Flexible regulating unit data transmission method for smart grid

Abstract

The application provides a flexible adjustment unit data transmission method for a smart power grid, and belongs to the technical field of power grid management. The flexible adjustment unit data transmission method for the smart power grid comprises the following steps: collecting electric energy parameters of a special transformer user in real time, wherein the electric energy parameters comprise phase voltage, current, active power, power factor and frequency; performing edge computing processing on the electric energy parameters to formulate a load prediction strategy, a load identification strategy, an electric energy quality monitoring strategy and a flexible regulation strategy; and reporting the processed electric energy parameters to an interactive management master station through an encrypted communication mode. The flexible adjustment unit data transmission method for the smart power grid can guarantee the safety of data transmission, realize fine management of the load of the smart power grid, optimize electric energy quality and enhance dynamic regulation capability, and maintain the stability of the power grid system.

Description

Technical Field

[0001] This application belongs to the field of power grid management technology, specifically relating to a data transmission method for flexible regulation units in smart grids. Background Technology

[0002] The smart grid, also known as "Grid 2.0," is a modern power supply and management system. It is based on an integrated, high-speed, two-way communication network and integrates advanced sensing and measurement technologies, equipment technologies, control methods, and decision support system technologies. The design of the smart grid aims to achieve multiple objectives, including grid reliability, safety, economy, efficiency, environmental friendliness, and operational safety.

[0003] Data transmission in flexible regulation units is a core component of efficient energy management and dynamic control in smart grids. It is of great significance for improving grid stability, optimizing energy distribution, promoting the grid connection of renewable energy, and enhancing user experience.

[0004] However, during data transmission, there is a risk of data being illegally intercepted or tampered with, affecting the security and integrity of data transmission and hindering the refined management and dynamic control of the smart grid. Summary of the Invention

[0005] To address at least one of the technical problems existing in the background art, this application provides a data transmission method for flexible regulation units in smart grids, which can ensure the security of data transmission, realize fine load management, power quality optimization and enhanced dynamic regulation capabilities of smart grids, and maintain the stability of the power grid system.

[0006] The technical solution adopted in this application is as follows:

[0007] The first aspect of this application provides a data transmission method for a flexible regulation unit in a smart grid, comprising:

[0008] Real-time collection of electrical energy parameters from dedicated transformer users, including phase voltage, current, active power, power factor, and frequency;

[0009] Edge computing processing is performed on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies;

[0010] The processed electrical energy parameters are reported to the interactive management master station via encrypted communication.

[0011] According to the data transmission method for a flexible regulation unit for smart grids provided in the first aspect of this application, by acquiring power parameters in real time at high frequency, the system can instantly observe the grid's operating status, promptly detect and respond to abnormal situations in the grid, and enhance the system's stability and reliability. The integration of edge computing brings data processing closer to the data source, reducing data transmission latency and improving processing efficiency. The formulation of load forecasting and identification strategies helps to rationally allocate resources, optimize grid load distribution, reduce power loss, and improve energy utilization efficiency. Power quality monitoring strategies can monitor grid operating quality in real time, promptly detect and correct problems such as voltage fluctuations and harmonic pollution, ensure power supply quality, and improve the electricity experience for end users. Flexible regulation strategies, based on real-time data analysis results, can dynamically adjust the grid's operating status, better adapt to the volatility of renewable energy, promote the efficient access and utilization of clean energy, and support green and low-carbon transformation. The use of encrypted communication ensures data security during transmission, prevents illegal interception or tampering, protects user privacy, and enhances user trust in the smart grid. An efficient data reporting mechanism provides rich real-time data support for the interactive management master station, laying a solid data foundation for the master station's intelligent decision-making, automated operation and maintenance, and long-term planning, and promoting the development of the power grid towards a smarter and more autonomous direction. In summary, the data transmission method for flexible regulation units in smart grids provided in this application can ensure the security of data transmission, realize refined load management, power quality optimization, and enhanced dynamic regulation capabilities of the smart grid, and maintain the stability of the power grid system.

