Intelligent substation remote control system and data processing method thereof

Abstract

The invention discloses an intelligent substation remote control system and a data processing method thereof, and the system comprises a remote control terminal which is used for transmitting an operation instruction, an instruction receiving module which is used for receiving the operation instruction from the remote control terminal, and an instruction analysis module which is used for analyzing the operation instruction to determine a corresponding operation parameter, the command generation module is used for generating a substation control command based on the operation parameters, and the control command sending module is used for sending the control command to the intelligent substation and executing corresponding operation; according to the intelligent substation remote control system and the data processing method thereof, accurate synchronization of equipment state information is guaranteed, and the asynchronous phenomenon is avoided, so that the overall operation efficiency and stability of the system are improved, the adaptability and expansion capability of the system are improved, and the requirement for efficient operation of a modern intelligent substation is met.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and in particular to an intelligent substation remote control system and a data processing method thereof. Background Art

[0002] In modern smart substation systems, remote control methods are a key technology. Through advanced information and communication technology, operators can remotely monitor and control the status of equipment in substations. This technology plays an important role in ensuring the stability and efficiency of power grid operation, especially in high-voltage direct current transmission systems. However, existing remote control methods have some technical challenges in actual operation.

[0003] First, in the HVDC transmission system, communication delay has a great impact on the real-time control of the system. Due to the delay of data transmission, the accuracy and timeliness of remote control cannot meet the actual operation requirements, especially in severe weather conditions, such as storms and thunderstorms. These environmental factors will further deteriorate the quality of the communication channel and increase the error rate in data transmission, thus affecting the stable operation of the system. Secondly, the remote control of smart substations also faces network security risks. Since the system transmits control instructions through the network, if the network protection mechanism is not perfect, the control signal may be tampered or intercepted by illegal intruders, thereby threatening the security and reliability of the entire power grid. Existing network security measures may have shortcomings in dealing with these potential threats and cannot effectively resist high-level network attacks. In addition, there are a large number of devices in smart substations, and large-scale data concurrent processing is another technical problem. During the remote control process, if the system's data throughput capacity is limited, the response time of the command may be too long, thus affecting the real-time control of the equipment. At the same time, in the process of rapid switching between multiple devices, if there is a lack of effective synchronization mechanism, there will be a lack of synchronization, resulting in inaccurate equipment status information, affecting the overall system operation efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a remote control system for a smart substation and a data processing method thereof, so as to solve the problems in the prior art that in a high-voltage direct current transmission system, communication delay has a great impact on the real-time control of the system, the remote control of the smart substation also faces network security risks, there are a large number of devices in the smart substation, and large-scale data concurrent processing is difficult; at the same time, in the process of rapid switching between multiple devices, if there is a lack of an effective synchronization mechanism, asynchrony will occur, resulting in inaccurate device status information, affecting the operating efficiency of the overall system.

[0005] To achieve the above object, the present invention provides the following technical solution: a remote control system for an intelligent substation, the system comprising: Remote control terminal, used to send operation instructions; A command receiving module connected to the remote control terminal, used to receive operation commands from the remote control terminal; An instruction parsing module connected to the instruction receiving module, used to parse the operation instruction to determine the corresponding operation parameters; A command generation module connected to the instruction parsing module, used to generate a substation control command based on the operation parameters; A control command sending module connected to the command generating module is used to send control commands to the smart substation to perform corresponding operations; The command generation module generates a substation control command based on the operation parameter, including: When the equipment is in a situation where the load changes greatly, the control command automatically adjusts the operating parameters. The specific formula is: , in, Indicates the generated control command, represents the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time; The control command sending module sends a control command to the smart substation, and performs corresponding operations including: The generated control command is sent to each device in the smart substation through the control command sending module, and the command is executed locally at the substation edge computing node. The edge node receives the remote control command in real time and generates the corresponding sub-command locally. The specific formula is: , in, Indicates time, Indicates the total execution time of the command. Indicates the time required to generate the command. Indicates the transmission time of the command.

[0006] Preferably, the instruction receiving module receives the operation instruction from the remote control terminal including: A communication channel needs to be established between the remote control terminal and the smart substation system. The communication channel includes TCP / IP, UDP, HTTP, and WebSocket. First, a connection request from the remote control terminal is received through the network layer, and a secure and encrypted communication connection is established through steps including identity authentication and key exchange. Before receiving the operation instruction, the remote control terminal needs to be authenticated; After the connection is successfully established, it starts to receive operation instructions from the remote control terminal. These instructions are transmitted in the form of data packets and stored in the buffer; When receiving instruction data, an integrity check is performed on the operation instruction, and the check method includes a cyclic redundancy check.

