Normalized operation and maintenance method and system for power grid data processing information

Abstract

The invention discloses a power grid data processing information normalization operation and maintenance method and system, and relates to the technical field of power system informationization, and the method comprises the steps: collecting analog electric signal data through a sensor disposed on a power grid node, and converting the analog electric signal data into digital signal data through a 12-bit A / D converter disposed in a data collection model; sending the collected digital signal data to a data processing center by using a TCP / IP protocol and quantum key distribution; monitoring power grid information state data in the cloud storage in real time by using a state monitoring model; constructing an intelligent operation and maintenance model to analyze and evaluate the cloud-stored power grid information state data; and making an operation and maintenance decision according to an analysis and evaluation result, and executing an operation and maintenance plan and optimization. Through high-precision data acquisition, safe data transmission, efficient preprocessing and storage, real-time state monitoring and intelligent operation and maintenance model construction, normalized operation and maintenance of power grid data processing information are realized.

Description

Technical Field

[0001] The present invention relates to the field of power system information technology, and in particular to a method and system for normalized operation and maintenance of power grid data processing information. Background Art

[0002] With the rapid development of power systems and the continuous improvement of their intelligence level, the normalized operation and maintenance of power grid data processing information has become a key link in ensuring the safe and stable operation of power systems. Traditional power grid operation and maintenance methods mainly rely on manual inspections and post-processing, which is not only inefficient, but also difficult to detect potential problems in a timely manner, resulting in a long fault response time and increasing the operating costs of power systems. In recent years, with the application of advanced technologies such as the Internet of Things, big data analysis, and artificial intelligence, the normalized operation and maintenance technology of power grid data processing information has developed rapidly. The application of these technologies has made power grid operation and maintenance more intelligent and automated, which can effectively improve operation and maintenance efficiency, reduce operation and maintenance costs, and significantly improve the reliability of power systems.

[0003] However, the existing power grid data processing information normalization operation and maintenance technology still has some shortcomings. First, traditional data acquisition equipment often has low accuracy and lacks effective data preprocessing methods, resulting in uneven quality of collected data, which is difficult to meet the needs of refined operation and maintenance. Secondly, during the data transmission process, due to the lack of a high-level encryption mechanism, the security and integrity of the data cannot be effectively guaranteed. In addition, although there are cloud computing-based storage solutions, there are still challenges in the real-time processing and analysis of massive data. Finally, the existing intelligent operation and maintenance models are often too simple to accurately evaluate the health status of equipment, nor can they effectively predict the time and location of failures, which limits the effectiveness and predictability of operation and maintenance decisions. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for normalized operation and maintenance of power grid data processing information to solve the problems of low data collection accuracy, insufficient data transmission security, limited data processing capability and inaccurate intelligent operation and maintenance model in the existing method for normalized operation and maintenance of power grid data processing information.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for normalized operation and maintenance of power grid data processing information, which includes collecting analog electrical signal data through sensors deployed on power grid nodes, and using a 12-bit A / D converter built into a data acquisition model to convert the analog electrical signal data into digital signal data; using the TCP / IP protocol and quantum key distribution to send the collected digital signal data to a data processing center; preprocessing the digital signal data sent to the data processing center, outputting it as power grid information status data, and storing the power grid information status data in the cloud; using a status monitoring model to monitor the power grid information status data in the cloud storage in real time; constructing an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud; making operation and maintenance decisions based on the results of the analysis and evaluation, and executing operation and maintenance plans and optimizations.

[0008] As a preferred solution of the normalized operation and maintenance method of power grid data processing information described in the present invention, wherein: the analog electrical signal data is collected by sensors deployed on the power grid nodes, and the analog electrical signal data is converted into digital signal data using a 12-bit A / D converter built into the data acquisition model, the specific steps are:

[0009] Use sensors deployed at each node of the power grid to monitor physical quantity data, including current transformers, voltage transformers, temperature sensors, humidity sensors, displacement sensors, vibration sensors, capacitive sensors, photoelectric sensors, gas sensors, and pressure sensors;

[0010] Convert physical quantity data into analog electrical signal data, including current, voltage, temperature, humidity, displacement, vibration, capacitance, light, gas, and pressure;

[0011] The data acquisition module receives the electrical signal data from the sensor and converts the analog electrical signal data into digital signal data through a built-in 12-bit A / D converter.

[0012] As a preferred solution of the normalized operation and maintenance method of power grid data processing information of the present invention, wherein: the collected digital signal data is sent to the data processing center by using the TCP / IP protocol and quantum key distribution, and the specific steps are as follows:

[0013] The converted digital signal data is transmitted to the data processing center via TCP / IP protocol;

[0014] Quantum key distribution is used during the transmission process to establish a shared key between Alice and Bob in the BB84 protocol;

[0015] Alice selects a series of light particles as the initial polarization states, including a right-angle polarization basis and a diagonal polarization basis;

[0016] Alice sends light particles to Bob through the quantum channel. Bob randomly selects the right-angle polarization basis and the diagonal polarization basis as the measurement basis to measure each received light particle.