[0012] According to one embodiment of this application, the method further includes:

[0013] The electrical energy parameters are frozen and stored at the minute level;

[0014] Record abnormal event information, including overvoltage, overcurrent, reverse power, power failure, and control circuit abnormality.

[0015] According to one embodiment of this application, the step of freezing and storing the electrical energy parameters at the minute level specifically involves:

[0016] Define the freeze period;

[0017] The electrical energy parameters acquired during the freeze period are integrated into a data frame;

[0018] The data frames are stored according to a preset format.

[0019] According to one embodiment of this application, the real-time acquisition of electrical energy parameters of dedicated transformer users specifically includes:

[0020] The three-phase current value or unidirectional current value is monitored in real time by current transformer, and the three-phase voltage value or unidirectional voltage value is monitored in real time by voltage transformer.

[0021] Based on voltage and current values, the active power, reactive power, and power factor are calculated in real time through an internal calculation module.

[0022] According to one embodiment of this application, the step of performing edge computing processing on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies specifically includes:

[0023] The electrical energy parameters are subjected to noise reduction, missing value filling, and standardization.

[0024] Acquire historical data, extract time series features, and build a strategy prediction model;

[0025] Spectral analysis and pattern recognition are performed on the current and voltage values ​​to extract the feature vector of the user load;

[0026] The feature vectors are classified using clustering algorithms or neural networks to determine the user load classification results;

[0027] Based on the user load classification results, the load forecasting strategy, the load identification strategy, the power quality monitoring strategy, and the flexible control strategy are generated.

[0028] According to one embodiment of this application, the step of reporting the processed power parameters to the interactive management master station via encrypted communication specifically includes:

[0029] The electrical energy parameters are organized into a data packet according to a preset format. The data packet includes a data header, a data body, and a checksum.

[0030] Determine the encryption algorithm and key;

[0031] The data packet is encrypted using the encryption algorithm and the key, and then converted into ciphertext.

[0032] According to one embodiment of this application, the step of reporting the processed power parameters to the interactive management master station via encrypted communication further includes:

[0033] Establish an encrypted communication channel and report the encrypted data packet to the interactive management master station through the encrypted communication channel;

[0034] During the reporting process, the data transmission status is monitored.

[0035] According to one embodiment of this application, the method further includes:

[0036] In the edge computing process, abnormal data in the power parameters are monitored in real time;

[0037] The abnormal data is analyzed using an anomaly detection algorithm;

[0038] Mark abnormal data points and generate alarm commands.

[0039] A second aspect of this application provides a data transmission device for a flexible regulation unit in a smart grid, comprising:

[0040] The parameter acquisition module is suitable for real-time acquisition of electrical energy parameters of dedicated transformer users, including phase voltage, current, active power, power factor and frequency;

[0041] The calculation and processing module is suitable for performing edge computing processing on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies.

[0042] The reporting module is adapted to report the processed power parameters to the interactive management master station via encrypted communication.

[0043] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the data transmission method for a flexible regulation unit for a smart grid as described in any of the first aspects of this application. Attached Figure Description

[0044] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0045] Figure 1 A schematic flowchart illustrating the data transmission method for a flexible regulation unit in a smart grid, as provided in an embodiment of this application;

[0046] Figure 2 A schematic diagram of the structure of a data transmission device for a flexible regulation unit for a smart grid provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0048] in,

[0049] 110. Parameter acquisition module; 120. Calculation and processing module; 130. Reporting module;

[0050] 810, Processor; 820, Communication interface; 830, Memory; 840, Communication bus. Detailed Implementation

[0051] To more clearly illustrate the overall concept of this application, a detailed explanation is provided below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below. It should be noted that, unless otherwise specified, the embodiments of this application and the features thereof can be combined with each other.

[0053] Furthermore, it should be understood in the description of this application that the terms "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0054] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0055] In this application, unless otherwise expressly specified and limited, the "above" or "below" of the second feature can mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0056] like Figure 1As shown, a first aspect of this application provides a data transmission method for a flexible regulation unit in a smart grid, comprising:

[0057] Step 100: Collect the electrical energy parameters of the dedicated transformer user in real time. The electrical energy parameters include phase voltage, current, active power, power factor and frequency.

