Intelligent power grid safety monitoring system

Abstract

The invention discloses an intelligent power grid safety monitoring system, and relates to the technical field of electric power, and the system comprises a data collection module which is used for obtaining various kinds of key data in the operation process of an intelligent power grid in real time, and providing a basis for subsequent analysis and decision making; the communication transmission module is used for safely, reliably and quickly transmitting a large amount of collected data to a data processing center; the data processing module is used for efficiently processing mass data transmitted by the communication transmission module; the data analysis module is used for carrying out deep analysis on the collected data; and the monitoring display module is used for visually displaying results of the data processing module and the data analysis module to operation and maintenance personnel. The system is provided with the monitoring display module, operation and maintenance efficiency is improved through visualization, key support is provided for safe, efficient and economical operation of the power grid through cross-department cooperation and refined management and control, and the system is a core link of the intelligent power grid from technology integration to practical landing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a smart grid safety monitoring system. BACKGROUND

[0002] With the rapid development of economy, the scale and complexity of smart grid are increasing day by day. On the one hand, distributed energy makes the topology structure and operation characteristics of power grid more complex and changeable. On the other hand, the amount of information interaction in smart grid increases exponentially, and the risk of network attack also increases significantly. These factors have brought serious challenges to the safe and stable operation of smart grid. Therefore, safety monitoring, as the core link of smart grid operation management, is a key means to ensure reliable power supply and prevent various accident risks.

[0003] The patent with publication number CN118630920A discloses a digital smart grid safety monitoring system, relating to the technical field of power grid safety monitoring. The data collection system is used to collect and summarize various power grid data in the power distribution network system. The data monitoring system is used to monitor various power grid data in real time and send warning information when the data is abnormal. The data prediction system predicts the power distribution network situation based on existing power grid data. The human mobilization system makes corresponding personnel and material allocation according to the fault situation. The digital smart grid safety monitoring system can monitor various power grid data of the power distribution network system and predict future power consumption, and timely allocate corresponding personnel and materials.

[0004] The patent with publication number CN115834177A discloses a smart grid safety monitoring method and device, and storage medium. The method includes: obtaining the communication traffic of the to-be-monitored device in the smart grid system, and calculating the information network abnormality degree of the to-be-monitored device based on the communication traffic; obtaining the power measurement data of the to-be-monitored device, and calculating the power system abnormality degree based on the power measurement data; calculating the grid system abnormality degree according to the information network abnormality degree and the power system abnormality degree; and detecting and positioning the attack event by comparing the size relationship between the grid system abnormality degree and the preset safety threshold. The information network abnormality degree and the power system abnormality degree are combined to calculate the grid system abnormality degree, so as to comprehensively examine the factors related to the attack event in the smart grid for safety monitoring, which can effectively improve the accuracy of smart grid safety monitoring.

[0005] For example, patent No. CN120522499A discloses a power grid multi-agent collaborative large model safety monitoring method and system, relating to the technical field of smart grid safety monitoring. The method includes: obtaining the voltage record of the nodes on the power grid multi-agent management path, the delay time of load change, and the number of current direction changes, identifying the voltage change trend according to the node voltage trough value in the detection period, detecting whether the current direction has a continuous reversal between two time periods to obtain a direction consistent path list. By comparing the correlation of voltage and load change time, and the continuous reversal of current direction, the stability problem and potential fault in the power grid can be effectively identified, large-scale failure can be prevented in advance, the labeling of key abnormal sources and the adjustment of path impact weight can more accurately adjust resources, optimize the operation efficiency of the power grid, significantly improve the control ability of the overall safety and efficiency of the power grid, and reduce the operation risk and maintenance cost.

[0006] However, in the above-mentioned power grid monitoring system, the power grid data collection and transmission method is relatively single, the data processing capability is limited, and it is difficult to meet the rapid processing and analysis demand of a large amount of data, the identification and diagnosis ability of complex faults and potential safety hazards is insufficient, it is difficult to achieve early warning and accurate prevention and control, the integration and collaboration of the system are poor, there is a lack of effective information sharing and interaction between different monitoring subsystems, and it is difficult to form a unified safety monitoring system.

[0007] In view of the above problems, it is urgent to make innovative design on the basis of the original monitoring system. SUMMARY

[0008] The purpose of the present application is to provide an intelligent power grid safety monitoring system to solve the problem of the existing monitoring system in the above background art, that is, the power grid data collection and transmission method is relatively single, the data processing capability is limited, it is difficult to meet the rapid processing and analysis demand of a large amount of data, the identification and diagnosis ability of complex faults and potential safety hazards is insufficient, it is difficult to achieve early warning and accurate prevention and control, the integration and collaboration of the system are poor, there is a lack of effective information sharing and interaction between different monitoring subsystems, and it is difficult to form a unified safety monitoring system.

[0009] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an intelligent power grid safety monitoring system, comprising a data acquisition module for acquiring various types of key data in the operation process of the intelligent power grid in real time, providing a basis for subsequent analysis and decision-making;

[0010] A communication transmission module is used to safely, reliably and quickly transmit a large amount of collected data to a data processing center;

[0011] The data processing module is used for efficiently processing a large amount of data transmitted by the communication transmission module, guaranteeing data quality, laying a foundation for subsequent analysis, mining data value and realizing accurate perception of the safety state of the power grid.

[0012] The data analysis module is used for deeply analyzing the collected data, realizing real-time evaluation, fault diagnosis and risk prediction of the operation state of the power grid.

[0013] The monitoring display module presents the results of the data processing module and the data analysis module to the operation and maintenance personnel in an intuitive manner, so as to facilitate real-time monitoring of the operation state of the power grid and timely discovery and processing of various safety problems.