[0017] Alice and Bob publicly disclose their chosen right-angle polarization basis and diagonal polarization basis through classical channels;

[0018] Alice and Bob retain the measurement results of the light particles using the same measurement basis;

[0019] Alice and Bob publicly share part of their common results to check for errors. If the error rate is below a preset threshold, they continue; otherwise, they give up and start over.

[0020] The classical channel in quantum key distribution is used to perform error correction and privacy amplification to shorten the key length, and finally obtain the key for encrypting and decrypting data.

[0021] As a preferred solution of the normalized operation and maintenance method of power grid data processing information of the present invention, the digital signal data sent to the data processing center is pre-processed and output as power grid information status data, and the power grid information status data is stored in the cloud. The specific steps are:

[0022] Perform pre-processing operations on the transmitted digital signal data, including cleaning, repairing, and standardization, and output the data as power grid information status data reflecting the power grid engineering equipment;

[0023] Use cloud storage services to store power grid information status data;

[0024] Install database management software to manage and retrieve power grid information status data.

[0025] As a preferred solution of the normalized operation and maintenance method of power grid data processing information of the present invention, wherein: the state monitoring model is used to monitor the power grid information state data in the cloud storage in real time, and the specific steps are as follows:

[0026] The status monitoring model is used to display the real-time monitoring cloud storage through maps and matrices to reflect the grid information status data of power grid engineering equipment, including installed capacity, historical alarm information, safe operation time, characteristic curves and video monitoring information.

[0027] As a preferred solution of the normalized operation and maintenance method of power grid data processing information of the present invention, wherein: the intelligent operation and maintenance model is constructed to analyze and evaluate the power grid information status data stored in the cloud, and the specific steps are:

[0028] Construct a comprehensive evaluation model to evaluate the health status of cloud storage data reflecting the power grid information status of power grid engineering equipment;

[0029] Use decision tree algorithm to divide warning levels;

[0030] Utilize deep learning algorithms to build a fault pattern recognition model.

[0031] As a preferred solution of the normalized operation and maintenance method of power grid data processing information of the present invention, wherein: the operation and maintenance decision is made according to the results of analysis and evaluation, and the operation and maintenance plan and optimization are executed, the specific steps are:

[0032] For abnormal transformer temperature, take alarm measures, notify the maintenance team to check immediately, and plan a thorough overhaul to replace potential aging parts;

[0033] In response to a surge in regional electricity demand, spare generators are deployed from low-load areas to increase the number of substations in the region;

[0034] For circuit breaker failures, shorten the maintenance cycle from one year to six months, evaluate and replace with more reliable brands and models;

[0035] For potential risks in the substation control software, software updates are carried out during off-peak hours, and a comprehensive software upgrade plan is made regularly;

[0036] To prepare for severe weather, key lines and facilities should be reinforced in advance, and infrastructure construction in areas vulnerable to weather should be strengthened.

[0037] In the second aspect, the present invention provides a normalized operation and maintenance system for power grid data processing information, including an acquisition module, a transmission module, a preprocessing storage module, a monitoring module, an intelligent operation and maintenance module, and a decision module; the acquisition module is used to collect analog electrical signal data through sensors deployed on power grid nodes, and use the 12-bit A / D converter built into the data acquisition model to convert the analog electrical signal data into digital signal data; the transmission module is used to send the collected digital signal data to a data processing center using the TCP / IP protocol and quantum key distribution; the preprocessing storage module is used to preprocess the digital signal data sent to the data processing center, output it as power grid information status data, and store the power grid information status data in the cloud; the monitoring module is used to monitor the power grid information status data in the cloud storage in real time using a status monitoring model; the intelligent operation and maintenance module is used to construct an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud; the decision module is used to make operation and maintenance decisions based on the results of the analysis and evaluation, and execute operation and maintenance plans and optimizations.

[0038] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for normalized operation and maintenance of power grid data processing information as described in the first aspect of the present invention is implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for normalized operation and maintenance of power grid data processing information as described in the first aspect of the present invention.

[0040] The beneficial effects of the present invention are as follows: the present invention realizes the normalized operation and maintenance of power grid data processing information through high-precision data acquisition, secure data transmission, efficient preprocessing and storage, real-time status monitoring and intelligent operation and maintenance model construction. Specifically, the accuracy and real-time performance of data acquisition are improved, the security of data transmission is enhanced, the data processing process is simplified and the storage efficiency is improved, the timeliness of fault detection is enhanced, and the scientific nature and accuracy of operation and maintenance decisions are improved, thereby reducing operation and maintenance costs and improving the stability and reliability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 This is a flow chart of the normalized operation and maintenance method for power grid data processing information in Example 1.

[0043] Figure 2 This is a flow chart of cloud digital storage in Example 1. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for normalized operation and maintenance of power grid data processing information, comprising the following steps:

[0046] S1. Collect analog electrical signal data through sensors deployed on grid nodes, and convert the analog electrical signal data into digital signal data using the built-in 12-bit A / D converter in the data acquisition model.