[0007] Preferably, when receiving the instruction data, the operation instruction is checked for integrity, and the checking method includes a cyclic redundancy check, and the cyclic redundancy check is specifically: First, the instruction data to be transmitted is represented as a binary number. Before sending the data, the instruction data is expanded and zeros are added after it. The number of zeros depends on the number of bits of the generating polynomial. The expanded data is divided by the generating polynomial in binary. The highest bit of the data is first taken and XORed with the generating polynomial until all data bits are processed. The final remainder is the CRC check code. The generated CRC check code is attached to the end of the data to form the final transmission data packet. The instruction receiving module at the receiving end will perform the same binary division operation on the entire received data packet, including the instruction data and the CRC check code. The receiving end performs the following operations on the received data: Divide the received data and the additional check code together with the generator polynomial; If the remainder is zero, it means that no error occurred during the transmission process and the instruction data is valid. If the remainder is not zero, it means that an error occurred during the data transmission process, the data is damaged and needs to be retransmitted.

[0008] Preferably, the instruction parsing module parses the operation instruction to determine the corresponding operation parameter, including: Use a finite state machine to parse the operation instructions to ensure that the system goes through different states in sequence during instruction processing. Each state corresponds to a part of the instruction. The specific formula is: , in, Indicates the next parsing state, represents the state transition function, Indicates the current parsing status. Indicates part of the current input command. Indicates the first status.

[0009] Preferably, the instruction parsing module parses the operation instruction to determine the corresponding operation parameter further comprising: Analyze historical operation data, and combine it with the actual equipment operation status, dynamically adjust the parameter range, and detect abnormal instructions. If the operation parameters exceed the historical working range of the equipment, the model identifies potential risks and issues warnings. The specific formula for this process is: , in, Indicates the parameters, Represents a given data, Indicates that given data Under these conditions, parameters The conditional probability of Indicated in the parameter Under the given conditions, the data The probability of Representation parameters The prior probability of Representation data The marginal probability of .

[0010] Preferably, the command generation module generates a substation control command based on the operation parameter and further includes: When generating control commands, combined with the needs of multi-device collaborative operation, a dynamic priority scheduling algorithm is used to assign an execution order to each device. Dynamic scheduling adjusts the order of issuing control commands according to the device operating status, operation urgency, and task priority. The specific formula is: , in, Indicates the index of the device. Indicates time, Indicates the device In time The priority of Indicates the device The corresponding task weight, Indicates the device From last execution to time waiting time.

[0011] Preferably, the control command sending module sends a control command to the smart substation, and performing corresponding operations further includes: In the substation, the devices work in coordination at the same time. The sending of control commands ensures the synchronization between the devices. The distributed clock synchronization technology ensures that the devices execute the received control commands synchronously at the microsecond level. The specific formula is: , in, Indicates the base time for synchronization. Indicates the command execution time of the device. Indicates the time deviation caused by network delay or device difference during command transmission. Indicates time.

[0012] A smart substation data processing method, using the smart substation remote control system, the method comprising: S1. The remote control terminal sends operation instructions related to the substation; S2, receiving an operation instruction from a remote control terminal through an instruction receiving module; S3, the instruction parsing module parses the received operation instruction and determines the required operation parameters; S4. Based on the parsed operation parameters, the command generation module generates corresponding smart substation control commands; S5. The generated control command is transmitted to the smart substation through the control command sending module to execute corresponding operations.

[0013] Preferably, the generation of the control command comprises performing a logical operation on the operating parameters.

[0014] Preferably, before sending the control command, the current state of the smart substation equipment is detected.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The intelligent substation remote control system and data processing method thereof send operation instructions through a remote control terminal, the instruction receiving module receives the operation instructions from the remote control terminal, the instruction parsing module parses the operation instructions to determine corresponding operation parameters, the command generating module generates a substation control command based on the operation parameters, and the control command sending module sends the control command to the intelligent substation to perform corresponding operations, which can provide higher control accuracy and timeliness, ensure the accuracy and timeliness of remote operation, effectively resist high-level network attacks, ensure that the remote control instructions of the intelligent substation are not destroyed during the transmission process, improve the overall security and reliability of the power grid, ensure accurate synchronization of equipment status information, avoid the occurrence of asynchronism, thereby improving the overall operation efficiency and stability of the system, improving the adaptability and expansion capability of the system, meeting the needs of efficient operation of modern intelligent substations, and solving the problems that in the prior art, communication delay has a great impact on the real-time control of the system in a high-voltage direct current transmission system, the remote control of the intelligent substation is also faced with risks in network security, there are a large number of devices in the intelligent substation, and large-scale data concurrent processing is difficult, and at the same time, in the process of rapid switching between multiple devices, if there is a lack of an effective synchronization mechanism, asynchronism will occur, resulting in inaccurate equipment status information, affecting the operation efficiency of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the connection of the system modules of the present invention; Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, in a possible implementation, a smart substation remote control system includes a remote control terminal, an instruction receiving module, an instruction parsing module, a command generating module and a control command sending module. The remote control terminal is used to send operation instructions. The instruction receiving module is connected to the remote control terminal and is used to receive these operation instructions. The received operation instructions will be passed to the instruction parsing module for parsing to parse out the corresponding operation parameters. The command generating module generates corresponding substation control commands based on the parsed operation parameters. Finally, the control command sending module sends these control commands to the smart substation for operation control.