[0058] Step 200: Perform edge computing processing on the power parameters to formulate load forecasting strategy, load identification strategy, power quality monitoring strategy and flexible control strategy.

[0059] Step 300: Report the processed power parameters to the interactive management master station via encrypted communication.

[0060] In step 100, the power grid is typically a three-phase AC system. Phase voltage refers to the voltage between each phase line and the neutral line (or ground line). Real-time monitoring of phase voltage helps assess grid stability and power quality, and promptly detects problems such as voltage imbalance. Current is a physical quantity that measures the intensity of electrical energy flow. Real-time monitoring of current helps understand real-time changes in user load, which is crucial for power distribution and fault diagnosis. Similarly, current is also divided into three-phase current, corresponding to the three phases of the power grid. Active power is the actual power used for work, directly related to energy conversion and actual consumption, and is the main basis for calculating electricity charges. Real-time monitoring of active power is crucial for load management, electricity charge calculation, and power balance analysis. Power factor reflects the efficiency of energy use and is the ratio of active power to apparent power. A high power factor indicates high energy efficiency, and vice versa. Real-time monitoring of the power factor is critical for improving power quality and system efficiency. In AC systems, frequency is a physical quantity that measures the number of times the current direction changes per second; the standard frequency in my country is 50Hz. Frequency stability is crucial for the normal operation of power grid equipment, and real-time frequency monitoring helps maintain the frequency stability and synchronous operation of the power grid.

[0061] Real-time acquisition of these power parameters provides an accurate and real-time data foundation for subsequent edge computing processing, load forecasting, power quality monitoring, flexible control strategy formulation, and rapid response to abnormal events.

[0062] In step 200, edge computing is used to perform time-series analysis and seasonal pattern recognition on power parameters, and combined with external factors (such as weather forecasts and holidays) to predict the load change trend of the power grid over a future period. This helps to allocate resources in advance and ensure the stability and efficiency of power supply.

[0063] Load identification strategies utilize waveform analysis, spectrum analysis, and pattern recognition technologies of current and voltage to identify different types of load characteristics from real-time data, such as distinguishing between industrial loads and residential loads, providing a basis for refined management and demand-side response.

[0064] Power quality monitoring strategies involve real-time monitoring of power quality parameters such as voltage fluctuations, frequency deviations, and harmonic content. Edge computing can quickly identify situations where power quality deteriorates and promptly activate preset compensation measures or alarm mechanisms to ensure the power quality of the power grid.

[0065] The flexible control strategy refers to the ability of edge computing to dynamically adjust the operating parameters of the power grid based on the above analysis results. This includes adjusting the output of distributed energy sources, the charging and discharging strategies of energy storage devices, and the load adjustment of users participating in demand response, in order to achieve a balance between power grid supply and demand, optimize the efficiency of power use, and promote the efficient consumption of renewable energy.

[0066] The advantage of edge computing is that it can process data at close range, reduce data transmission latency, improve response speed, and alleviate the pressure on cloud computing.

[0067] In step 300, to protect the data from interception or tampering by unauthorized third parties during transmission, encryption technology is used to encrypt the data. This typically involves using encryption algorithms such as AES (Advanced Encryption Standard), RSA, and TLS (Transport Layer Security). The data is converted into ciphertext before being sent, and only the recipient with the correct key can decrypt and restore the data, ensuring data security.

[0068] The interactive management master station is the central control unit of the smart grid, responsible for receiving and processing data reported from various edge computing units, and performing comprehensive analysis and decision-making. These processed power parameters include load forecast results, load identification information, power quality reports, and control strategy recommendations, which are key inputs for the master station to perform grid dispatching, optimized operation, fault handling, and strategy formulation.

[0069] Before data transmission, the system establishes a secure communication channel, which involves the SSL / TLS handshake protocol to verify the identities of both communicating parties and ensure the security of data during transmission. Encrypted communication is not limited to the data itself, but also includes the authentication of both communicating parties and the verification of the integrity of data transmission.