[0047] Furthermore, sensors deployed at each node of the power grid are used to monitor physical quantity data, including current transformers, voltage transformers, temperature sensors, humidity sensors, displacement sensors, vibration sensors, capacitive sensors, photoelectric sensors, gas sensors, and pressure sensors;

[0048] Convert physical quantity data into analog electrical signal data, including current, voltage, temperature, humidity, displacement, vibration, capacitance, light, gas, and pressure;

[0049] The data acquisition module receives the electrical signal data from the sensor and converts the analog electrical signal data into digital signal data through a built-in 12-bit A / D converter.

[0050] It should be noted that the current transformer converts a large current into a smaller current through the principle of electromagnetic induction, generally into a standard AC current signal (such as 5A or 1A); this small current can be converted into a voltage signal through a resistor;

[0051] The voltage transformer converts high voltage into a standard low voltage signal (such as 100V), which can also be converted into a voltage signal through a resistor;

[0052] Temperature sensors, such as thermocouples or thermistors, change their resistance as temperature changes, and this change can be converted into a voltage signal through a circuit;

[0053] The humidity sensor measures the water molecule content in the air and converts the humidity change into a voltage signal;

[0054] Displacement sensors, like capacitive or inductive sensors, generate voltage signals by causing changes in capacitance or inductance due to changes in the position of an object;

[0055] Vibration sensors, like piezoelectric sensors, when subjected to vibration produce a voltage signal proportional to the intensity of the vibration;

[0056] Capacitive sensors detect changes in capacitance and convert them into voltage signals;

[0057] Photoelectric sensors generate voltage signals through changes in light;

[0058] Gas sensors generate voltage signals by detecting the presence and concentration of specific gases;

[0059] The pressure sensor generates a voltage signal by detecting changes in pressure.

[0060] It should be noted that the features and advantages of a 12-bit A / D converter are:

[0061] A 12-bit converter can distinguish 4096 different levels of the input signal;

[0062] The sampling rate depends on the specific application scenario and the model of the converter, usually ranging from thousands to millions of samples per second;

[0063] High resolution means better accuracy and can capture smaller signal changes;

[0064] Good linearity ensures that the conversion results are consistent over the entire input range;

[0065] Good temperature drift and time stability, able to maintain accurate conversion over a long period of time;

[0066] Low power design helps to extend battery life or reduce system power consumption.

[0067] It should be noted that by deploying various types of sensors at each node of the power grid to monitor physical quantity data, converting these physical quantities into analog electrical signal data, and then using the data acquisition model with a built-in 12-bit A / D converter to convert the analog signal into digital signal data, this series of steps realizes real-time monitoring and digitization of the power grid operation status; this approach allows analog signals that were originally difficult to process directly to be converted into digital signals that are easy for computers to process and analyze, and then enables efficient remote monitoring, data analysis and fault diagnosis, providing basic data support for the intelligent operation and maintenance of the power grid. In short, the main purpose of this step is to convert signals in the physical world into a form that can be processed by modern information technology, so as to realize the automated management and intelligent operation and maintenance of the power grid.

[0068] S2. Use TCP / IP protocol and quantum key distribution to send the collected digital signal data to the data processing center.

[0069] Furthermore, the converted digital signal data is transmitted to a data processing center via TCP / IP protocol;

[0070] Quantum key distribution is used during the transmission process to ensure the security of data transmission;

[0071] Alice and Bob establish a shared key through the BB84 protocol;

[0072] Alice selects a series of light particles as the initial polarization states, including a right-angle polarization basis and a diagonal polarization basis;

[0073] Alice sends light particles to Bob through the quantum channel. Bob randomly selects the right-angle polarization basis and the diagonal polarization basis as the measurement basis to measure each received light particle.

[0074] Alice and Bob publicly disclose their chosen right-angle polarization basis and diagonal polarization basis through classical channels;

[0075] Alice and Bob retain the measurement results of the light particles using the same measurement basis;

[0076] Alice and Bob publicly share part of their common results to check for errors. If the error rate is below a preset threshold, they continue; otherwise, they give up and start over.

[0077] Using classical channels to perform error correction, ensuring that Alice and Bob have identical keys;

[0078] Perform privacy amplification to reduce key length, thereby reducing the amount of information available to an eavesdropper;

[0079] The resulting key is used to encrypt and decrypt data.

[0080] It should be noted that the specific steps of the BB84 protocol are as follows:

[0081] Alice chooses two bases: a rectangular basis and a diagonal basis, and randomly selects a basis and a polarization state for each photon;

[0082] Bob also randomly chooses a rectangular basis or a diagonal basis to measure the polarization state of each photon;

[0083] Through a classical channel (such as the Internet), Alice and Bob announce their respective basis choices;

[0084] Alice and Bob compare their choices of basis and keep only those measurements that used the same basis.

[0085] Alice and Bob randomly select a portion of the common results for public comparison to check for errors;

[0086] If the error rate exceeds a preset threshold, it indicates that there may be an eavesdropper. At this time, Alice and Bob will abandon the current key and start over;

[0087] Use classical channels for error correction to ensure that Alice and Bob have the same key.

