Intelligent evaluation method for power transmission and transformation operation safety quantification of smart grid power system

Abstract

The invention discloses a power transmission and transformation operation safety quantification intelligent evaluation method for a smart grid power system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the basic information of a power system from multiple channels, deploying sensors around key power facilities through an intelligent sensor network, and collecting data in real time; the central server receives the data uploaded by the edge computing device, carries out deep analysis on the data through an adaptive learning model and outputs an evaluation report and suggested measures; based on the evaluation report and the suggested measures, establishing a virtual model of the power system by using digital twinning, and performing analog simulation; recording the evaluation report, the suggested measures and a model simulation result on the block chain; and according to the information on the block chain, a security evaluation result is automatically generated and suggested measures are pushed to operation and maintenance personnel. According to the invention, by using the adaptive learning model, the system can perform self-updating according to the historical data and the current state, so that the accuracy and reliability of fault prediction are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for quantitative intelligent evaluation of power transmission and transformation operation safety in a smart grid power system. Background Art

[0002] With the continuous development of smart grid technology, the safety assessment methods of power transmission and transformation operation are also constantly improving; traditional power system safety assessment mainly relies on manual inspections and regular maintenance, which is not only inefficient but also difficult to monitor the operating status of the system in real time; in recent years, intelligent assessment methods based on the Internet of Things and big data analysis have gradually become a research point; by deploying sensor networks, real-time monitoring of key power facilities can be achieved and a large amount of operating data can be collected; these data are pre-processed by edge computing devices and uploaded to the central server, and in-depth analysis is performed using advanced data analysis algorithms to provide more accurate safety assessment results; however, existing technologies still have shortcomings in data processing, fault prediction and information security.

[0003] Although the existing intelligent assessment methods can achieve a certain degree of data collection and preliminary analysis, they lack efficient methods in data cleaning, standardization processing, and feature extraction, resulting in uneven data quality and affecting the accuracy of the analysis; in addition, the existing fault prediction models mostly use static threshold judgments, which do not fully consider the dynamic change characteristics of the system and are prone to false alarms. At the same time, the lack of effective encryption measures in the data transmission process has made information security issues increasingly severe; in view of the above-mentioned deficiencies, the present invention proposes a smart grid power system transmission and transformation operation safety quantitative intelligent assessment method, which effectively improves the real-time and accuracy of data processing by introducing edge computing, adaptive learning models, digital twin simulation and blockchain technology, enhances the reliability of fault prediction, and ensures the security of data. Summary of the invention

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

[0005] Therefore, the present invention provides a smart grid power system transmission and transformation operation safety quantitative intelligent assessment method to solve the power system operation status monitoring and assessment method of the insufficient ability to process massive data and lack of effective intelligent analysis means.

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

[0007] In the first aspect, an embodiment of the present invention provides a method for quantitative intelligent assessment of the safety of power transmission and transformation operation of a smart grid power system, which includes collecting basic information of the power system from multiple channels, deploying sensors around key power facilities through an intelligent sensor network, and collecting data in real time; using edge computing devices deployed in substations to receive sensor data, and performing real-time preprocessing and uploading it to a central server; the central server receives the data uploaded by the edge computing device, performs in-depth analysis of the data through an adaptive learning model, and outputs an assessment report and recommended measures; based on the assessment report and recommended measures, a virtual model of the power system is established using digital twins, and simulation is performed; the assessment report, recommended measures, and results of model simulation are recorded on a blockchain; based on the information on the blockchain, a safety assessment result is automatically generated and recommended measures are pushed to operation and maintenance personnel.

[0008] As a preferred solution of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system of the present invention, wherein: the basic information of the power system includes configuration information, historical data, environmental parameters, real-time data, user needs and specifications;

[0009] The deployed sensors include temperature sensors, humidity sensors, current and voltage sensors, vibration sensors, and environmental monitoring sensors.

[0010] As a preferred solution of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system described in the present invention, the edge computing device deployed in the substation is used to receive sensor data, perform real-time preprocessing and upload it to the central server. The specific steps are as follows:

[0011] The edge computing device receives the data collected by the sensor through wireless communication and performs data cleaning;

[0012] After data cleaning is completed, data standardization begins;

[0013] Extract useful features from data through features;

[0014] Compress the data after feature extraction;

[0015] Convert the preprocessed data into a unified JSON format for transmission;

[0016] Use TLS / SSL encryption for data encryption;

[0017] The edge computing device establishes a connection with the central server and transmits data;

[0018] Upload the data in small batches.