[0070] In this way, the smart grid can not only efficiently utilize edge computing to process real-time data, but also centrally manage the status information of all subsystems while ensuring information security, providing a solid guarantee for the efficient operation, flexible control, and rapid fault response of the smart grid.

[0071] According to the data transmission method for a flexible regulation unit for smart grids provided in the first aspect of this application, by acquiring power parameters in real time at high frequency, the system can instantly observe the grid's operating status, promptly detect and respond to abnormal situations in the grid, and enhance the system's stability and reliability. The integration of edge computing brings data processing closer to the data source, reducing data transmission latency and improving processing efficiency. The formulation of load forecasting and identification strategies helps to rationally allocate resources, optimize grid load distribution, reduce power loss, and improve energy utilization efficiency. Power quality monitoring strategies can monitor grid operating quality in real time, promptly detect and correct problems such as voltage fluctuations and harmonic pollution, ensure power supply quality, and improve the electricity experience for end users. Flexible regulation strategies, based on real-time data analysis results, can dynamically adjust the grid's operating status, better adapt to the volatility of renewable energy, promote the efficient access and utilization of clean energy, and support green and low-carbon transformation. The use of encrypted communication ensures data security during transmission, prevents illegal interception or tampering, protects user privacy, and enhances user trust in the smart grid. An efficient data reporting mechanism provides rich real-time data support for the interactive management master station, laying a solid data foundation for the master station's intelligent decision-making, automated operation and maintenance, and long-term planning, and promoting the development of the power grid towards a smarter and more autonomous direction. In summary, the data transmission method for flexible regulation units in smart grids provided in this application can ensure the security of data transmission, realize refined load management, power quality optimization, and enhanced dynamic regulation capabilities of the smart grid, and maintain the stability of the power grid system.

[0072] In some embodiments of this application, the method further includes:

[0073] Power parameters are frozen and stored at the minute level;

[0074] Record abnormal event information, including overvoltage, overcurrent, reverse power, power failure, and control circuit abnormalities.

[0075] Minute-level freeze storage refers to the process where the system "freezes" and stores the current power parameters every minute. This frozen data includes instantaneous values ​​of key parameters such as phase voltage, current, active power, power factor, and frequency. This minute-level data recording method helps construct detailed time series of power usage, providing comprehensive historical data support for subsequent load analysis, power quality assessment, trend prediction, and fault tracing. By defining a fixed freeze period (e.g., every minute), the system can effectively manage storage resources while ensuring data continuity and integrity.

[0076] In the process of recording abnormal event information, the system not only continuously monitors power parameters but also automatically identifies and records any abnormal events that deviate from the normal operating range. These abnormal events include, but are not limited to, overvoltage (voltage exceeding safety thresholds), overcurrent (current exceeding rated values), reverse power (current or power flow not in the expected direction), power outage (power supply interruption), and control loop anomalies (errors or faults in the monitoring and control system). Recording this abnormal information is crucial for timely detection of potential grid problems, accident prevention, rapid response, and corrective action. Through a systematic abnormal event log, maintenance personnel can quickly locate the source of problems and take targeted measures, effectively improving the stability and security of the power grid.

[0077] In some embodiments of this application, electrical energy parameters are frozen and stored at the minute level, specifically as follows:

[0078] Define the freeze period;

[0079] The electrical energy parameters acquired during the freeze period will be integrated into a data frame;

[0080] The data frames are stored according to a preset format.

[0081] A fixed time interval, typically every minute, is set as the data freeze and storage cycle. At the end of this cycle, the system captures and records the current electrical parameters, such as voltage, current, and power. The minute-level cycle selection aims to provide a sufficient frequency of data points to accurately reflect real-time changes in grid operation without excessively burdening data storage and processing. At the end of each freeze cycle, the system integrates the continuously monitored electrical parameter values ​​into a dataset called a "data frame." The data frame contains instantaneous readings of all key electrical parameters within that cycle, such as phase voltage, current, active power, power factor, and frequency. Integrating the data frame facilitates management and storage, presenting the data in a structured form for easy subsequent analysis and querying. Finally, the system stores the data frame in a predefined format, such as CSV, JSON, or other specific database formats, to a hard drive, database, or cloud storage service. The preset storage format ensures data consistency and compatibility, facilitating data exchange and sharing between different systems and tools. In addition, reasonable data organization and compression strategies are also important considerations in this process, in order to optimize the use of storage space and speed up data retrieval.