[0088] By reducing the key length through privacy amplification, the amount of information available to eavesdroppers is reduced, ensuring the security of the key.

[0089] It should be noted that the process of randomly selecting the measurement basis is:

[0090] Alice and Bob each independently generate a series of random numbers; these random numbers are usually drawn from a binary distribution (e.g., 0 or 1);

[0091] If the random number is 0, it corresponds to the right-angle polarization basis; if the random number is 1, it corresponds to the diagonal polarization basis;

[0092] Based on the generated random number sequence, Alice and Bob each decide which measurement basis to use for measurement;

[0093] If the random number is 0, Alice and Bob will choose the right-angle polarization basis for measurement; if it is 1, they will choose the diagonal polarization basis.

[0094] It should be noted that for the random selection of hardware generators, random numbers can be generated by physical methods (such as random number generators based on quantum effects) or software methods (such as pseudo-random number generators); physical methods are generally considered to be more secure because they rely on true physical random processes;

[0095] For random selection by software generators, in real applications, if a computer program is used to generate random numbers, a pseudo-random number generator (PRNG) is usually used; although these are not truly random numbers, they are sufficient for many applications.

[0096] It should be noted that in the BB84 protocol, Alice and Bob disclose their respective measurement bases through the classical channel, rather than the specific measurement results. Specifically, Alice will tell Bob which light particles are measured with the right-angle polarization base and which are measured with the diagonal polarization base; similarly, Bob will also disclose the measurement base he uses; they only retain the measurement results of those light particles that both parties use the same measurement base.

[0097] It should be noted that in the BB84 protocol, error correction is used to ensure that the keys generated by Alice and Bob are the same. This is because when quantum states are transmitted in a quantum channel, errors may be introduced, resulting in inconsistent measurement results between Alice and Bob. Through the error correction protocol, they can find and correct these inconsistencies so that the keys held by both parties become consistent; this process usually involves exchanging information over a classical channel, but does not directly expose any part of the key;

[0098] Privacy amplification is to further improve the security of the key, ensuring that even if an eavesdropper monitors the quantum channel, he cannot obtain enough information to reconstruct the entire key; this step is achieved by reducing the length of the key while ensuring that the keys between Alice and Bob remain consistent; generally, privacy amplification is achieved using a mathematical operation called a "hash function", which maps the original key into a shorter new key.

[0099] For example: Assume that Alice and Bob have obtained a consistent key sequence through error correction. Alice's key is 101010101010, and Bob's key is 101010101010.

[0100] Next, the privacy of Alice and Bob’s keys is amplified;

[0101] Assume that Alice and Bob choose SHA-256 as the hash function and decide to use only the first 8 bits of the key, which is 10101010;

[0102] Calculate the SHA-256 hash value of each part of the key; assume that the resulting hash value is a part of 0123456789abcdef (in fact, SHA-256 will produce a longer hash value);

[0103] Finally, Alice and Bob use this shorter hash value as the final key to encrypt and decrypt data, and the security of the key is also guaranteed.

[0104] It should be noted that Alice and Bob only retain the measurement results of those light particles that they both use the same measurement basis; this means that when they disclose their respective measurement bases, they will compare these bases and only retain the measurement results of those light particles that they measured using the same basis.

[0105] It should be noted that Alice and Bob will disclose part of their common results to check whether there is an eavesdropper. They do not disclose all the measurement results, but randomly select a part of the common results for comparison to detect whether there is third-party interference; if the error rate is higher than the preset threshold, it is considered that there is an eavesdropper, and the current key generation process needs to be abandoned and restarted.

[0106] It should be noted that by using the TCP / IP protocol to transmit data and combining the quantum key distribution (QKD) method, especially the BB84 protocol, the digital signal data collected by the power grid nodes can be securely sent to the data processing center; this process first transmits the digital signal data through the TCP / IP protocol, and then uses the principles of quantum mechanics to ensure the security of data transmission. The BB84 protocol ensures that Alice and Bob can share a key known only to both parties through a series of interactions between them, and even if there is a potential eavesdropper, its presence can be detected and corresponding measures can be taken; the purpose of this is to protect sensitive data from unauthorized access and ensure the confidentiality and integrity of communications.

[0107] S3. Pre-process the digital signal data sent to the data processing center, output it as power grid information status data, and store the power grid information status data in the cloud.

[0108] Furthermore, the transmitted digital signal data is pre-processed, including cleaning, repairing, and standardizing, and output as power grid information status data reflecting the power grid engineering equipment;

[0109] The power grid information status data reflects the real-time status information of power grid engineering equipment;

[0110] Use cloud storage services to store power grid information status data;

[0111] Install database management software to manage and retrieve power grid information status data.

[0112] It should be noted that the advantages of cloud storage are: storage space can be easily expanded as needed; pay-as-you-go, no need to invest in expensive hardware; automatic data backup for easy and quick recovery; multiple copies of storage to avoid single point of failure.

[0113] The selection of database management software and its functions are as follows: you can choose a suitable database management system based on data type, scale, application requirements, etc., such as MySQL, PostgreSQL, MongoDB, etc.; its function is to provide it with data query, indexing, transaction processing, backup and recovery and other functions.