[0014] Preferably, the data collection module comprises an electrical parameter sensor, a device state sensor, an environment monitoring sensor and a smart meter.

[0015] Preferably, the electrical parameter sensor is used for collecting basic electrical parameters such as voltage, current, power and frequency in the power grid, the device state sensor is used for monitoring the operation state of the power equipment, the environment monitoring sensor is used for collecting relevant information of the operation environment of the power grid, and the smart meter installed at the user end is used for collecting information such as power consumption, power consumption time and power consumption of the user, realizing real-time monitoring and analysis of the power consumption behavior of the user.

[0016] Preferably, the communication mode of the communication transmission module comprises optical fiber communication, wireless communication and power line carrier wave communication.

[0017] Preferably, the steps of the optical fiber communication are as follows: firstly, data source preparation and signal preprocessing are performed, the data to be transmitted of the smart grid safety monitoring system is obtained, the collected data is filtered, the electrical signal is modulated into an optical signal, the transmission link of the optical signal in the optical fiber is selected and deployed, after the optical signal is transmitted to the target node, the optical signal is received and adjusted, that is, after the optical signal is transmitted to the target node, the optical signal needs to be restored into an electrical signal through an optical receiver, after the demodulation is completed, the data needs to be verified and distributed, and accurate transmission of the data to the target module of the smart grid safety monitoring system is ensured.

[0018] Preferably, the wireless communication step is: first, the data is screened and preprocessed, and the data after screening and preprocessing can reduce the transmission load and ensure data security, the wireless communication converts the preprocessed electrical signal into a radio frequency signal that can be transmitted in the air, then the transmission channel of the wireless signal is selected, that is, the adaptive channel is selected according to the position of the monitoring node, after the wireless signal is transmitted to the target node, the signal receiving and restoration are completed by the wireless receiving module, that is, the wireless signal receiving and demodulation, the demodulated digital signal is outputted and consistent with the preprocessed digital signal at the transmitting end, which prepares for subsequent data verification, and after the demodulation is completed, the data integrity needs to be verified and distributed to the corresponding module to ensure effective use of data.

[0019] Preferably, the data processing module includes four stages, and the four stages are: first stage: data receiving and preprocessing, which can guarantee data quality and lay a foundation for subsequent data analysis; second stage: data fusion and feature extraction, integrating multi-source information, achieving information complementation through data fusion, and then extracting key features to support subsequent analysis; third stage: deep analysis, realizing accurate diagnosis of power grid fault and real-time evaluation of safety risk through machine learning, deep learning and other algorithms; fourth stage: result output and feedback optimization, delivering effective information and continuously improving performance.

[0020] Preferably, the data analysis module includes data exploratory analysis, which can understand data rules and determine the analysis direction; analysis model construction, which can establish the mapping relationship between data and safety targets; real-time data analysis and reasoning, which can dynamically adapt to power grid operation; output decision suggestion, which can verify the results and optimize the model; closed-loop iteration, which can improve the analysis accuracy.

[0021] Preferably, the data exploratory analysis step is: first, basic statistical analysis is performed, including data distribution characteristic analysis, data correlation analysis and time series trend analysis, then visual analysis is performed, including static visualization, dynamic visualization and correlation visualization, and finally, preliminary identification of abnormal patterns is performed, including abnormal identification based on statistical threshold and abnormal identification based on time series deviation.

[0022] Preferably, the step of monitoring the display module is: first receiving data of different modules through a standardized interface, converting the data into a unified format inside the module through a protocol analysis engine according to the data protocol of different modules, classifying the data according to priority and timeliness, and matching according to the relevance of the data, based on the adapted unified data, the monitoring display module generates diversified visual interfaces according to the needs of different monitoring scenes, ensures that the operation and maintenance personnel can quickly obtain core information, classifies and pushes the fault alarm and risk warning output by the data analysis module, ensures timely response, finally archives the display data and operation records, and shares relevant data with other departments of the power grid, and supports cross-department cooperation.

[0023] Compared with the prior art, the present application has the following advantages:

[0024] 1. Through the data acquisition module, multi-factor correlation analysis can be supported, and composite risks can be warned. The core value of the data acquisition module is to provide comprehensive, real-time and accurate raw data for the intelligent power grid safety monitoring system. It is not only the premise of subsequent data processing, analysis and display, but also provides core support for the safe, efficient, economic and reliable operation of the power grid by eliminating information blind spots, supporting fault diagnosis, pre-warning, optimizing operation strategy and ensuring economic operation.

[0025] 2. Through the data analysis module, the key transformation from data to decision is realized, and the analysis accuracy and response speed directly determine the warning ability and disposal efficiency of the intelligent power grid safety monitoring system, which is the core technical support for the transformation of the power grid from passive repair to active prevention.

[0026] 3. The monitoring display module is provided, which not only improves the operation efficiency through visualization, supports scientific decision-making through data, strengthens safety prevention and control through risk warning, and provides key support for the safe, efficient and economic operation of the power grid through cross-department cooperation and fine management and control, and is the core link of the intelligent power grid from technology integration to practical application. BRIEF DESCRIPTION OF DRAWINGS

[0027] Fig. 1 It is a schematic diagram of the whole monitoring system of the present application.

[0028] Fig. 2 It is a schematic diagram of the implementation method of the monitoring system of the present application. DETAILED DESCRIPTION

[0029] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part but not all of the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of the present application.

[0030] The embodiment of the present application provides a smart grid safety monitoring system, comprising:

[0031] A data acquisition module is configured to acquire various types of key data in the operation process of the smart grid in real time, and provide a basis for subsequent analysis and decision-making.

[0032] A communication transmission module is configured to safely, reliably and quickly transmit a large amount of collected data to a data processing center.