[0082] In some embodiments of this application, the power parameters of dedicated transformer users are collected in real time, specifically as follows:

[0083] The three-phase current value or unidirectional current value is monitored in real time by current transformer, and the three-phase voltage value or unidirectional voltage value is monitored in real time by voltage transformer.

[0084] Based on voltage and current values, the active power, reactive power, and power factor are calculated in real time through an internal calculation module.

[0085] Current transformers are used to measure current in a circuit. In a three-phase system, three current transformers are typically used to monitor the three-phase current, while a single-phase system requires only one. Current transformers operate on the principle of magnetic coupling, allowing for safe and accurate current measurement even under high current conditions, without being directly connected to the circuit, thus ensuring measurement safety.

[0086] Voltage transformers convert high or medium voltage levels to lower voltages (such as 100V or lower) for safe measurement and monitoring. Similarly, three voltage transformers are needed to monitor the three-phase voltage in a three-phase system, while only one is required in a single-phase system. Voltage transformers are also based on the principle of electromagnetic induction, providing accurate voltage readings for the system.

[0087] Active power represents the power actually used for work, and is the product of voltage, current, and the cosine of the power factor angle. It reflects the actual electrical energy consumed and is an important basis for billing.

[0088] Reactive power is related to the energy exchange between electric and magnetic fields, reflecting the ability of inductors and capacitors in a circuit to store and release energy, and has a significant impact on the power quality of the power grid.

[0089] Power factor is a measure of the ratio of active power to apparent power in a circuit, reflecting the efficiency of power supply utilization. A high power factor means high energy efficiency and is an important parameter for evaluating the efficiency of electrical equipment and systems.

[0090] Through this series of real-time monitoring and calculations, the smart grid can keep track of the electricity usage of dedicated transformer users in real time, providing necessary data support for load management, power quality optimization, fault early warning, and electricity cost calculation.

[0091] In some embodiments of this application, edge computing processing is performed on power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies, specifically as follows:

[0092] The electrical energy parameters are denoised, missing values ​​are filled in, and standardized.

[0093] Acquire historical data, extract time series features, and build a strategy prediction model;

[0094] Spectral analysis and pattern recognition are performed on the current and voltage values ​​to extract the feature vector of the user load;

[0095] Clustering algorithms or neural networks are used to classify feature vectors to determine user load classification results;

[0096] Based on the user load classification results, load forecasting strategy, load identification strategy, power quality monitoring strategy and flexible control strategy are generated.

[0097] Since the actual collected data contains noise caused by measurement errors or external interference, noise reduction is necessary to ensure the accuracy of subsequent analysis. Missing value imputation addresses data gaps caused by various reasons, using appropriate methods (such as interpolation, prediction imputation based on historical data, etc.) to fill in missing values, ensuring data continuity and integrity. Different parameters may have differences in units and scales; standardization (such as z-score standardization) unifies all parameters to the same scale, facilitating comparison and model training.

[0098] When acquiring historical data, electrical parameters from past time periods are collected as the basis for model training. Further, features reflecting load change trends, periodicity, and seasonality, such as mean, variance, and autocorrelation coefficient, are extracted from historical data. Models, such as ARIMA and LSTM time series forecasting models, are built using these extracted features for load forecasting. Spectral analysis, including Fourier transforms, of current and voltage signals identifies different frequency components, aiding in understanding load characteristics, such as harmonic content and fluctuation characteristics. Based on the spectral analysis results, specific load patterns or abnormal behaviors are identified, and feature vectors reflecting user load characteristics are extracted. These feature vectors are used as input, and user loads are classified using methods such as K-means clustering, hierarchical clustering, or deep learning neural networks to identify different types of electricity consumption behavior or load types. Based on the user load classification, customized load forecasting strategies (predicting future load changes), load identification strategies (clarifying the specific composition and characteristics of various load types), power quality monitoring strategies (preventing and resolving power quality problems), and flexible control strategies (dynamically adjusting power supply strategies to adapt to load changes and improve grid efficiency and reliability) are generated.