[0114] It should be noted that by pre-processing the digital signal data transmitted to the data processing center, including cleaning, repair, standardization and other operations, it can be converted into power grid information status data reflecting the real-time status of power grid engineering equipment; the accuracy and real-time nature of these data are crucial for power grid monitoring; subsequently, storing these power grid information status data in the cloud can not only achieve efficient data management and rapid retrieval, but also facilitate remote access and analysis; in addition, installing database management software can help further improve data security and maintainability.

[0115] S4. Use the status monitoring model to monitor the power grid information status data in the cloud storage in real time.

[0116] Furthermore, the status monitoring model is used to display the real-time monitoring cloud storage through maps and matrices to reflect the grid information status data of the power grid engineering equipment, including installed capacity, historical alarm information, safe operation time, characteristic curves and video monitoring information.

[0117] It should be noted that geographic information system GIS software or online map services, such as Google Maps API, are used to display data related to geographic location;

[0118] Use Excel, Tableau, or other visualization tools to present numerical data in a table or heat map.

[0119] It should be noted that status data reflects the maximum power supply capacity of the power grid, measures the reliability of equipment, and is closely related to maintenance plans.

[0120] It should be noted that by displaying key indicators such as installed capacity, historical alarm information, safe operation time, characteristic curves and video monitoring information in the form of maps and matrix diagrams, operation and maintenance personnel can intuitively understand the working status of power grid equipment; such real-time monitoring helps to promptly discover potential problems, reduce the occurrence of faults, and improve the stability and safety of the power grid.

[0121] S5. Build an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud.

[0122] Furthermore, a comprehensive evaluation model is constructed to evaluate the health status of the cloud storage reflecting the grid information status data of the grid engineering equipment. The expression is:

[0123]

[0124] H stands for the health index, which indicates the overall health status of the device. Its value range is [0, 1]. A value close to 0 indicates that the device has a serious problem, while a value close to 1 indicates that the device is in good condition. i Represents the health coefficient of the i-th state device, indicating the health of the device, which is usually determined based on factors such as the device's operating status and historical records. i Represents the importance value of the i-th state device, indicating the importance of the device in the overall system. The higher the importance, the greater the impact of the device on the system operation. i is the index of the state device, and N represents the total number of devices.

[0125] The decision tree algorithm is used to divide the warning level, and the expression is:

[0126]

[0127] Where W represents the warning level value, indicating the risk level of the warning information, and the value range is [0, ∞). A lower value indicates a lower risk level, and a higher value indicates a higher risk level. j represents the importance value of the jth warning information, indicating the importance of the warning information among all the warning information. j Represents the frequency value of the jth warning information, indicating the frequency of the warning information, S jrepresents the severity value of the jth warning information, indicating the severity of the consequences that may be caused by the warning information, M is the number of warning information, that is, the total number of warning information, j is the index of the warning information, log2 represents the logarithmic function, emphasizing the importance of warning information with high frequency and low severity, while weakening the importance of warning information with low frequency but high severity;

[0128] A fault mode recognition model is constructed using a deep learning algorithm to predict and diagnose the fault mode of power grid equipment. The expression is:

[0129]

[0130] Among them, P is the predicted probability value, which indicates the probability that the device may fail. The value range is (0, 1). A value close to 0 indicates that the device is normal, and a value close to 1 indicates that the device may fail. k is the kth eigenvector, which represents the feature set used for prediction, which may include various features such as the operating status and historical records of the equipment. α is a model parameter used to adjust the model sensitivity. β is a model parameter used to adjust the model sensitivity. e is the base of the natural logarithm. K is the number of eigenvectors. k is the index of the eigenvector.

[0131] It should be noted that the health status formula is obtained by comprehensively considering the health coefficient C of each device. i and importance V i to evaluate the health of the entire system; this calculation method ensures that the status of key devices has a greater impact on the overall health index, allowing the maintenance team to pay more attention to these key devices; in addition, the value range of the health index H is limited to [0, 1], which makes the health status of different devices or systems directly comparable, and when H is close to 0, it indicates that there are serious problems in the system, prompting the maintenance team to take timely measures.

[0132] The classification formula of the warning level provides a quantitative indicator for measuring the risk level of potential system failures. The logarithmic function log2 is used to calculate the frequency F of the warning information. j and severity S j Dynamically adjust the importance of warnings, so that warning information that needs to be handled first can be effectively identified. The maximum function max(·, 0) ensures that the warning level will not be lower than 0, which avoids the difficulty of interpretation caused by negative values ​​and helps the maintenance team better understand the risk level of warning information.

[0133] The fault mode recognition model formula is based on the characteristic vector X of multiple related factors (such as temperature, current, etc.) k, the model improves the accuracy and reliability of prediction; parameters α and β can adjust the sensitivity of the model to adapt to different application scenarios and needs; in addition, the use of deep learning algorithms can achieve real-time monitoring and prediction, which helps to prevent possible failures in advance, thereby improving the efficiency and reliability of equipment management.