[0033] A data processing module is configured to efficiently process a large amount of data transmitted by the communication transmission module, guarantee data quality, lay a foundation for subsequent analysis, mine data value, and realize accurate perception of the safety state of the power grid.

[0034] A data analysis module is configured to deeply analyze the collected data, realize real-time evaluation, fault diagnosis and risk prediction of the operation state of the power grid.

[0035] A monitoring display module is configured to present the results of the data processing module and the data analysis module to operation and maintenance personnel in an intuitive manner, facilitate real-time monitoring of the operation state of the power grid, and timely discover and handle various safety problems.

[0036] The embodiments will be described in detail below with reference to the accompanying drawings and specific implementation manners, with reference to Figs. 1-2 The method for implementing the monitoring system according to the smart grid safety monitoring system of the embodiment of the present application comprises the following steps:

[0037] S1, collecting data, mainly composed of sensors and intelligent devices distributed at various key nodes of the power grid, acquiring various types of key data in the operation process of the smart grid in real time, and providing a basis for subsequent analysis and decision-making.

[0038] In the present application, it is necessary to explain that the data acquisition module includes electrical parameter sensors, device state sensors, environmental monitoring sensors and smart meters, wherein the electrical parameter sensors are used to collect basic electrical parameters such as voltage, current, power and frequency in the power grid, the device state sensors are used to monitor the running state of power equipment, such as the oil temperature, winding temperature and oil level of the transformer, the on-off state and contact wear of the switching device, and the rotating speed, vibration and bearing temperature of the motor, the environmental monitoring sensors are used to collect relevant information of the power grid operating environment, such as temperature, humidity, air pressure, wind speed, rainfall and harmful gas concentration, and the smart meters are installed at the user end to collect information such as user power consumption, power consumption time and power consumption, and realize real-time monitoring and analysis of user power consumption behavior.

[0039] In specific implementation, the electrical parameter sensors are used as follows: high-precision electronic voltage and current transformers are installed at the incoming and outgoing lines of the transmission line and the substation, and at the important load nodes, these transformers can convert high voltage and large current into small signals suitable for measurement and transmission, and transmit the signals to the data acquisition unit through optical fiber or cable; the device state sensors are used as follows: optical fiber temperature sensors are installed on the transformer to monitor the winding and oil temperature of the transformer in real time, the temperature information is accurately obtained by measuring the characteristic change of the transmitted light in the optical fiber, the vibration sensor is used to monitor the vibration of the motor, and the frequency, amplitude and phase of the vibration signal are analyzed to determine whether the motor is running normally; the environmental monitoring sensors are used as follows: temperature and humidity sensors, wind speed sensors and rainfall sensors are installed on the towers of the outdoor transmission line and the substation to monitor environmental temperature and humidity, wind speed and rainfall, etc. in real time, and provide basis for evaluating the running reliability of power grid equipment under different environmental conditions; harmful gas sensors are installed in areas where harmful gas leakage may occur, such as cable trenches and distribution rooms of substations, to monitor harmful gas concentration and discover potential safety hazards in time; the smart meter supports bidirectional communication, which can not only upload user power consumption data to the monitoring system, but also receive control instructions from the monitoring system to realize remote control of user power consumption equipment.

[0040] In this embodiment, sensors and devices are distributed in various links of the smart grid according to certain density and layout principles, ensuring that the operation data of the power grid can be comprehensively and accurately collected. For power transmission lines, a set of electrical parameter sensors and device state sensors are installed at certain intervals, and environmental monitoring sensors are additionally arranged at important crossings and sections that are prone to natural disasters. In the substation, corresponding sensors are equipped for each main transformer, switch device, and mutual inductor, etc. key devices, to realize comprehensive monitoring of the operation state of the devices. On the user side, smart meters are reasonably configured according to the type and scale of electricity consumption of users, to ensure that the user electricity consumption information can be accurately obtained. The data collection frequency is flexibly set according to different data types and monitoring requirements. For data with high real-time requirements such as electrical parameters, the collection frequency is usually set to once per second or higher, to ensure that the instantaneous changes of the operation state of the power grid can be captured in time. For device state and environmental monitoring data, the collection frequency is usually once per minute or once every few minutes, which can meet the monitoring requirements of the operation state of the devices and the changes of the environment, and can effectively reduce the data transmission and storage pressure. For user electricity consumption data, the smart meter collects and uploads the data at a preset time interval (such as every 15 minutes or every 30 minutes), to facilitate the statistics and analysis of the user electricity consumption behavior.

[0041] It can be seen that in the embodiment, through data acquisition, multi-link data can be covered, and the whole network operation dynamic can be mastered. Through whole-link data acquisition, the operation and maintenance personnel can master the whole network operation dynamic in real time. For example, when the load of a certain regional distribution network increases suddenly, combined with the user side power consumption data and the distribution side equipment state data, it can be quickly judged whether the load growth is caused by the user power consumption peak, so as to avoid the judgment deviation caused by relying on single-link data. Not only electrical parameters (voltage, current, power) are collected, but also equipment state data (such as transformer oil level, partial discharge amount, switch mechanical characteristics) and environmental data (such as temperature and humidity, rainfall, icing density) are collected synchronously. For example, when monitoring the transformer, the current and voltage data cannot comprehensively judge the health status of the equipment, but combined with the oil temperature, partial discharge amount and environmental temperature data, it can more accurately identify whether the oil temperature is increased due to overload and whether the partial discharge exceeds the standard due to insulation aging. The real operation state of the equipment is restored, and misjudgment caused by a single data type is avoided. For the scenes that are difficult to cover by traditional monitoring (such as remote mountainous area transmission line and underground distribution network), the data acquisition module fills the information blind area by deploying wireless sensors, infrared monitoring equipment and the like. For example, the remote mountainous area transmission line is easily affected by icing and lightning, but manual inspection is difficult and low in frequency. The data acquisition module can collect the icing thickness in real time through the icing sensor and record the lightning frequency and position through the lightning positioning sensor, so as to timely find the hidden risk and avoid the expansion of the fault caused by the monitoring blind area. At the same time, the data acquisition module can store the historical operation data for a long time. These data are the core basis for building a risk prediction model. Through continuous data acquisition, the trend change of the parameters can be monitored in real time, and the risk clues can be found in time. The collected multiple types of data can support multi-factor correlation analysis, early warning of composite risks, and the core value of the data acquisition module lies in providing "comprehensive, real-time and accurate" raw data for the intelligent power grid safety monitoring system. It is not only the premise of subsequent data processing, analysis and display, but also provides core support for the realization of "safe, efficient, economic and reliable" operation of the power grid by eliminating information blind area, supporting fault diagnosis, pre-positioning risk warning, optimizing operation and maintenance strategy and guaranteeing economic operation. The data acquisition module is the key starting point for the transformation of intelligent power grid from "traditional operation" to "intelligent operation".