[0019] As a preferred solution of the method for quantifying intelligent evaluation of power transmission and transformation operation safety of the smart grid power system of the present invention, the central server receives data uploaded by the edge computing device, performs in-depth analysis on the data through an adaptive learning model and outputs an evaluation report and recommended measures. The specific steps are:

[0020] The central server sets a listening port and waits to receive data from the edge computing device;

[0021] After receiving the data, the central server decompresses the data and stores the received data in an internal database;

[0022] The central server uses an adaptive learning model based on deep learning to perform in-depth analysis on the data, expressed as:

[0023]

[0024] Among them, P t represents the potential failure trend value predicted at time point t, C t Data representing the current grid operation status, H t-1 Represents the historical data of the previous time point t-1, and Δt is the time interval from t-1 to t.

[0025] As a preferred solution of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system of the present invention, wherein: for judging a fault, it means that,

[0026] P t >τ, it is considered to have a potential fault;

[0027] P t ≤τ, it is considered to be operating normally;

[0028] Where τ is the preset threshold;

[0029] Output assessment report and recommended measures based on the judgment results.

[0030] As a preferred solution of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system of the present invention, the virtual model of the power system is established based on the evaluation report and the recommended measures using digital twins, and simulation is performed. The specific steps are:

[0031] Based on the evaluation report and recommended measures obtained through analysis, a virtual model of the power system is established using digital twins, which is expressed as:

[0032] S t =A×S t-1 +B×U t ;

[0033] Among them, St represents the simulation result at time t, S t-1 represents the simulation result at the previous time point t-1, A is the state transfer matrix, B is the input matrix, U t Represents the control input value at time t.

[0034] As a preferred solution of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system of the present invention, the evaluation report, recommended measures and model simulation results are recorded on the blockchain, and the specific steps are as follows:

[0035] Convert the data of the assessment report, recommended measures and model simulation results into JSON structured data format, and perform hash operation on the converted data to generate a hash value;

[0036] Convert the data of assessment reports, recommended measures and model simulation results into JSON structured data format;

[0037] Perform hash operation on the converted data to generate a hash value;

[0038] In blockchain, new blocks are constructed using hash values;

[0039] The block construction is completed, and the validity of the block is verified through the consensus mechanism;

[0040] When nodes in the blockchain determine that a block is verified and reach consensus, they update the node distributed ledger to include the new block;

[0041] Verify the immutability of data based on the addition of new blocks and the update of node distributed ledgers.

[0042] As a preferred solution of the method for quantifying and intelligently evaluating the safe operation of power transmission and transformation in the smart grid power system of the present invention, the method automatically generates the safety evaluation results based on the information on the blockchain and pushes the recommended measures to the operation and maintenance personnel. The specific steps are as follows:

[0043] Extract the latest assessment reports, recommended measures and simulation results from the blockchain and convert the data into a unified JSON data format;

[0044] Analyze the probability and risk level of potential failures based on historical failure records and current status data;

[0045] Design a safety assessment report template with power system overview, current status analysis, potential failure analysis, and recommended measures;

[0046] Use Python programming language and Jinja2 template engine to automatically generate security assessment results;

[0047] Automatically fill in the security assessment report template based on the converted unified data set and the results of the assessment analysis;

[0048] Convert the filled template into a PDF file;

[0049] Operation and maintenance personnel can view information in real time through mobile terminal devices and make corresponding maintenance decisions;

[0050] The operation and maintenance personnel formulate specific maintenance action plans based on the assessment results and recommended measures, and execute maintenance actions.

[0051] In a second 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, it implements any step of the method for quantitative intelligent assessment of the safety of transmission and transformation operation of a smart grid power system as described in the first aspect of the present invention.

[0052] In a third 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 quantitative intelligent assessment of the safety of transmission and transformation operation of a smart grid power system as described in the first aspect of the present invention.

[0053] The beneficial effects of the present invention are as follows: by utilizing an adaptive learning model, the system can self-update according to historical data and current status, thereby improving the accuracy and reliability of fault prediction; this technology not only improves the intelligence level of power system operation status assessment, but also provides a scientific decision-making basis for operation and maintenance personnel, thereby significantly enhancing the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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.