[0134] It should be noted that the process of feature selection is to select features including temperature, voltage, current, operating time, alarm records, etc.; these features are highly correlated with the health status and failure mode of the equipment.

[0135] It should be noted that the long short-term memory network LSTM is selected as the deep learning model because it is suitable for time series data and can capture long-term dependencies.

[0136] It should be noted that building an intelligent operation and maintenance model involves several key steps:

[0137] Collect a large amount of power grid equipment operation data, including normal operation data and fault data;

[0138] Extract meaningful features from raw data, such as temperature, voltage fluctuation, current change, etc.

[0139] Choose a suitable deep learning model, such as convolutional neural network (CNN), recurrent neural network (RNN), or long short-term memory network (LSTM);

[0140] Use a labeled dataset to train the model; the labeled dataset includes data on failure modes and normal modes;

[0141] The goal of model training is to minimize the difference between the predicted results and the actual labels;

[0142] Adjust model hyperparameters such as learning rate and batch size through methods such as cross-validation;

[0143] Use an independent test set to evaluate the performance of the model; key indicators include accuracy, recall, F1 score, etc.

[0144] Deploy the trained model to the production environment for real-time fault pattern recognition;

[0145] Regularly evaluate model performance and update the model based on new data.

[0146] It should be noted that, assuming that an LSTM model is to be trained to identify the failure mode of power grid equipment:

[0147] Normalize the data so that the feature values ​​are in a similar range; divide the dataset into training set, validation set, and test set;

[0148] Build a model that includes an input layer, one or more LSTM layers, and an output layer;

[0149] LSTM layers can capture long-term dependencies in time series data;

[0150] Select the cross entropy loss function and optimizer Adam, set the training rounds and batch size;

[0151] Train the model using the training set and evaluate the model performance using the validation set at the end of each epoch; adjust the hyperparameters based on the performance on the validation set;

[0152] Use the test set to evaluate the generalization ability of the model; calculate the model's accuracy, recall and other indicators;

[0153] Deploy the trained model to the production environment for fault pattern recognition in real-time data streams.

[0154] It should be noted that the health index of the equipment is calculated through a comprehensive evaluation model to reflect the overall health status of the equipment; then the decision tree algorithm is used to determine the classification criteria for the warning level, effectively distinguishing warning information of different risk levels; finally, through deep learning models, especially long short-term memory networks, the failure modes of power grid equipment are accurately predicted; the construction and training of these models involve multiple key steps such as data preparation, feature engineering, model selection and training, which ensure the accuracy and practicality of the model.

[0155] S6. Make operation and maintenance decisions based on the results of analysis and evaluation, and implement operation and maintenance plans and optimization.

[0156] Furthermore, operational and maintenance decision suggestions are made based on the results of the analysis and evaluation;

[0157] For abnormal transformer temperature, take alarm measures and notify the maintenance team to immediately check whether the transformer cooling system is working properly, and plan to conduct a thorough overhaul and replace potential aging parts;

[0158] In case of a surge in regional electricity demand, spare generators will be deployed from low-load areas to support the increase in the number of substations in the region to cope with future demand growth;

[0159] For circuit breaker failures, shorten the maintenance cycle from one year to six months, evaluate and replace with more reliable brands and models;

[0160] For potential risks in the substation control software, software updates are carried out during off-peak hours to reduce the impact on power supply. A comprehensive software upgrade plan is made regularly to ensure that the software of all key systems is the latest version;

[0161] To prepare for severe weather, key lines and facilities should be reinforced in advance, emergency response teams should be prepared, and infrastructure construction should be strengthened in areas prone to weather.

[0162] It should be noted that the need for action is determined based on outputs such as health indices and warning levels;

[0163] The execution process of the operation and maintenance plan is: identify responsible persons; arrange required resources; monitor progress and ensure completion on time.

[0164] The effectiveness evaluation of optimization measures is as follows: evaluating performance changes before and after implementation; observing the long-term effects of optimization measures.

[0165] It should be noted that by making operation and maintenance decisions based on the results of intelligent operation and maintenance model analysis and evaluation, and executing corresponding operation and maintenance plans and optimization measures, effective management and maintenance of power grid equipment can be achieved; this process includes specific operation and maintenance decisions on abnormal transformer temperature, surge in regional power demand, circuit breaker failure, hidden dangers in substation control software, and severe weather prevention; these decisions can help the operation and maintenance team respond to equipment abnormalities in a timely manner, optimize resource allocation, improve equipment reliability and efficiency, thereby reducing the occurrence of failures and ensuring the stable operation of the power grid.

[0166] The present embodiment also provides a normalized operation and maintenance system for power grid data processing information, including: an acquisition module, a transmission module, a preprocessing storage module, a monitoring module, an intelligent operation and maintenance module, and a decision module; the acquisition module is used to collect analog electrical signal data through sensors deployed on power grid nodes, and use the built-in 12-bit A / D converter in the data acquisition model to convert the analog electrical signal data into digital signal data; the transmission module is used to send the collected digital signal data to the data processing center using the TCP / IP protocol and quantum key distribution; the preprocessing storage module is used to preprocess the digital signal data sent to the data processing center, output it as power grid information status data, and store the power grid information status data in the cloud; the monitoring module is used to use the status monitoring model to monitor the power grid information status data in the cloud storage in real time; the intelligent operation and maintenance module is used to build an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud; the decision module is used to make operation and maintenance decisions based on the results of the analysis and evaluation, and execute operation and maintenance plans and optimizations.