[0042] S2, safely, reliably and quickly transmit the collected large amount of data to the data processing center;

[0043] In the present application, it should be noted that the communication mode of the communication transmission module includes optical fiber communication, wireless communication and power line carrier communication.

[0044] The step of optical fiber communication is: first, data source preparation and signal preprocessing, from the multi-type data obtained from the data acquisition module, high priority and large capacity data (such as real-time electrical parameters of substation, high-definition video monitoring data of transmission line, continuous monitoring data of equipment state, etc.) need to be transmitted through optical fiber, low priority data (such as environmental temperature and humidity routine record) can be transmitted through wireless communication auxiliary transmission to avoid occupying optical fiber bandwidth, the collected analog signal (such as current and voltage analog output of sensor) is converted into digital signal through analog-to-digital converter (ADC), and the digital signal is packaged into the format (such as IEC61850 standard protocol) conforming to power grid communication protocol, so as to ensure the compatibility and recognizability of data in optical fiber transmission, for high-definition video, mass historical operation data and other large capacity data, H.264 / H.265 video compression algorithm is adopted to reduce data amount and transmission bandwidth occupation, the core formula of H.264 / H.265 video compression algorithm can be referred to eliminate spatial redundancy, which is: let the current pixel be P(x, y), the left adjacent pixel set be A={ }, the upper adjacent pixel set be B={ }, the prediction value Pred(x, y) is generated based on the direction model, and the average of adjacent pixels is taken as the prediction value (suitable for flat area): Pred(x, y)= , for a specific angle θ such as (0°, 45°, 90°, etc.), the prediction value is generated by adjacent pixel interpolation, Pred(x, y)= )(near horizontal direction), Pred(x, y)= )(near vertical direction), the difference (residual) between the actual pixel and the prediction value is the object of subsequent compression, and the formula is , the absolute value of the residual is much smaller than the original pixel value (usually concentrated near 0), greatly reducing data redundancy, while encrypting the data, the encryption method can refer to AES-256 encryption algorithm, prevent data from being stolen or tampered with during transmission, protect the security of power grid data, select appropriate light sources according to transmission distance and bandwidth requirements, short distance transmission (such as internal equipment in substation) commonly uses vertical cavity surface emitting laser (VCSEL), long distance transmission (such as between substation and dispatching center) commonly uses distributed feedback laser (DFB), provide stable current for light source through driving circuit, control light source in normal working state, load electrical signal on optical signal by using digital modulation technology, inject modulated optical signal into optical fiber through optical fiber coupler, ensure the coupling efficiency of optical signal (usually requires ≥90%) during coupling process, reduce optical energy loss, avoid transmission distance shortening or signal attenuation too large due to coupling deviation, select single mode fiber or multimode fiber according to the distribution of power grid monitoring nodes: single mode fiber has small core diameter (about 9μm) and small dispersion, suitable for long distance transmission above 10km; Multimode fiber has large core diameter (such as 50μm, 62.5μm, low cost, suitable for short distance transmission within 2km; at the same time, in the process of optical fiber laying, excessive bending (bending radius needs to be ≥10 times of the diameter of the optical fiber) is avoided, and bending loss is reduced; when the transmission distance exceeds the maximum unrepeatered transmission distance of the optical fiber (usually 40-80km for single-mode optical fiber and 500m-2km for multi-mode optical fiber), an optical amplifier (such as an erbium-doped fiber amplifier EDFA) needs to be deployed in the link to amplify the attenuated optical signal and compensate for the loss of optical energy during transmission; for super-long distance transmission (such as cross-regional power grid dispatching), a relay station also needs to be set up to convert the optical signal into an electrical signal for regeneration processing and then modulate the optical signal for continuous transmission to avoid signal distortion; an optical time domain reflectometer (OTDR) is deployed in the optical transmission link to monitor the transmission state of the optical signal in real time; when faults such as optical fiber breakage and loose joints occur, the OTDR can accurately locate the fault position (error ≤10m) by analyzing the reflected wave of the optical signal and feed back the fault information to the monitoring system, so that the maintenance personnel can promptly repair the fault and ensure the continuity of data transmission; then the optical signal is restored to an electrical signal through an optical receiver, and the optical signal transmitted by the optical fiber is received by an optoelectronic detector (such as a PIN photodiode or an avalanche photodiode APD) to convert the change in optical intensity of the optical signal into a corresponding change in current, completing the "optical-electric" conversion; the APD has an avalanche multiplication effect and is more sensitive than the PIN, making it suitable for long-distance and weak light signal receiving scenarios; the weak current signal output by the optoelectronic detector is amplified to a voltage signal by a preamplifier, and then further amplified to an appropriate amplitude by a main amplifier; at the same time, high-frequency noise (such as electromagnetic interference and noise caused by fiber dispersion) introduced during transmission is filtered out by a low-pass filter to reduce the impact of noise on signal quality; after demodulation, the data needs to be verified and distributed to ensure accurate transmission of the data to the target module of the smart grid security monitoring system.