[0055] Figure 1 This is a flow chart of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of the smart grid power system in Example 1.

[0056] Figure 2 This is a flowchart of the automated security assessment and maintenance suggestion push in Example 1. DETAILED DESCRIPTION

[0057] 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.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0060] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system, comprising the following steps:

[0061] Collect basic information about the power system from multiple channels, deploy sensors around key power facilities through intelligent sensor networks, and collect data in real time; use edge computing devices deployed in substations to receive sensor data, perform real-time preprocessing, and upload it to the central server; the central server receives data uploaded by edge computing devices, conducts in-depth analysis of the data through adaptive learning models, and outputs evaluation reports and recommended measures; based on the evaluation reports and recommended measures, use digital twins to build a virtual model of the power system and perform simulations; record the evaluation reports, recommended measures, and model simulation results on the blockchain; based on the information on the blockchain, automatically generate security assessment results and push recommended measures to operation and maintenance personnel.

[0062] This embodiment also provides a method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system, including:

[0063] S1. Collect basic information of the power system from multiple channels, deploy sensors around key power facilities through intelligent sensor networks, and collect data in real time.

[0064] Furthermore, the basic information of the power system includes configuration information, historical data, environmental parameters, real-time data, user requirements and specifications.

[0065] It should be noted that the configuration information includes equipment list, equipment parameters, network topology, and protection configuration. The equipment list is all key power equipment, such as transformers, circuit breakers, switches, cables, protection devices, etc.

[0066] Equipment parameters are detailed technical specifications of each equipment, such as rated voltage, rated current, capacity, model, manufacturer, etc.;

[0067] Network topology is the physical connection diagram of the power system, including the connection relationship between substations, line layout, etc.

[0068] Protection configuration is the setting of relay protection device, including protection type, setting value, action logic, etc.

[0069] Historical data includes operation logs, fault records, maintenance records, and performance data;

[0070] The operation log records the equipment's operating status, operation records, maintenance records, etc.

[0071] Fault records include the time, location, type, cause, and treatment measures of past fault events;

[0072] Maintenance records are historical records of regular inspections and maintenance, including inspection times, problems found, measures taken, etc.

[0073] Performance data is the performance of the equipment under different load conditions, such as efficiency, loss, temperature change, etc.;

[0074] Environmental parameters include meteorological data, geographic information, and environmental monitoring;

[0075] Meteorological data are temperature, humidity, wind speed, rainfall, thunderstorm activity, etc.;

[0076] Geographic information is the geographical coordinates, altitude, terrain features, etc. of the device’s location;

[0077] Environmental monitoring is air quality, soil resistivity, electromagnetic interference, etc.;

[0078] Real-time data is sensor data, including voltage, current, temperature, humidity, vibration acceleration, noise level, insulation resistance, partial discharge;

[0079] User requirements and specifications include grid specifications and user requirements;

[0080] Grid codes are national or regional power system standards and specifications, such as IEEE and IEC standards;

[0081] User demand is the special requirements of specific users or applications for the power system, such as reliability, stability, safety, etc.

[0082] The deployed sensors include temperature sensors, humidity sensors, current and voltage sensors, vibration sensors, and environmental monitoring sensors.

[0083] Monitor the operating status and environmental parameters of the equipment in real time to realize data collection.

[0084] It should be noted that basic information of the power system is collected through multiple channels, including configuration information, historical data, environmental parameters, real-time data, user needs and specifications. This information provides basic data support for subsequent data analysis; at the same time, intelligent sensor networks are deployed around key power equipment to collect the operating status and environmental parameters of the equipment in real time, providing data sources for real-time monitoring and prediction.

[0085] S2, uses edge computing devices deployed in substations to receive sensor data, pre-process it in real time, and upload it to the central server.

[0086] Furthermore, the edge computing device receives the data collected by the sensor through wireless communication and performs data cleaning;

[0087] After data cleaning is completed, data standardization begins;

[0088] Extract useful features from data through features;

[0089] Compress the data after feature extraction to reduce network transmission speed;

[0090] Convert the preprocessed data into a unified JSON format for transmission;

[0091] Use TLS / SSL encryption to encrypt data and protect the security of data transmission;

[0092] The edge computing device establishes a connection with the central server and transmits data;

[0093] Divide data into multiple small batches for uploading to avoid network congestion.