[0167] This embodiment also provides a computer device, which is suitable for the case of a normalized operation and maintenance method for power grid data processing information, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the normalized operation and maintenance method for power grid data processing information proposed in the above embodiment.

[0168] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0169] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing normalized operation and maintenance of power grid data processing information as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0170] In summary, the present invention realizes the normalized operation and maintenance of power grid data processing information through high-precision data acquisition, secure data transmission, efficient preprocessing and storage, real-time status monitoring and intelligent operation and maintenance model construction. Specifically, it improves the accuracy and real-time performance of data acquisition, enhances the security of data transmission, simplifies the data processing process and improves storage efficiency, strengthens the timeliness of fault detection, and improves the scientificity and accuracy of operation and maintenance decisions, thereby reducing operation and maintenance costs and improving the stability and reliability of power grid operation.

[0171] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of the normalized operation and maintenance method of power grid data processing information are provided.

[0172] A new smart grid monitoring system was tested and evaluated in detail to demonstrate its significant advantages over traditional grid monitoring methods. The system uses key technologies such as advanced 12-bit A / D converters, quantum key distribution technology, and deep learning algorithms to improve the accuracy and efficiency of grid monitoring.

[0173] A typical power grid node was selected as the test object, which includes a main transformer, several circuit breakers, voltage transformers, current transformers and other auxiliary equipment; the test environment is a medium-sized power grid, which includes various types of loads and power supplies to simulate the real power grid environment; the performance of this system in power grid monitoring is evaluated, especially in terms of data collection accuracy, data transmission security, and the effectiveness of the intelligent operation and maintenance model.

[0174] First, various sensors were deployed on the test nodes, including current transformers, voltage transformers, temperature sensors, etc. These sensors converted analog signals into digital signals through 12-bit A / D converters. During the test, the quality of the signal before and after the conversion was recorded to evaluate the performance of the A / D converter. The digital signal data was sent to the data processing center using the TCP / IP protocol, and the BB84 quantum key distribution protocol was used to ensure the security of data transmission. During the test, the quantum key was cracked through simulated attacks to evaluate the security of the key.

[0175] Secondly, the received data is preprocessed in the data processing center, including data cleaning, repair and standardization; the preprocessed data is converted into power grid information status data and stored in the cloud; the MySQL database management system is used to manage this data; the status monitoring model is used to monitor the power grid information status data in the cloud storage in real time, and key indicators such as installed capacity and historical alarm information are displayed through maps and matrix diagrams.

[0176] Finally, a comprehensive evaluation model was constructed to evaluate the health index of the equipment, and a decision tree algorithm was used to divide the warning level. In addition, a LSTM-based fault pattern recognition model was constructed to predict the failure probability of the equipment. According to the output results of the intelligent operation and maintenance model, corresponding operation and maintenance decisions and optimization measures were formulated.

[0177] The details are shown in the following table:

[0178] Table 1 Experimental comparison of power grid equipment status monitoring and health assessment data

[0179]

[0180]

[0181] The 12-bit A / D converter showed excellent performance and was able to accurately convert analog signals into digital signals. For example, the temperature of transformer A remained accurate after being converted from 45°C. The quantum key distribution method withstood the test of analog attacks during the data transmission stage, proving its effectiveness in protecting data transmission security. Compared with traditional methods, these advanced technologies have significantly improved data accuracy and security.

[0182] The intelligent operation and maintenance model constructed can accurately assess the health status of equipment and effectively predict the probability of failure. For example, the health index of transformer C is only 0.78, indicating that further inspection and maintenance are needed; based on the output results of the model, more accurate operation and maintenance decisions can be made, such as timely maintenance of transformer C to avoid failures; these innovative methods not only improve the efficiency of power grid operation, but also reduce the occurrence of failures, thereby significantly enhancing the stability and reliability of the power grid.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for normalized operation and maintenance of power grid data processing information, characterized in that: include, The analog electrical signal data is collected by sensors deployed on the grid nodes and converted into digital signal data using the 12-bit A / D converter built into the data acquisition model; Using TCP / IP protocol and quantum key distribution, the collected digital signal data is sent to the data processing center; Pre-process the digital signal data sent to the data processing center, output it as power grid information status data, and store the power grid information status data in the cloud; Use the status monitoring model to monitor the power grid information status data in cloud storage in real time; Build an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud; Make operation and maintenance decisions based on the results of analysis and evaluation, and implement operation and maintenance plans and optimizations.