[0045] In this embodiment, the steps of wireless communication are: first, the data is screened and preprocessed; after screening and preprocessing, the data can reduce the transmission load and ensure data security; the wireless communication converts the preprocessed electrical signal into a radio frequency signal that can be transmitted in the air; then the transmission channel of the wireless signal is selected, i.e. the appropriate channel is selected according to the position of the monitoring node; after the wireless signal is transmitted to the target node, the signal reception and restoration are completed by the wireless receiving module, i.e. the wireless signal is received and demodulated; after demodulation, the digital signal output is consistent with the preprocessed signal at the transmitting end, preparing for subsequent data verification; and after demodulation, the data integrity needs to be verified and distributed to the corresponding module to ensure effective use of the data.

[0046] In this embodiment, the steps of power line carrier communication are as follows: firstly, data screening and preprocessing are performed, the localized data in the area that does not need to be transmitted across the area is screened out from the data acquisition module, the collected analog signal is converted into a digital signal through an analog-to-digital converter, is packaged according to the power line carrier communication protocol of the power grid, to ensure that the PLC devices of different manufacturers can be interchanged, the lightweight compression algorithm is used for small-capacity and periodic data in the area to reduce the data volume and transmission delay; the data is encrypted through an encryption algorithm to prevent data leakage caused by illegal access to the power line, while controlling the encryption power consumption to adapt to the low-power PLC terminal in the area; then, the electric signal is modulated into a power line carrier signal, according to the smart grid PLC application specification, the carrier frequency range conforming to the national standard is selected, that is, 10-500 kHz for the low-voltage distribution network and 10-400 kHz for the medium-voltage distribution network, to avoid overlapping with the power frequency (50 Hz / 60 Hz) and harmonic frequency of the power system itself, reduce interference, and use a modulation mode suitable for the complex channel of the power line to load the electric signal onto the high-frequency carrier, and the modulated carrier signal is amplified to the appropriate power (the output power of the low-voltage PLC terminal is usually 10-20 dBm) through a power amplifier, to ensure that the signal has enough transmission distance on the power line; at the same time, the noise signals other than the carrier frequency are filtered out through a band-pass filter to avoid interference to the power line and other equipment, the modulated carrier signal needs to be injected into the existing power line through a coupling circuit to realize power-signal sharing, when the carrier signal is transmitted on the power line, it will be attenuated due to the line resistance, capacitance and inductance, the PLC repeater can be deployed at intervals in the area to receive and amplify the attenuated carrier signal and forward it, to extend the transmission distance, the adaptive power control technology is used, the PLC terminal adjusts the transmission power in real time according to the received signal strength, to balance the transmission distance and power consumption, for the main interference in the power line, the frequency band affected by the interference can be identified through subcarrier detection, the data is automatically transmitted in the frequency band, the data is encoded through forward error correction coding, the receiving end corrects the errors generated in the transmission process through decoding without retransmission, the load change of the user in the area will cause the change of the power line impedance and noise characteristics, the PLC module adjusts the modulation parameters in real time through real-time channel monitoring to adapt to the channel change, after the carrier signal is transmitted to the target node (such as the area concentrator and distribution terminal), the PLC receiving module extracts and restores the electric signal, after demodulation, the data integrity is verified and distributed to the target module of the smart grid safety monitoring system.

[0047] It can be seen that in the embodiment, corresponding steps can be selected and implemented according to different communication modes. In the intelligent power grid safety monitoring system, the optical fiber communication realizes the power grid data transmission with large capacity, high security and long distance through the whole process operation of "data preparation-signal modulation-optical transmission-optical reception-demodulation-data distribution", which provides reliable communication support for real-time monitoring, fault diagnosis and safety decision of the system. The wireless communication realizes the efficient transmission of low-priority data through the process of "lightweight preprocessing-low-power modulation-flexible channel transmission-accurate demodulation-on-demand distribution" in the intelligent power grid safety monitoring system, which is especially suitable for scattered scenes that are difficult to cover by optical fiber and forms a complement to optical fiber communication to jointly build a "wired + wireless" three-dimensional network of power grid communication transmission. The power line carrier communication module realizes low-cost and convenient data transmission in the intelligent power grid distribution network area through the core process of "multiplexing power line-anti-interference modulation-dynamic adaptive channel", which forms a complement to the "long distance, large capacity" of optical fiber communication and the "wide coverage, flexible deployment" of wireless communication to jointly build a power grid communication network covering "main trunk-branch-substation" and provide reliable support for local data acquisition and analysis of the safety monitoring system.

[0048] S3, efficiently process a large amount of data transmitted by the communication transmission module, guarantee data quality, lay a foundation for subsequent analysis, and at the same time, mine data value to realize accurate perception of the power grid safety state;

[0049] In the present application, it should be noted that the data processing module includes four stages, and the four stages are: the first stage: data reception and preprocessing, which can guarantee data quality and lay a foundation for subsequent data analysis; the second stage: data fusion and feature extraction, which integrates multi-source information, realizes information complementation through data fusion, and extracts key features to support subsequent analysis; the third stage: deep analysis, which realizes accurate diagnosis of power grid faults and real-time evaluation of safety risks through machine learning, deep learning and other algorithms; the fourth stage: result output and feedback optimization, which delivers effective information and continuously improves performance.