[0094] It should be noted that the connection between edge computing devices and intelligent sensor networks mainly includes:

[0095] Smart sensors are connected to edge computing devices wirelessly; wireless methods include Wi-Fi, Zigbee, LoRa and other protocols;

[0096] The data collected by the sensors is sent to the edge computing device through specific protocols (such as MQTT, CoAP, etc.); these protocols are designed to efficiently transmit data in low-bandwidth, high-latency or unreliable network environments;

[0097] After receiving the data, the edge computing device immediately starts data preprocessing, including data cleaning, fusion, standardization, normalization, and feature extraction;

[0098] Since the power system has high real-time requirements, edge computing devices usually have certain computing capabilities, which can quickly process data locally, reduce dependence on central servers, and speed up response;

[0099] Data needs to be encrypted and protected during transmission to ensure that sensitive information will not be leaked even if the data is intercepted; in addition, authentication and access control mechanisms need to be considered to ensure that only authorized devices can access the edge computing network.

[0100] S3. The central server receives data uploaded by edge computing devices, conducts in-depth analysis of the data through adaptive learning models, and outputs evaluation reports and recommended measures.

[0101] Furthermore, the central server sets a listening port to wait for receiving data from the edge computing device;

[0102] After receiving the data, the central server decompresses the data and stores the received data in an internal database;

[0103] The central server uses an adaptive learning model based on deep learning to perform in-depth analysis on the data, expressed as:

[0104]

[0105] Among them, P t represents the potential failure trend value predicted at time point t, C t Data representing the current grid operation status, H t-1 Represents the historical data of the previous time point t-1, and Δt is the time interval from t-1 to t.

[0106] S3.1、For judging fault, it means,

[0107] When P t >τ, it is considered to have a potential fault;

[0108] When P t <τ, it is considered to be running normally;

[0109] Where τ is a preset threshold value, which is set by domain experts of the power system. Domain experts understand the normal operating range of the system, common failure modes, and characteristics of various abnormal situations. These domain experts can set reasonable threshold values ​​based on historical data and industry standards.

[0110] For example, P t =0.7, the system is considered to have a potential fault and an alarm notification is issued;

[0111] Output assessment report and recommended measures based on the judgment results.

[0112] It should be noted that, assuming the grid operation state C t =120V, historical data H at the previous time point t-1 t-1 =110V, time interval Δt=1 minute, preset threshold τ=0.5;

[0113] According to the formula:

[0114] The preset threshold τ = 0.5, then P t =10, then P t >τ, the system will be considered to be faulty;

[0115] Based on the fault, the system will generate an assessment report and issue an alarm notification, suggesting that the operation and maintenance personnel conduct inspection and maintenance;

[0116] It should be noted that by calculating the current grid operation state C t Compared with the historical data H at the previous time point t-1 t-1 The rate of change between t and compared with the preset threshold τ, the central server can monitor the stability of the system in real time; when P t When the threshold τ is exceeded, the system determines that there is a potential fault and generates an evaluation report, and issues an alarm to notify the operation and maintenance personnel to conduct inspection and maintenance, so as to timely discover and handle problems and improve the safety and reliability of the power system.

[0117] S4. Based on the assessment report and recommended measures, use digital twins to build a virtual model of the power system and perform simulations.

[0118] Furthermore, based on the evaluation report and recommended measures obtained through analysis, a virtual model of the power system is established using digital twins, expressed as:

[0119] S t =A×S t-1 +B×U t ;

[0120] Among them, S t represents the simulation result at time t, S t-1 represents the simulation result at the previous time point t-1, A is the state transition matrix, which describes how the system transitions from one state to the next state, B is the input matrix, which represents the impact of external control or disturbance on the system, and U t Represents the control input value at time t.

[0121] It should be noted that in order to verify the accuracy of the formula, an example is given:

[0122] Assume the following parameters:

[0123] The state transfer matrix A describes the changing rules of voltage and current;

[0124] The input matrix B describes the effect of external control (such as switch operation) on voltage and current;

[0125] Control input U t Including operation instructions of operation and maintenance personnel;

[0126] The following situation is obtained by calculating the formula:

[0127] Through simulation, we can predict the voltage and current changes of the system under specific operation;

[0128] If simulation results show that certain operations may cause excessive voltage fluctuations, the operation strategy can be adjusted in advance to avoid similar problems in actual operation.