[0019] The working principle of the system can be simply understood as a closed-loop control process. The remote control terminal serves as the input end of the user operation and controls the equipment of the substation by sending operation instructions. The instruction receiving module is responsible for receiving the operation instructions from the terminal and passing these instructions to the instruction parsing module. The instruction parsing module converts the instructions into operation parameters executable by the equipment through the preset parsing logic. The command generation module generates specific control commands based on the parameters obtained by parsing. The control command combines the current status and operation requirements of the substation equipment to ensure that the control command can be effectively executed. Finally, the control command sending module transmits the generated control command to each device of the smart substation to complete the corresponding operation. The smart substation remote control system realizes remote and precise control of the substation. The operation instructions are sent by the user through the remote control terminal, and on-site operation is no longer required, which improves the flexibility and operation efficiency of the system. At the same time, the system ensures stable communication and data parsing capabilities between various links through modular design, and can generate precise control commands according to different operation requirements. The system is adaptive and can automatically adjust the operation parameters according to the current status of the substation to ensure the stable operation of the system in various operation scenarios.

[0020] In other embodiments, the instruction receiving module can support a variety of different communication protocols, such as TCP / IP, UDP, HTTP, WebSocket, etc. The specific protocol selection can be adjusted according to the network environment or user needs. In addition, the command generation module can also use different algorithms or rules to generate control commands according to different device types or operation requirements. For example, a load balancing mechanism can be introduced for high-load devices, or a priority scheduling algorithm can be used for emergency situations to improve the control response speed. At the same time, the implementation of the control command sending module can also use different transmission methods according to the transmission distance and environment, such as wireless networks or fiber optic transmission, to ensure fast and reliable delivery of commands.

[0021] The command generation module generates a substation control command based on the operation parameters, including: When the equipment is in a situation where the load changes greatly, the control command automatically adjusts the operating parameters. The specific formula is: , in, Indicates the generated control command, represents the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time.

[0022] When the load of the equipment changes greatly, the command generation module dynamically adjusts the control command through the above formula. In order to adapt to different load conditions, the system introduces an adaptive gain coefficient. By adjusting these two parameters to optimize the control command, the equipment can operate stably while ensuring the efficiency of the operation. As time goes by, the control command will also be automatically adjusted according to the state and load changes of the equipment to ensure the smooth operation of the equipment. The adaptive control algorithm of the system can automatically adjust the operating parameters according to the load conditions of the equipment to avoid instability or failure of the equipment when the load changes greatly. By dynamically adjusting the operating parameters, the system can maintain a stable control effect under different load conditions, while improving the response speed and operating efficiency of the equipment. The introduction of the adaptive gain coefficient further enhances the flexibility of the system in complex environments, allowing the control command to quickly respond to changes in the state of the equipment and improve the robustness of the system.

[0023] In other embodiments, the adaptive algorithm can be implemented using different mathematical models, such as using a PID control algorithm or a fuzzy control algorithm to replace the current adaptive formula, further enhancing the control accuracy and adaptability of the system. In addition, the adaptive gain coefficient can also be dynamically adjusted according to the different operating states of the equipment, or the historical data can be analyzed through a machine learning algorithm to predict the future load conditions of the equipment, thereby optimizing the adjustment strategy of the operating parameters.

[0024] In one possible implementation, the control command sending module of the smart substation remote control system is responsible for sending the generated control commands to the various devices of the smart substation, and executing these commands locally on the edge computing node of the substation. After receiving the remotely transmitted control command, the edge node will parse and generate the corresponding sub-commands in real time, so as to directly control the operation of the relevant equipment locally. Specifically, the generated control command is transmitted to the smart substation through the control command sending module and executed on the edge computing node. The edge node not only receives and processes commands from the remote, but also generates local sub-commands according to actual needs, so as to ensure that the equipment can respond to control instructions efficiently and accurately. The execution time of the command is calculated by the following formula: , in, Indicates time, Indicates the total execution time of the command. Indicates the time required to generate the command. Indicates the transmission time of the command.

[0025] When the control command is generated, the command sending module is responsible for sending it to the edge computing node of the substation. The edge computing node parses the control command and generates sub-commands locally to ensure that the operation can be executed quickly and accurately. By calculating the command generation time and the command transmission time, the system can evaluate the total execution time of each command, thereby monitoring the efficiency of the operation execution process in real time. The system adopts edge computing, which can effectively reduce the delay between the command from the remote terminal to the device execution, and ensure the improvement of the system response speed. By generating sub-commands in real time, the system can more flexibly adapt to the operating requirements of different equipment in the substation, further improving the collaborative work efficiency of the equipment. In addition, the calculation method of the total command execution time can help the system monitor the execution process in real time to ensure that the operation is completed smoothly within the specified time.