[0050] In a specific implementation, first, the data is preprocessed, that is, the collected data is cleaned, denoised, normalized, and the like, to remove noise, outliers, and duplicate data in the data, fill in missing data, and uniformly convert data of different formats and units into a standard format suitable for analysis, to improve data quality and provide a reliable data foundation for subsequent analysis. For example, an outlier detection algorithm based on statistical methods is used to identify and remove outliers in electrical parameter data; a data interpolation algorithm is used to fill in missing values in equipment status data, and then key features reflecting the operating state of the power grid and the health status of the equipment are extracted from the preprocessed data, such as the trend of electrical parameters, characteristic indicators of equipment operating status, and mode characteristics of user power consumption behavior. For example, by performing Fourier transform on voltage and current data, the harmonic characteristics thereof are extracted to evaluate the power quality of the power grid; the time domain and frequency domain characteristics of vibration signals are used to determine the fault type and severity of the motor, and an artificial intelligence algorithm such as a support vector machine, an artificial neural network, or a deep learning algorithm is used to establish a fault diagnosis model, which is trained and learned from a large amount of historical fault data and normal operating data, so that the model can accurately identify various fault types and fault locations during power grid operation and provide fault cause analysis and fault handling suggestions.

[0051] As can be seen in the present embodiment, through the data processing module, the original data can be "cleaned", abnormal data caused by sensor failure can be identified and corrected through an outlier detection algorithm, data loss caused by temporary communication interruption can be supplemented by interpolation, and redundant information collected repeatedly can be removed; secondly, "standardization and normalization" is performed, data of different units and different orders of magnitude are converted into standardized data of a unified dimension, and the influence of data order differences on the analysis results is avoided; thirdly, "data integration" is performed, data scattered in different collection nodes is associated and integrated according to key fields such as time stamp and equipment number, to form a structured data set, providing a high-quality and high-availability data foundation for subsequent in-depth analysis. At the same time, the data processing module can link multiple module functions to ensure closed-loop operation of the monitoring system. By receiving the data stream transmitted by the collection module in real time, the data processing priority is dynamically adjusted, and "data quality feedback" is fed back to the collection module, such as suggesting that the collection module start a backup sensor when a certain sensor continuously transmits abnormal data, to ensure data continuity. The processed structured data (such as real-time operating parameters, fault information, and warning levels) is pushed to the monitoring display module in a visual form (such as a dashboard, a trend chart, and an alarm pop-up window), to facilitate the intuitive understanding of the power grid state by the operation and maintenance personnel. At the same time, the decision scheme is synchronized to the execution module (such as automatically controlling switches and remotely adjusting equipment parameters), to realize the automatic closed loop of "analysis-decision-execution".

[0052] S4, the collected data is analyzed in depth to realize real-time evaluation of the operating state of the power grid, fault diagnosis, and risk prediction;

[0053] In the present application, it should be pointed out that the data analysis module includes data exploratory analysis, insight into data rules, and clear analysis direction; analysis model construction, establishment of mapping relationship between data and security target; real-time data analysis and reasoning, dynamic adaptation of power grid operation; output decision suggestion, verification and model optimization of results; closed-loop iteration, improvement of analysis accuracy;

[0054] In a specific implementation, the data exploratory analysis step is: first, basic statistical analysis is performed, wherein the basic statistical analysis includes data distribution characteristic analysis, data correlation analysis and time series trend analysis; then, visual analysis is performed, wherein the visual analysis includes static visualization, dynamic visualization and correlation visualization; finally, preliminary identification of abnormal patterns is performed, wherein the abnormal pattern identification includes abnormal identification based on statistical threshold and abnormal identification based on time series deviation, a targeted analysis model is constructed, and model training and optimization are completed using historical data to ensure that the model can accurately adapt to the power grid scenario. The specific steps are: first, determine the analysis target according to the monitoring requirements, set the evaluation index, select the appropriate model according to the characteristics of the analysis target, collect historical data and real-time incremental data, label the data, and divide the training set, validation set and test set in the "time series" or "random sampling" manner. Considering the time series characteristics of power grid data, time series division is preferred to avoid model overfitting caused by data leakage. Based on the exploratory analysis results, the input features are optimized again, the evaluation index of the training set and the validation set is monitored in real time to avoid overfitting. After the model training is completed, the data analysis module enters the real-time running phase, calls the trained model to perform dynamic analysis and reasoning based on the standardized data transmitted by the data processing module in real time, receives the real-time data output by the data processing module through the data flow processing framework, ensures that the data access delay is less than 100 ms, meets the real-time monitoring requirements of the power grid, performs "immediate feature engineering" on the real-time data, and generates consistent features with the model training (such as real-time calculation of the load average of the past 10 minutes, difference features), to ensure that the data format of the input model is consistent with the training data. For example: when analyzing the real-time transformer oil temperature, the "current oil temperature and the average of the past 1 hour" and "oil temperature rising rate" need to be calculated simultaneously as the input of the overheating warning model. According to the data type, set the analysis priority, call the corresponding model for reasoning in parallel for different monitoring targets. When different models analyze the same object, the fusion result improves the accuracy. For example: when analyzing the transformer state, the "oil temperature overheating warning model" outputs "there is an overheating risk (probability 90%) ", and the "partial discharge analysis model" outputs "the partial discharge exceeds the standard (probability 85%) ". Through weighted fusion (weight 0.5), it is concluded that "the transformer insulation fault risk is extremely high (probability 87.5%) ", which avoids single model misjudgment. The model reasoning result is compared with the actual power grid operation situation to calculate the accuracy, recall rate and other indicators. For example: the fault diagnosis model outputs 10 times of fault warning, 9 times of which actually occur (accuracy 90%), while 12 times of actual faults occur (recall rate 75%), and the reasons for the 2 times of faults not recalled need to be analyzed. The "suspected abnormal result" (such as low probability fault warning, fuzzy risk assessment) output by the model needs to be manually reviewed, and the result is judged whether it is accurate or not combined with the experience of the operation and maintenance personnel.For example, the model output is "60% risk of flashover of insulator on a certain line", the operation and maintenance personnel found that the insulator has slight damage during on-site inspection, the verification result is valid, and the case data is supplemented to the model training set, the newly collected historical data is added to the training set in batches, the model is updated by using incremental learning technology to avoid resource waste caused by retraining, when the power grid topology changes (such as adding a new substation or line transformation) or the operation mode changes (such as a large number of new energy grid-connected), the model structure or input features are adjusted.