[0129] Through the formula, the central server can accurately simulate the power system and predict its behavior under different conditions, thereby helping operation and maintenance personnel make more scientific decisions, optimize system performance, and improve system safety and reliability.

[0130] It should be noted that the construction of the digital twin model is:

[0131] Collect various operating parameters and environmental data from the power system;

[0132] Use this data to build a virtual model that can be an exact replica of one or more parts of the power system;

[0133] Run simulations on virtual models to simulate different operating conditions and failure scenarios and evaluate the effects of different operating strategies;

[0134] By comparing the measurement results with the actual system, the model is continuously adjusted and optimized to make it reflect the actual situation more accurately;

[0135] Apply verified models to actual systems to assist decision making and improve system operation efficiency and reliability.

[0136] It should be noted that the specific implementation method of the digital twin model is:

[0137] Choose MATLAB / Simulink, Python simulation framework, etc. These tools can help build complex system models;

[0138] Combining physical principles and data analysis techniques to build models, ensuring that the models are both scientific and reasonable and can reflect changes in actual data;

[0139] Complex power system simulations usually require a lot of computing resources, so you may need to use cloud services or high-performance computing clusters;

[0140] As new data and knowledge accumulate, the model needs to be constantly updated to maintain its relevance and accuracy.

[0141] It should be noted that based on the assessment report and recommended measures, digital twin technology is used to build a virtual model of the power system and perform simulations; in this way, the assessment model can be optimized to improve the accuracy of the prediction and the reliability of the system.

[0142] S5. Record the assessment report, recommended measures, and model simulation results on the blockchain.

[0143] Furthermore, the data of the assessment report, the recommended measures, and the results of the model simulation are converted into a JSON structured data format, and a hash operation is performed on the converted data to generate a hash value;

[0144] Convert the data of assessment reports, recommended measures and model simulation results into JSON structured data format;

[0145] Perform hash operation on the converted data to generate a hash value;

[0146] In blockchain, new blocks are constructed using hash values;

[0147] The block construction is completed, and the validity of the block is verified through the consensus mechanism;

[0148] After the block construction is completed, the validity of the block is verified through the consensus mechanism, which ensures that all participating nodes can reach a consensus on the newly added block;

[0149] When nodes in the blockchain determine that a block has been verified and reach consensus, they update the node distributed ledger to include the new block, thereby ensuring that the data of all nodes is synchronized and consistent;

[0150] Based on the addition of new blocks and the update of node distributed ledgers, the immutability of data is verified, and any attempt to modify historical records will be discovered and deemed invalid.

[0151] It should be noted that in a blockchain network, the process of block verification usually involves a consensus mechanism. A block can be considered verified when the following conditions are met:

[0152] Different blockchain systems use different consensus mechanisms, including Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS). For example, in the PoW mechanism, miners need to solve a complex mathematical problem to generate a new block. The first miner to solve the problem will broadcast its solution, and other nodes will verify whether the solution is correct. If most nodes agree that the solution is correct, the block is added to the blockchain;

[0153] In the PoS mechanism, nodes choose the right to create the next block based on the number of tokens they hold and the length of time; once a new block is created and broadcast, other nodes verify its validity and confirm that it complies with the rules.

[0154] Nodes in a blockchain network exchange information through network communication; each node independently verifies newly received blocks; when more than a certain percentage of nodes (more than 50%, but the specific threshold depends on the specific blockchain implementation) agree that a block is valid, it can be considered to have been verified;

[0155] For example, in the Bitcoin network, it usually takes at least six subsequent blocks to be added to the chain, known as “six confirmations,” before a block is considered immutable.

[0156] Each node will check whether the data in the new block complies with the predetermined rules, such as the validity of the transaction, the correctness of the digital signature, the consistency of the hash value, etc.; if all the data in the new block passes these checks and is consistent with the data in the existing blockchain, then the block will be considered valid.

[0157] Once a new block is verified to be valid, all nodes will add it to their local copies (i.e., distributed ledgers); this means that the ledgers of all nodes will be updated synchronously and contain the latest block information.