[0099] In some embodiments of this application, the processed power parameters are reported to the interactive management master station via encrypted communication, specifically as follows:

[0100] The electrical energy parameters are organized into a data packet according to a preset format. The data packet includes a data header, a data body, and a checksum.

[0101] Determine the encryption algorithm and key;

[0102] Data packets are encrypted using encryption algorithms and keys, and then converted into ciphertext.

[0103] First, the processed electrical parameters need to be formatted according to a predetermined format. This typically involves arranging the parameters in a specific order and adding necessary identifiers or metadata to form a structured data packet. The data packet usually contains three main parts:

[0104] Data header: Contains metadata about the data packet, such as the packet type, length, source address, destination address, etc., to facilitate parsing and processing by the receiver.

[0105] Data body: The core content, namely the pre-processed electrical energy parameter information.

[0106] Checksum: To ensure that no errors occur during data transmission, a checksum (such as CRC checksum, MD5 digest, etc.) is usually calculated and appended to the end of the data packet. The receiving end can use this to verify the integrity of the data.

[0107] Choose an appropriate encryption algorithm based on security requirements and system configuration. Common encryption algorithms include AES (Advanced Encryption Standard), RSA, and DES. AES is often used for symmetric encryption of data, while RSA can be used for key exchange in asymmetric encryption.

[0108] The key is crucial in the encryption and decryption process and must be kept secure. For symmetric encryption algorithms, the sender and receiver must share the same key; while asymmetric encryption involves the use of public and private keys. The generation, distribution, and updating of keys must follow strict security policies.

[0109] The entire data packet (including the header, body, and checksum) is encrypted using a defined encryption algorithm and key. The encryption process transforms plaintext data into ciphertext that cannot be directly deciphered, protecting the data from unauthorized interception and interpretation.

[0110] After encryption, the encrypted data packet is uploaded to the interactive management main station through a secure channel. This channel uses a dedicated network, HTTPS protocol, or other secure communication protocols to ensure the security of data transmission.

[0111] Through this process, even if the data is intercepted by a third party during transmission, the encryption protection prevents the third party from directly reading the original power parameters, thus effectively protecting the privacy and security of the data.

[0112] In some embodiments of this application, the processed power parameters are reported to the interactive management master station via encrypted communication, and the method further includes:

[0113] Establish an encrypted communication channel and report the encrypted data packets to the interactive management main station through the encrypted communication channel;

[0114] During the reporting process, the data transmission status is monitored.

[0115] Before actual data reporting, the primary task is to ensure the security of the communication link. This is typically achieved by establishing an encrypted communication channel, such as using SSL / TLS protocols (commonly used for HTTPS communication) or more specialized industrial-grade security protocols like DTLS (the TLS version for resource-constrained devices). This process involves server authentication and key exchange, ensuring that data remains encrypted throughout transmission, effectively preventing man-in-the-middle attacks and data eavesdropping. The establishment of an encrypted channel provides a secure "tunnel" for reporting power parameters, ensuring that even if data passes through multiple nodes during network transmission, its content cannot be easily deciphered.

[0116] During the reporting process, real-time monitoring of data transmission status is crucial, involving tracking all stages such as data packet sending, transmission, and confirmation of arrival at the interactive management master station. This step may include, but is not limited to, the following aspects:

[0117] Packet acknowledgment: The ACK mechanism of the TCP / IP protocol or a specific application layer protocol is used to confirm whether a data packet has arrived successfully, ensuring reliable data transmission.

[0118] Transmission rate and latency monitoring: Monitoring the actual rate and latency of data transmission helps to detect network congestion or other problems that affect transmission efficiency in a timely manner.

[0119] Error detection and retransmission: When a data transmission error is detected or no acknowledgment is received after a timeout, the retransmission mechanism is automatically triggered to ensure the complete delivery of data.