[0055] As can be seen in the present embodiment, the data analysis module realizes the key transformation from "data" to "decision" through the whole process steps of "exploring analysis to determine direction - model construction to lay foundation - real-time reasoning to make decisions - verification and optimization to promote improvement", the analysis accuracy and response speed directly determine the "early warning ability" and "disposal efficiency" of the intelligent power grid safety monitoring system, and is the core technical support to ensure the transformation of the power grid from "passive repair" to "active prevention".

[0056] S5, the results of the data processing module and the data analysis module are presented to the operation and maintenance personnel in an intuitive way, so as to facilitate real-time mastering of the operation state of the power grid and timely discovery and processing of various safety problems;

[0057] In specific implementation, first, multi-type result data (such as real-time running parameters, fault diagnosis report, risk assessment level, load prediction curve) from the data analysis module needs to be received, and at the same time, the original standardized data (such as electrical quantity and environmental quantity collected by a sensor in real time) of the data processing module is compatible, format adaptation and priority sorting are performed, a foundation is laid for subsequent visual presentation, data of different modules is received through a standardized interface, structured result data is received from the data analysis module, real-time stream data is received from the data processing module, for data protocols of different modules, data is converted into a module-internal unified “key-value pair+metadata” format through a protocol analysis engine, display confusion caused by protocol differences is eliminated, and the accessed data is divided into priorities according to “emergency-important-regular”, key information is ensured to be displayed preferentially, and emergency data can be priority 1, which needs to immediately trigger a pop-up window alarm and sound-light prompt.Important data is priority 2, needs to be displayed in a prominent position on the main interface, updated within 10 seconds, regular data is priority 3, loaded on demand, updated within 1 minute, then the data is divided into "real-time data (≤1 second update)", "quasi-real-time data (1-30 seconds update)", "periodic data (1 minute-1 hour update)", respectively stored in memory cache (real-time data), local database (quasi-real-time data), historical database (periodic data), avoid high-time data occupying too much storage resources, ensure the interface refresh smooth, integrate all related data of the device (real-time voltage, oil temperature, historical fault record, risk assessment result) to form "single device data view", facilitate the operation and maintenance personnel to query the full-dimensional information of a certain device, bind the geographic location information (such as latitude and longitude, tower number) with the data, for example: the icing thickness data of "10kV#3 line#5 tower", the surrounding wind speed data, the historical icing fault record are associated, clicking the tower on the map interface can view all associated data, associate related data of the same event, for example: "#2 line single-phase grounding fault" event, associate the diagnostic results, on-site video monitoring, repair personnel location, power outage range statistical data of the event to form "event traceability view", based on the adapted unified data, the monitoring display module generates diversified visual interfaces according to the needs of different monitoring scenes (substation, transmission line, distribution network), ensures that the operation and maintenance personnel can quickly obtain core information, for continuously changing parameters (voltage, current, load), use "dynamic curve chart" to display time series changes, for load prediction, risk development trend, use "curve chart + interval prediction" to display, for fault alarm, risk warning output by the data analysis module, the monitoring display module ensures that the operation and maintenance personnel can obtain key information in the first time through "hierarchical alarm + multi-channel push", avoids delay disposal, the monitoring display module is not only "information display window", but also "operation interaction platform", needs to support the operation and maintenance personnel to query deep data through the interface, issue control instructions, record disposal process, at the same time, the monitoring display module needs to regularly archive the display data and operation records, and store them to other departments of the power grid (share related data, support cross-department cooperation.

[0058] It can be seen that in the present embodiment, the monitoring display module is not only a key bridge connecting data analysis results and operation and maintenance practices, but also provides multi-dimensional support for power grid safe operation, efficient operation and maintenance and scientific decision-making through visual, interactive and collaborative design. The monitoring display module can centrally present scattered fault information and device abnormal data, and operation and maintenance personnel can directly lock fault points according to interface prompts without checking lines one by one, thereby shortening fault positioning time. The monitoring display module supports "multi-dimensional query + fuzzy association" function, and operation and maintenance personnel do not need to switch and find data in multiple systems and multiple reports. For devices with remote control function (such as circuit breakers and ice melting devices), the module supports "interface instruction issuance + operation feedback real-time display", and operation and maintenance personnel can complete routine operations without going to the site. The monitoring display module provides objective and comprehensive data support for decision-making through multi-dimensional data presentation and trend analysis, and builds a comprehensive risk prevention and control system through hierarchical alarm, multi-channel push and risk visualization. Through data archiving and sharing function, the monitoring display module breaks down departmental information barriers, and can convert massive and complex data analysis results in smart grid into practical information that can be seen, used and coordinated. It not only improves operation efficiency through visualization, guarantees scientific decision-making through data support, and strengthens safety prevention and control through risk early warning, but also provides key support for power grid to realize "safe, efficient and economic" operation through cross-departmental collaboration and fine management and control, and is a core link for smart grid to move from "technology integration" to "practical landing", and an important driving force for promoting the transformation of power grid from "traditional operation and maintenance" to "intelligent operation and maintenance".