[0158] It should be noted that the assessment report, recommended measures and digital twin simulation results are recorded on the blockchain to ensure the transparency and immutability of the data; this can enhance the trust in the data and provide a reliable reference for future decision-making.

[0159] S6. Based on the information on the blockchain, automatically generate security assessment results and push recommended measures to operation and maintenance personnel.

[0160] Furthermore, the latest assessment reports, recommended measures and simulation results are extracted from the blockchain and the data is integrated and converted into a unified JSON data format;

[0161] Analyze the probability and risk level of potential failures based on historical failure records and current status data;

[0162] Design a safety assessment report template with power system overview, current status analysis, potential failure analysis, and recommended measures;

[0163] Use Python programming language and Jinja2 template engine to automatically generate security assessment results;

[0164] Automatically fill in the security assessment report template based on the converted unified data set and the results of the assessment analysis;

[0165] Convert the filled template into a PDF file for easy viewing and archiving;

[0166] Operation and maintenance personnel can view information in real time through mobile terminal devices and make corresponding maintenance decisions;

[0167] The operation and maintenance personnel formulate specific maintenance action plans based on the assessment results and recommended measures, and execute maintenance actions.

[0168] It should be noted that after receiving the safety assessment results, the maintenance decisions made by the operation and maintenance personnel include:

[0169] Determine the exact location and cause of the fault;

[0170] If a serious fault is detected, immediate action is required to prevent the accident from escalating;

[0171] Arrange equipment inspection or parts replacement in advance based on predicted potential failure trends;

[0172] Deploy maintenance personnel and spare parts resources to solve problems as quickly as possible;

[0173] Adjust operating parameters or operating procedures based on simulation results to improve system efficiency.

[0174] It should be noted that the operation and maintenance personnel formulate a specific maintenance action plan based on the assessment results and recommended measures, including:

[0175] Specify which maintenance activities should be performed when;

[0176] Ensure all necessary spare parts and tools are available;

[0177] Ensure the maintenance team is familiar with the latest maintenance procedures and technical requirements;

[0178] Collaborates with other departments to complete general maintenance tasks, if necessary;

[0179] Keep detailed records of the maintenance process and regularly review the progress of maintenance to ensure it is completed as planned.

[0180] It should be noted that the power system overview includes system configuration, key equipment information, etc.; the current status analysis includes real-time data, historical data comparison, trend charts, etc.; the potential fault analysis includes fault prediction value, risk level, fault cause analysis, etc.; the recommended measures include specific maintenance steps, preventive measures, emergency plans, etc.

[0181] It should be noted that by extracting and integrating the latest assessment reports, recommended measures and simulation results from the blockchain, a detailed safety assessment report is automatically generated and converted into PDF format; operation and maintenance personnel can view this information in real time and make maintenance decisions quickly, including determining the location and cause of the fault, taking immediate measures to avoid the expansion of the accident, arranging equipment inspection or replacement of parts in advance, and allocating maintenance resources; in addition, a specific maintenance action plan is formulated based on the assessment results to ensure that spare parts and tools are in place, the maintenance team is familiar with the process, collaborates with other departments to complete the task, and records the maintenance process to ensure completion as planned.

[0182] This embodiment also provides a computer device, which is suitable for the case of a smart grid power system transmission and transformation operation safety quantitative intelligent assessment method, 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 smart grid power system transmission and transformation operation safety quantitative intelligent assessment method as proposed in the above embodiment.

[0183] 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.

[0184] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for realizing the quantitative intelligent evaluation of the safe operation of power transmission and transformation of the smart grid power system 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 storage, flash memory, disk or optical disk.

[0185] In summary, by utilizing an adaptive learning model, the present invention enables the system to self-update according to historical data and current status, thereby improving the accuracy and reliability of fault prediction; this technology not only improves the intelligence level of power system operation status assessment, but also provides a scientific decision-making basis for operation and maintenance personnel, thereby significantly enhancing the safety and stability of the power system.

