribution spatial structure difference relative to the electrolytic cell sequence state parameter significant aggregation coding vector, thereby affecting the calculation accuracy of the semantic deviation.

[0050] Therefore, in a preferred example of the present application, calculating the semantic deviation between the electrolytic cell state parameter query encoding vector and the electrolytic cell sequence state parameter significant aggregation encoding vector includes: The electrolytic cell state parameter query coding vector is optimized, and the optimization process includes: The absolute value sum and the square root of the square sum of all eigenvalues ​​of the electrolytic cell state parameter query coding vector are calculated to obtain the first electrolytic cell state parameter query coding space structure value and the second electrolytic cell state parameter query coding space structure value, that is: in, The first eigenvalues, and Respectively represent the first electrolytic cell state parameter query coding space structure value and the second electrolytic cell state parameter query coding space structure value; Calculate the sum of the first electrolytic cell state parameter query coding space structure value and the second electrolytic cell state parameter query coding space structure value as the electrolytic cell state parameter query coding space structure explicit metric value; The characteristic mean of the electrolytic cell state parameter query coding vector is multiplied by the electrolytic cell state parameter query coding spatial structure explicit metric value to obtain the electrolytic cell state parameter query coding statistical field value ,in, Represents the characteristic mean of the electrolytic cell state parameter query coding vector; Subtract one from the electrolytic cell state parameter query code statistical field value and divide it by the electrolytic cell state parameter query code statistical field value to obtain the electrolytic cell state parameter query code partial spatial structure explicit metric value ; Calculate the electrolytic cell state parameter query code vector and use the electrolytic cell state parameter query code partial division space structure explicit metric value as the power function of the exponent , and multiply the electrolytic cell state parameter query code partial spatial structure explicit metric value by point to obtain the electrolytic cell state parameter query code microscopic representation vector ,in, represents dot product; After multiplying the electrolytic cell state parameter query code vector by the electrolytic cell state parameter query code partial spatial structure explicit metric value, an exponential function with a natural constant as the base is calculated to obtain the electrolytic cell state parameter query code macroscopic mapping vector ; After calculating the logarithmic value of the microscopic representation vector of the electrolytic cell state parameter query code with base 2, the logarithmic value is weighted and summed with the macroscopic mapping vector of the electrolytic cell state parameter query code to obtain the optimized electrolytic cell state parameter query code vector ,in, and represents the weighted hyperparameter, Finally, the semantic deviation between the optimized electrolytic cell state parameter query encoding vector and the electrolytic cell sequence state parameter significant aggregation encoding vector is calculated.

[0051] That is, the partial low-order derivatives of the statistical distribution field corresponding to the electrolytic cell state parameter query coding vector are used as non-overlapping macro-feature representation behavior patches of the electrolytic cell state parameter query coding vector, and the different macro-behavior patch organization spaces under the non-isotropic backbone structure of the electrolytic cell state parameter query coding vector are used to strengthen the dynamic sensitivity of the long sequence micro-complex information distribution of the electrolytic cell state parameter query coding vector to the probabilistic macro-representation behavior. In this way, the consistency of the feature distribution space structure of the electrolytic cell state parameter query coding vector relative to the electrolytic cell sequence state parameter significant aggregation coding vector is improved to improve the calculation accuracy of the semantic offset.

[0052] Specifically, the step S52 determines whether the detected electrolytic cell has an insulation fault to the ground based on the comparison between the semantic deviation degree and the preset threshold value. Specifically, if the semantic deviation degree exceeds the preset threshold value, it indicates that there is a significant difference between the operating state of the detected electrolytic cell and the average state of the overall sequence, and it can be considered that the electrolytic cell has an insulation fault to the ground and requires further inspection and maintenance. On the contrary, if the semantic deviation degree is lower than the preset threshold value, it means that the operating state of the detected electrolytic cell is consistent with the average state of the overall sequence and is within the normal operating fluctuation range. Through this method, the insulation condition of the electrolytic cell can be effectively monitored in real time and fault warnings can be issued, thereby ensuring the safety and stability of the electrolytic production process.

[0053] After confirming that there is an insulation fault in the electrolytic cell, it is crucial to take prompt and effective measures. The entire treatment process needs to ensure safety, reduce losses and resume normal production as soon as possible. The following is a detailed description of the specific implementation methods of the measures: First, once it is confirmed that the electrolyzer has an insulation fault to the ground, the emergency shutdown procedure is immediately initiated to cut off the power supply to prevent the current from continuing to leak and avoid possible safety accidents. At the same time, the on-site operators, maintenance team and management are notified in a timely manner to ensure that all relevant parties are aware of the situation and are ready to take further action. To ensure safety, isolation belts or other physical barriers are used to isolate the faulty electrolyzer area from other work areas to prevent unrelated personnel from entering the dangerous area; warning signs and signs are set up in obvious locations around the faulty electrolyzer to remind all personnel to pay attention to safety and stay away from the faulty area.