[0120] Security monitoring: Continuously monitor the communication link to check for any attempts to illegally intrude or interfere with the communication, and ensure the effectiveness and security of the encrypted channel.

[0121] In summary, by establishing an encrypted communication channel and implementing real-time monitoring of data transmission status, not only is the security of power parameter data strengthened, but the reliability and efficiency of data reporting are also improved. This ensures that the interactive management master station can accurately receive the processed power parameter information, providing a solid foundation for subsequent analysis and management decisions.

[0122] In some embodiments of this application, the method further includes:

[0123] In edge computing processing, abnormal data in power parameters are monitored in real time;

[0124] Analyze abnormal data using anomaly detection algorithms;

[0125] Mark abnormal data points and generate alarm commands.

[0126] In real-time monitoring of abnormal data in electrical parameters, preliminary analysis is conducted immediately after data acquisition to identify data points that deviate significantly from normal patterns. This can be achieved through threshold comparisons, statistical methods (such as analysis of standard deviation), or learning models based on historical data. Real-time monitoring ensures a rapid response to any unusual data changes that may be caused by equipment failure, grid anomalies, or measurement errors.

[0127] Once the monitoring system identifies potentially anomalous data, it will then employ anomaly detection algorithms for in-depth analysis. These algorithms include machine learning methods (such as cluster analysis, isolation forests, and support vector machines) or statistical methods (such as Grubbs tests and Z-scores). They can more accurately determine which data deviates from normal behavior patterns based on the inherent patterns and relationships within the data, rather than simply comparing it to preset thresholds.

[0128] After analysis, the system will clearly mark confirmed abnormal data points, a crucial step for subsequent fault diagnosis and rapid response. Simultaneously, the system will automatically generate alert commands, which can be emails or SMS notifications sent to operations and maintenance personnel, or direct triggering of early warning processes within the automated management system. Alert commands should contain sufficient information, such as the time period of the anomaly, the specific abnormal parameter value, and its potential impact, so that recipients can quickly make decisions and take appropriate measures.

[0129] like Figure 2 As shown, a second aspect of this application provides a data transmission device for a flexible regulation unit in a smart grid, comprising:

[0130] The parameter acquisition module 110 is suitable for real-time acquisition of the electrical energy parameters of dedicated transformer users, including phase voltage, current, active power, power factor and frequency;

[0131] The calculation and processing module 120 is adapted to perform edge computing processing on the power parameters in order to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies and flexible control strategies.

[0132] The reporting module 130 is adapted to report the processed power parameters to the interactive management master station via encrypted communication.

[0133] The data transmission device for a flexible regulation unit for a smart grid provided in the second aspect of this application can realize the data transmission method for a flexible regulation unit for a smart grid in any of the embodiments of the first aspect above. Therefore, it can achieve any of the technical effects in the data transmission method for a flexible regulation unit for a smart grid above, which will not be elaborated here.

[0134] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the data transmission method for a flexible regulation unit for a smart grid as described in any of the first aspects above.

[0135] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute the data transmission method for a flexible regulation unit for a smart grid according to any embodiment of the first aspect described above, the method including:

[0136] Step 100: Collect the electrical energy parameters of the dedicated transformer user in real time. The electrical energy parameters include phase voltage, current, active power, power factor and frequency.

[0137] Step 200: Perform edge computing processing on the power parameters to formulate load forecasting strategy, load identification strategy, power quality monitoring strategy and flexible control strategy.

[0138] Step 300: Report the processed power parameters to the interactive management master station via encrypted communication.

[0139] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the data transmission method for flexible regulation units of smart grids provided by the above methods, the method comprising:

[0141] Step 100: Collect the electrical energy parameters of the dedicated transformer user in real time. The electrical energy parameters include phase voltage, current, active power, power factor and frequency.

[0142] Step 200: Perform edge computing processing on the power parameters to formulate load forecasting strategy, load identification strategy, power quality monitoring strategy and flexible control strategy.

[0143] Step 300: Report the processed power parameters to the interactive management master station via encrypted communication.

[0144] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data transmission method for a flexible regulation unit in a smart grid provided by the methods described above, the method comprising:

[0145] Step 100: Collect the electrical energy parameters of the dedicated transformer user in real time. The electrical energy parameters include phase voltage, current, active power, power factor and frequency.