[0186] 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 quantitative intelligent evaluation of power transmission and transformation operation safety in a smart grid power system, characterized by: include, Collect basic information about the power system from multiple channels, deploy sensors around key power facilities through intelligent sensor networks, and collect data in real time; The sensor data is received by edge computing devices deployed at the substation, and pre-processed in real time and uploaded to the central server; The central server receives data uploaded by edge computing devices, conducts in-depth analysis of the data through adaptive learning models, and outputs evaluation reports and recommended measures; Based on the assessment report and recommended measures, a virtual model of the power system is built using digital twins and simulations are performed; Record assessment reports, recommended measures, and model simulation results on the blockchain; Based on the information on the blockchain, security assessment results are automatically generated and recommended measures are pushed to operation and maintenance personnel.

2. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 1, characterized in that: The basic information of the power system includes configuration information, historical data, environmental parameters, real-time data, user requirements and specifications; The deployed sensors include temperature sensors, humidity sensors, current and voltage sensors, vibration sensors, and environmental monitoring sensors.

3. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 2, characterized in that: The edge computing devices deployed in the substation are used to receive sensor data, perform real-time preprocessing and upload it to the central server. The specific steps are as follows: The edge computing device receives the data collected by the sensor through wireless communication and performs data cleaning; After data cleaning is completed, data standardization begins; Extract useful features from data through features; Compress the data after feature extraction; Convert the preprocessed data into a unified JSON format for transmission; Use TLS / SSL encryption for data encryption; The edge computing device establishes a connection with the central server and transmits data; Upload the data in small batches.

4. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 3, characterized in that: The central server receives data uploaded by the edge computing device, performs in-depth analysis on the data through an adaptive learning model, and outputs an evaluation report and recommended measures. The specific steps are: The central server sets a listening port and waits to receive data from the edge computing device; After receiving the data, the central server decompresses the data and stores the received data in an internal database; The central server uses an adaptive learning model based on deep learning to perform in-depth analysis on the data, expressed as: Among them, P t represents the potential failure trend value predicted at time point t, C t Data representing the current grid operation status, H t-1 Represents the historical data of the previous time point t-1, and Δt is the time interval from t-1 to t.

5. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 4, characterized in that: For judging the fault, it means, P t >τ, it is considered to have a potential fault; P t ≤τ, it is considered to be operating normally; Where τ is the preset threshold; Output assessment report and recommended measures based on the judgment results.

6. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 4, characterized in that: Based on the assessment report and recommended measures, a virtual model of the power system is established using digital twins and simulation is performed. The specific steps are: Based on the evaluation report and recommended measures obtained through analysis, a virtual model of the power system is established using digital twins, which is expressed as: S t =A×S t-1 +B×U t ; Among them, S t represents the simulation result at time t, S t-1 represents the simulation result at the previous time point t-1, A is the state transfer matrix, B is the input matrix, U t Represents the control input value at time t.

7. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 5, characterized in that: The assessment report, recommended measures and model simulation results are recorded on the blockchain. The specific steps are as follows: Convert the data of the assessment report, recommended measures and model simulation results into JSON structured data format, and perform hash operation on the converted data to generate a hash value; Convert the data of assessment reports, recommended measures and model simulation results into JSON structured data format; Perform hash operation on the converted data to generate a hash value; In blockchain, new blocks are constructed using hash values; The block construction is completed, and the validity of the block is verified through the consensus mechanism; When nodes in the blockchain determine that a block is verified and reach consensus, they update the node distributed ledger to include the new block; Verify the immutability of data based on the addition of new blocks and the update of node distributed ledgers.

8. The method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to claim 6, characterized in that: The security assessment results are automatically generated based on the information on the blockchain and recommended measures are pushed to the operation and maintenance personnel. The specific steps are: Extract the latest assessment reports, recommended measures and simulation results from the blockchain and convert the data into a unified JSON data format; Analyze the probability and risk level of potential failures based on historical failure records and current status data; Design a safety assessment report template with power system overview, current status analysis, potential failure analysis, and recommended measures; Use Python programming language and Jinja2 template engine to automatically generate security assessment results; Automatically fill in the security assessment report template based on the converted unified data set and the results of the assessment analysis; Convert the filled template into a PDF file; Operation and maintenance personnel can view information in real time through mobile terminal devices and make corresponding maintenance decisions; The operation and maintenance personnel formulate specific maintenance action plans based on the assessment results and recommended measures, and execute maintenance actions.

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 quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for quantitative intelligent evaluation of power transmission and transformation operation safety of a smart grid power system according to any one of claims 1 to 8 are implemented.

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