[0054] Next, while the emergency shutdown is being performed, a professional technician will conduct a preliminary assessment to determine the severity of the fault and the extent of its impact on surrounding equipment. This step helps to develop the next repair plan. During this process, the time and location of the fault, the specific number and number of electrolytic cells involved, and other information are recorded, and all relevant monitoring data (such as cell AC voltage, cell-to-ground AC voltage, phase difference and adjacent cell voltage difference) are saved as the basis for subsequent analysis. This detailed data collection not only provides solutions to current problems, but also provides a basis for future preventive measures. Technicians also need to check the environmental conditions around the electrolytic cells, such as temperature, humidity and other factors, to eliminate the influence of external factors on fault judgment.

[0055] With the initial assessment complete, the next step is the in-depth investigation phase, which aims to gain a more complete understanding of the cause of the failure. This phase includes detailed physical inspections, such as visual inspection, tightness of connections, etc., as well as insulation resistance measurements using specialized instruments (such as megohmmeters) to directly verify the insulation performance of the electrolyzer to ground. In addition, historical data and trend charts should be reviewed to look for long-term patterns or abnormal fluctuations that may lead to failures. Through multi-source data comparison, the data change trends at different time points and locations are compared to identify abnormal fluctuations or deviations from the normal range. If a parameter is found to be unstable for a long time or has frequent abnormal values, it may be an early sign of insulation failure to ground.

[0056] Based on the results of the preliminary assessment and in-depth investigation, develop a detailed maintenance plan, including the required tools, materials, schedule, and list of participants. Ensure that the plan takes into account all potential risk points and formulates corresponding preventive measures. When performing maintenance work according to the established plan, focus on repairing or replacing damaged parts, such as insulating materials, connectors, etc. Strictly abide by safety regulations during the maintenance process to ensure the safety of operators. For complex or large-scale repair tasks, they can be implemented in stages to gradually restore the function of the electrolyzer. After each stage of work is completed, a comprehensive functional test is carried out, including but not limited to re-measuring the insulation resistance, checking whether the electrical connection is firm, etc., to ensure that the performance of the repaired electrolyzer meets the standard requirements and there are no safety hazards. In addition, during the repair process, care should be taken to retain detailed maintenance records for future review and reference.

[0057] After the repair work is completed, a root cause analysis must be conducted to explore the root cause of the failure. Use tools such as failure mode and effects analysis (FMEA) and fishbone diagram to assist in the analysis and find out the problem. Based on the analysis results, specific improvement measures are proposed, such as optimizing the design, strengthening daily maintenance, and updating aging equipment to prevent similar failures from happening again. In response to the problems exposed in this incident, special training is organized for employees to improve the safety awareness and technical level of all employees and ensure that everyone can respond to emergencies correctly. In addition, a regular inspection and maintenance system is established to strengthen the monitoring of key equipment, discover and solve problems in advance, and reduce the probability of failures. Through continuous improvement, the production process and equipment management are continuously optimized to improve overall operational efficiency and safety.

[0058] In addition to short-term repair and improvement measures, it is also necessary to establish a long-term monitoring and prevention mechanism to ensure the stable operation of the electrolyzer system. Introduce advanced monitoring technologies, such as online monitoring systems, to track the key parameters of the electrolyzer in real time and promptly warn of potential problems. Combined with the data analysis platform, use machine learning algorithms to predict the health of equipment and plan maintenance activities in advance. Conduct risk assessments regularly, identify new potential risk points, and formulate corresponding emergency plans. Strengthen employee training to improve the technical capabilities and emergency response capabilities of front-line operators. Through a series of comprehensive measures, build a safer and more reliable production environment and lay a solid foundation for the sustainable development of the enterprise.

[0059] Through the above series of steps, after confirming that there is an insulation fault to the ground in the electrolytic cell, actions can be taken quickly and effectively to ensure personnel safety, reduce economic losses, and provide valuable experience and guidance for future safe production.

[0060] In summary, the voltage phase-based electrolytic cell insulation fault detection method based on the embodiment of the present application is explained, which first loads an AC excitation signal to each electrolytic cell in the electrolytic cell sequence, and collects the state parameters of each electrolytic cell at the same time, and then uses a neural network model based on deep learning to embed the state parameters of each electrolytic cell and perform global dynamic aggregation to capture the overall operating state mode of the electrolytic cell sequence, and then uses the state parameter embedding representation of the detected electrolytic cell as a query feature, based on the semantic offset between it and the overall operating state mode of the electrolytic cell sequence, so as to intelligently identify whether the detected electrolytic cell has an insulation fault to the ground. In this way, accurate judgment of the electrolytic cell insulation fault to the ground can be achieved, and the adaptability to different operating condition fluctuations and noise environments is improved, thereby effectively reducing the risk of misjudgment or missed judgment.