[0146] Step 200: Perform edge computing processing on the power parameters to formulate load forecasting strategy, load identification strategy, power quality monitoring strategy and flexible control strategy.

[0147] Step 300: Report the processed power parameters to the interactive management master station via encrypted communication.

[0148] For any parts not mentioned in this application, existing technologies may be used or referenced.

[0149] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0150] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A flexible regulating unit data transmission method for smart grid, characterized in that, include: Real-time collection of electrical energy parameters from dedicated transformer users, including phase voltage, current, active power, power factor, and frequency; Edge computing processing is performed on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies. In this process, spectrum analysis and pattern recognition are performed on the current and voltage values ​​to extract the feature vectors of user loads. Clustering algorithms or neural networks are used to classify the feature vectors to determine the user load classification results. The processed power parameters are reported to the interactive management main station via encrypted communication. The electrical energy parameters are frozen and stored at the minute level; Record abnormal event information, including overvoltage, overcurrent, reverse power, power failure, and control circuit abnormality.

2. The smart grid oriented flexible regulation unit data transfer method according to claim 1, characterized in that, The specific steps of freezing and storing the electrical energy parameters at the minute level are as follows: Define the freeze period; The electrical energy parameters acquired during the freeze period are integrated into a data frame; The data frames are stored according to a preset format.

3. The data transmission method for flexible regulation units in smart grids according to claim 1, characterized in that, The real-time acquisition of electrical energy parameters from dedicated transformer users specifically includes: The three-phase current value or unidirectional current value is monitored in real time by current transformer, and the three-phase voltage value or unidirectional voltage value is monitored in real time by voltage transformer. Based on voltage and current values, the active power, reactive power, and power factor are calculated in real time through an internal calculation module.

4. The data transmission method for flexible regulation units in smart grids according to claim 3, characterized in that, The step of performing edge computing processing on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies, and flexible control strategies specifically involves: The electrical energy parameters are subjected to noise reduction, missing value filling, and standardization. Acquire historical data, extract time series features, and build a strategy prediction model; Based on the user load classification results, the load forecasting strategy, the load identification strategy, the power quality monitoring strategy, and the flexible control strategy are generated.

5. The data transmission method for flexible regulation units in smart grids according to claim 4, characterized in that, The process of reporting the processed power parameters to the interactive management master station via encrypted communication is as follows: The electrical energy parameters are organized into a data packet according to a preset format. The data packet includes a data header, a data body, and a checksum. Determine the encryption algorithm and key; The data packet is encrypted using the encryption algorithm and the key, and then converted into ciphertext.

6. The data transmission method for flexible regulation units in smart grids according to claim 5, characterized in that, The step of reporting the processed power parameters to the interactive management master station via encrypted communication also includes: Establish an encrypted communication channel and report the encrypted data packet to the interactive management master station through the encrypted communication channel; During the reporting process, the data transmission status is monitored.

7. The data transmission method for a flexible regulation unit for a smart grid according to any one of claims 1 to 6, characterized in that, The method also includes: In the edge computing process, abnormal data in the power parameters are monitored in real time; The abnormal data is analyzed using an anomaly detection algorithm; Mark abnormal data points and generate alarm commands.

8. A data transmission device for a flexible regulation unit in a smart grid, characterized in that, include: The parameter acquisition module is suitable for real-time acquisition of electrical energy parameters of dedicated transformer users, including phase voltage, current, active power, power factor and frequency; The calculation and processing module is adapted to perform edge computing processing on the power parameters to formulate load forecasting strategies, load identification strategies, power quality monitoring strategies and flexible control strategies. In this module, the current value and the voltage value are subjected to spectrum analysis and pattern recognition to extract the feature vector of the user load. The feature vector is then classified using a clustering algorithm or a neural network to determine the user load classification result. The reporting module is adapted to report the processed power parameters to the interactive management master station via encrypted communication.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the data transmission method for flexible regulation units for smart grids as described in any one of claims 1 to 7.

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