2. The method for normalizing operation and maintenance of power grid data processing information according to claim 1, characterized in that: The analog electrical signal data is collected by sensors deployed on the grid nodes, and the analog electrical signal data is converted into digital signal data using a 12-bit A / D converter built into the data acquisition model. The specific steps are: Use sensors deployed at each node of the power grid to monitor physical quantity data, including current transformers, voltage transformers, temperature sensors, humidity sensors, displacement sensors, vibration sensors, capacitive sensors, photoelectric sensors, gas sensors, and pressure sensors; Convert physical quantity data into analog electrical signal data, including current, voltage, temperature, humidity, displacement, vibration, capacitance, light, gas, and pressure; The data acquisition module receives the electrical signal data from the sensor and converts the analog electrical signal data into digital signal data through a built-in 12-bit A / D converter.

3. The method for normalized operation and maintenance of power grid data processing information according to claim 2, characterized in that: The method uses TCP / IP protocol and quantum key distribution to send the collected digital signal data to the data processing center. The specific steps are: The converted digital signal data is transmitted to the data processing center via TCP / IP protocol; Quantum key distribution is used during the transmission process to establish a shared key between Alice and Bob in the BB84 protocol; Alice selects a series of light particles as the initial polarization states, including a right-angle polarization basis and a diagonal polarization basis; Alice sends light particles to Bob through the quantum channel. Bob randomly selects the right-angle polarization basis and the diagonal polarization basis as the measurement basis to measure each received light particle. Alice and Bob publicly disclose their chosen right-angle polarization basis and diagonal polarization basis through classical channels; Alice and Bob retain the measurement results of the light particles using the same measurement basis; Alice and Bob publicly share part of their common results to check for errors. If the error rate is below a preset threshold, they continue; otherwise, they give up and start over. The classical channel in quantum key distribution is used to perform error correction and privacy amplification to shorten the key length, and finally obtain the key for encrypting and decrypting data.

4. The method for normalized operation and maintenance of power grid data processing information according to claim 3, characterized in that: The digital signal data sent to the data processing center is pre-processed, output as power grid information status data, and the power grid information status data is stored in the cloud. The specific steps are: Perform pre-processing operations on the transmitted digital signal data, including cleaning, repairing, and standardization, and output the data as power grid information status data reflecting the power grid engineering equipment; Use cloud storage services to store power grid information status data; Install database management software to manage and retrieve power grid information status data.

5. The method for normalized operation and maintenance of power grid data processing information according to claim 4, characterized in that: The specific steps of using the state monitoring model to monitor the power grid information state data in the cloud storage in real time are as follows: The status monitoring model is used to display the real-time monitoring cloud storage through maps and matrices to reflect the grid information status data of power grid engineering equipment, including installed capacity, historical alarm information, safe operation time, characteristic curves and video monitoring information.

6. The method for normalized operation and maintenance of power grid data processing information according to claim 5, characterized in that: The intelligent operation and maintenance model is constructed to analyze and evaluate the power grid information status data stored in the cloud. The specific steps are: Construct a comprehensive evaluation model to assess the health status of cloud storage data reflecting the grid information status of grid engineering equipment; Use decision tree algorithm to divide warning levels; Utilize deep learning algorithms to build a fault pattern recognition model.

7. The method for normalized operation and maintenance of power grid data processing information according to claim 6, characterized in that: The operation and maintenance decisions are made based on the results of the analysis and evaluation, and the operation and maintenance plans and optimizations are implemented. The specific steps are: For abnormal transformer temperature, take alarm measures, notify the maintenance team to check immediately, and plan a thorough overhaul to replace potential aging parts; In response to a surge in regional electricity demand, spare generators are deployed from low-load areas to increase the number of substations in the region; For circuit breaker failures, shorten the maintenance cycle from one year to six months, evaluate and replace with more reliable brands and models; For potential risks in the substation control software, software updates are carried out during off-peak hours, and a comprehensive software upgrade plan is made regularly; To prepare for severe weather, key lines and facilities should be reinforced in advance, and infrastructure construction in areas vulnerable to weather should be strengthened.

8. A normalized operation and maintenance system for power grid data processing information, based on the normalized operation and maintenance method for power grid data processing information according to any one of claims 1 to 7, characterized in that: Including collection module, transmission module, pre-processing storage module, monitoring module, intelligent operation and maintenance module, and decision-making module; The acquisition module is used to collect analog electrical signal data through sensors deployed on power grid nodes, and convert the analog electrical signal data into digital signal data using a 12-bit A / D converter built into the data acquisition model; The transmission module is used to send the collected digital signal data to the data processing center using the TCP / IP protocol and quantum key distribution; The preprocessing storage module is used to preprocess the digital signal data sent to the data processing center, output it as power grid information status data, and store the power grid information status data in the cloud; The monitoring module is used to monitor the power grid information status data in the cloud storage in real time using the status monitoring model; The intelligent operation and maintenance module is used to build an intelligent operation and maintenance model to analyze and evaluate the power grid information status data stored in the cloud; The decision module is used to make operation and maintenance decisions based on the results of analysis and evaluation, and to execute operation and maintenance plans and optimizations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for normalized operation and maintenance of power grid data processing information are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for normalized operation and maintenance of power grid data processing information are implemented as described in any one of claims 1 to 7.

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