[0061] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0062] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0064] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0065] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for detecting an electrolytic cell insulation fault based on voltage phase, characterized in that: include: Loading an AC excitation signal to each electrolytic cell in the electrolytic cell sequence and determining a state parameter of each electrolytic cell in the electrolytic cell sequence; Vectorizing and encoding the state parameters of each electrolytic cell to obtain a sequence of electrolytic cell state parameter embedded encoding vectors; Performing feature dynamic aggregation based on potential energy distribution on the sequence of electrolytic cell state parameter embedded coding vectors to obtain a significant aggregation coding vector of the electrolytic cell sequence state parameters; Extracting the electrolytic cell state parameter embedded coding vector of the detected electrolytic cell from the sequence of electrolytic cell state parameter embedded coding vectors as the electrolytic cell state parameter query coding vector; A semantic offset measurement is performed on the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector to determine whether the detected electrolytic cell has a ground insulation fault.

2. The method for detecting the insulation fault of an electrolytic cell to the ground based on voltage phase according to claim 1, characterized in that: The state parameters include the cell AC voltage of each electrolytic cell, the cell-to-ground AC voltage of each electrolytic cell, the phase difference between the cell AC voltage and the cell-to-ground AC voltage of each electrolytic cell, and the AC voltage difference between the cell AC voltage of the electrolytic cell and the cell AC voltage of the next electrolytic cell.

3. The method for detecting an electrolytic cell insulation fault based on voltage phase according to claim 2, characterized in that: The sequence of the electrolytic cell state parameter embedded coding vector is subjected to feature dynamic aggregation based on potential energy distribution to obtain a significant aggregation coding vector of the electrolytic cell sequence state parameter, including: Calculating a pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameters based on a characteristic static potential energy distribution of the sequence of the electrolytic cell state parameter embedding coding vectors; Calculating the aggregate movement direction of each electrolytic cell state parameter embedded coding vector in the sequence of electrolytic cell state parameter embedded coding vectors relative to the electrolytic cell sequence state parameter pseudo-anchored aggregate center representation vector to obtain a sequence of electrolytic cell state parameter aggregate movement directions; Based on the sequence of the electrolytic cell state parameter aggregation movement directions, the sequence of the electrolytic cell state parameter embedded coding vectors is dynamically aggregated toward the electrolytic cell sequence state parameter pseudo-anchor aggregation center representation vector to obtain the electrolytic cell sequence state parameter significant aggregation coding vector.

4. The method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to claim 3, characterized in that: Based on the characteristic static potential energy distribution of the sequence of the electrolytic cell state parameter embedded coding vector, a pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameter is calculated, including: Inputting each electrolytic cell state parameter embedding coding vector in the sequence of electrolytic cell state parameter embedding coding vectors into a characteristic static potential energy measurement network to obtain a sequence of electrolytic cell state parameter static potential energy measurement coefficients; Inputting the sequence of electrolytic cell state parameter static potential energy measurement coefficients into a characteristic energy level screening gating unit to obtain a sequence of electrolytic cell state parameter static potential energy weight factors; Based on the sequence of static potential energy weight factors of the electrolytic cell state parameters, a weighted sum of the sequence of embedded coding vectors of the electrolytic cell state parameters is calculated to obtain a pseudo-anchored aggregation center representation vector of the electrolytic cell sequence state parameters.

5. The method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to claim 4, characterized in that: Inputting each electrolytic cell state parameter embedding coding vector in the sequence of electrolytic cell state parameter embedding coding vectors into the characteristic static potential energy measurement network to obtain a sequence of electrolytic cell state parameter static potential energy measurement coefficients, including: Performing Z-score normalization on the electrolytic cell state parameter embedded coding vector to obtain a normalized electrolytic cell state parameter embedded coding vector; The sum of the cubes of the eigenvalues ​​in the standardized electrolytic cell state parameter embedded coding vector is calculated and divided by the characteristic scale value of the standardized electrolytic cell state parameter embedded coding vector to obtain the electrolytic cell state parameter static potential energy measurement coefficient.

6. The method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to claim 5, characterized in that: The aggregation movement direction of the electrolytic cell state parameter is the arc cosine function value between the electrolytic cell state parameter embedded coding vector and the electrolytic cell sequence state parameter pseudo-anchor aggregation center representation vector.

7. The method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to claim 6, characterized in that: The method of performing semantic offset measurement on the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector to determine whether the detected electrolytic cell has a ground insulation fault comprises: Calculating the semantic deviation between the electrolytic cell state parameter query coding vector and the electrolytic cell sequence state parameter significant aggregation coding vector; Based on the comparison between the semantic deviation and a preset threshold, it is determined whether the detected electrolytic cell has an insulation fault to the ground.

8. The method for detecting insulation fault of an electrolytic cell to ground based on voltage phase according to claim 7, characterized in that: The semantic deviation is the Euclidean distance value between the electrolytic cell state parameter query encoding vector and the electrolytic cell sequence state parameter significant aggregation encoding vector.

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