[0059] In summary, through the smart grid safety monitoring system, the running state of the power grid can be monitored in real time and comprehensively, potential safety hazards can be found in time, and effective measures can be taken for processing, so as to ensure the safe and stable operation of the power grid, improve the power supply reliability, reduce the loss caused by power outages to the society and economy, and realize real-time monitoring and analysis of power grid operation parameters such as voltage, current, power and frequency, master the running state of the power grid in time, evaluate the health state of power equipment, find equipment fault hidden dangers in advance, realize preventive maintenance of equipment, reduce equipment failure rate, prolong equipment service life, and realize real-time early warning and defense against various security threats faced by the power grid such as network attacks and external damage, and protect the information security and physical security of the power grid.

[0060] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for part of the technical features, and any modification, equivalent substitution, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A smart grid security monitoring system, characterized by, The smart grid security monitoring system comprises: A data acquisition module for acquiring various types of key data in real time during the operation of the smart grid, providing a basis for subsequent analysis and decision-making; A communication transmission module for safely, reliably and quickly transmitting the collected large amount of data to the data processing center; A data processing module for efficiently processing the large amount of data transmitted by the communication transmission module, ensuring data quality, laying a foundation for subsequent analysis, while mining data value and realizing accurate perception of the security state of the power grid; A data analysis module for in-depth analysis of the collected data, realizing real-time evaluation, fault diagnosis and risk prediction of the operation state of the power grid; A monitoring display module for presenting the results of the data processing module and the data analysis module to the operation and maintenance personnel in an intuitive manner, facilitating real-time monitoring of the operation state of the power grid and timely discovery and processing of various security problems.

2. The smart grid security monitoring system of claim 1, wherein: The data acquisition module comprises electrical parameter sensors, device state sensors, environmental monitoring sensors and smart meters.

3. The smart grid security monitoring system of claim 2, wherein: The electrical parameter sensors are used to collect basic electrical parameters such as voltage, current, power and frequency in the power grid, the device state sensors are used to monitor the operation state of power equipment, the environmental monitoring sensors are used to collect relevant information of the power grid operation environment, and the smart meters installed at the user end are used to collect information such as user power consumption, power consumption time and power consumption, realizing real-time monitoring and analysis of user power consumption behavior.

4. The smart grid security monitoring system of claim 1, wherein: The communication mode of the communication transmission module includes optical fiber communication, wireless communication and power line carrier communication.

5. The smart grid security monitoring system of claim 4, wherein: The steps of the optical fiber communication are: first, data source preparation and signal preprocessing are performed, the data to be transmitted of the smart grid security monitoring system is obtained, the collected data is filtered, then the electrical signal is modulated into optical signal, and the transmission link of the optical signal in the optical fiber is selected and deployed, after the optical signal is transmitted to the target node, the optical signal is received and adjusted, that is, after the optical signal is transmitted to the target node, the optical signal needs to be restored to the electrical signal by the optical receiver, after demodulation, the data needs to be verified and distributed to ensure accurate transmission of the data to the target module of the smart grid security monitoring system.

6. The smart grid security monitoring system of claim 4, wherein: The steps of the wireless communication are: first, the data is filtered and preprocessed, the filtered and preprocessed data can reduce the transmission load and ensure data security, the wireless communication converts the preprocessed electrical signal into a radio frequency signal that can be transmitted in the air, then the transmission channel of the wireless signal is selected, that is, the appropriate channel is selected according to the position of the monitoring node, after the wireless signal is transmitted to the target node, the signal reception and restoration are completed by the wireless receiving module, that is, the wireless signal is received and demodulated, the digital signal output after demodulation is consistent with the preprocessed digital signal at the transmitting end, preparing for subsequent data verification, and after demodulation is completed, the data integrity needs to be verified and distributed to the corresponding module to ensure effective use of the data.

7. The smart grid security monitoring system of claim 1, wherein: The data processing module includes four stages, and the four stages are: the first stage: data receiving and preprocessing, which can guarantee data quality and lay a foundation for subsequent data analysis; the second stage: data fusion and feature extraction, integrating multi-source information, realizing information complementation through data fusion, and then extracting key features to support subsequent analysis; the third stage: deep analysis, realizing accurate diagnosis of power grid fault and real-time evaluation of safety risk through machine learning, deep learning and other algorithms; the fourth stage: result output and feedback optimization, delivering effective information and continuously improving performance.

8. The smart grid security monitoring system of claim 1, wherein: The data analysis module includes data exploratory analysis, which inspects data rules and determines analysis direction; analysis model construction, which establishes the mapping relationship between data and safety targets; real-time data analysis and reasoning, which dynamically adapts to power grid operation; output decision suggestion, which verifies results and optimizes models; closed-loop iteration, which improves analysis accuracy.

9. The smart grid security monitoring system of claim 8, wherein: The data exploratory analysis step is: first, basic statistical analysis, including data distribution feature analysis, data correlation analysis and time series trend analysis; then, visual analysis, including static visualization, dynamic visualization and correlation visualization; finally, preliminary identification of abnormal patterns, including abnormal identification based on statistical threshold and abnormal identification based on time series deviation.

10. The smart grid security monitoring system of claim 1, wherein: The step of the monitoring display module is: first, receiving data of different modules through a standardized interface, converting the data into a unified format within the module through a protocol analysis engine according to the data protocol of different modules, then classifying the data according to priority and timeliness, and matching according to the correlation of the data, based on the adapted unified data, the monitoring display module generates diversified visual interfaces according to the needs of different monitoring scenarios, ensures that operation and maintenance personnel can quickly obtain core information, classifies and pushes the fault alarms and risk warnings output by the data analysis module, ensures timely response, finally archives and stores the display data and operation records, and shares relevant data with other departments of the power grid to support cross-department collaboration.

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