[0026] In other implementations, the transmission mode of the control command can be adjusted according to different communication environments, such as using optical fiber communication or satellite communication, etc. The edge node can also use different processor architectures or algorithms to generate sub-commands to meet the needs of different devices and further improve the processing capability of the system.

[0027] In a possible implementation, the instruction receiving module is used to receive operation instructions from a remote control terminal. A secure communication channel needs to be established between the smart substation system and the remote control terminal. The communication channel can adopt a variety of network protocols, including TCP / IP, UDP, HTTP or WebSocket. First, the system receives a connection request from the remote control terminal through the network layer. In the process of establishing the connection, the system will perform identity authentication and key exchange to ensure the security of communication. After successfully establishing an encrypted communication connection, the system begins to receive operation instructions from the remote control terminal. Before receiving the operation instructions, the system must authenticate the remote control terminal to ensure that only authorized terminals can send instructions. After the authentication is passed, the operation instructions are transmitted through the communication channel in the form of data packets and stored in the buffer of the instruction receiving module. In the process of receiving the instruction data, the system will perform integrity verification on these data. The commonly used verification method is cyclic redundancy check (CRC).

[0028] The working principle of the instruction receiving module includes two main stages: the first stage is the establishment of the communication channel. The system first performs identity authentication and key exchange by receiving the connection request of the remote control terminal to ensure the communication security. After the communication channel is established, the system enters the second stage and starts to receive operation instructions. The instruction data is transmitted to the receiving module in the form of data packets and stored in the buffer. To ensure the integrity of the instruction data, the system uses a cyclic redundancy check (CRC) to detect errors that may occur during the transmission process. The CRC check verifies the data by appending a check code to the end of the data. The receiving end performs the same check operation on the data packet to determine whether the check code is consistent, thereby verifying the integrity of the data transmission. By establishing a secure and encrypted communication channel and performing identity authentication, the system effectively prevents unauthorized terminal access and improves the security of the system. At the same time, the use of cyclic redundancy check (CRC) ensures the integrity of data transmission, can effectively detect and correct errors that may occur during the transmission process, and ensure the correct transmission of instructions. This mechanism makes remote control more reliable and secure, and meets the high reliability requirements of smart substations.

[0029] In other embodiments, the communication channel can be encrypted and authenticated based on other security protocols, for example, the SSL / TLS protocol can be used to enhance the encryption strength of data transmission. In addition, the identity authentication method can also use multi-factor authentication or digital certificate authentication to improve security according to needs. In terms of integrity verification, in addition to cyclic redundancy check, hash functions such as SHA-256 and MD5 can also be used for data verification to meet different security and performance requirements.

[0030] In a possible implementation, the instruction receiving module performs integrity check on the operation instruction when receiving instruction data, and the check method used is a cyclic redundancy check (CRC). The specific process is as follows: First, the instruction data to be transmitted is represented as a binary number. Before sending the data, the system will expand the instruction data, that is, add a certain number of zeros after it. The number of zeros added depends on the number of bits of the generator polynomial used for verification. The expanded data is binary divided using a pre-defined generator polynomial. The highest bit of the instruction data is first XORed with the highest bit of the generator polynomial, and then all the remaining data bits are processed. Finally, the remainder obtained by binary division is the CRC check code. The system appends the generated CRC check code to the end of the instruction data to form the final transmission data packet; When the receiving end receives a data packet, the instruction receiving module will perform an integrity check on the entire data packet. The data packet includes not only the instruction data, but also the CRC check code attached at the end. The receiving end will use the same generating polynomial as the sending end to perform the same binary division operation on the entire received data packet (including the instruction data and the CRC check code). If the final remainder is zero, it indicates that there is no error in the data transmission process and the instruction data is valid. If the remainder is not zero, it means that an error occurred during the data transmission process and the data has been damaged. The system will issue an error prompt and require the data packet to be retransmitted.

[0031] Cyclic redundancy check (CRC) is a widely used error detection method that processes binary data through a generator polynomial to detect the integrity of the data during transmission. The working principle of CRC is to process binary data through a specific generator polynomial to generate a remainder (i.e., CRC check code) and append it to the data. The receiver will use the same generator polynomial to recheck the data packet (including data and check code) to ensure that there is no error in the data. CRC can effectively detect various types of transmission errors by converting data into mathematical operations. Cyclic redundancy check can quickly detect errors in transmitted data through simple and effective mathematical operations, thereby ensuring the accuracy and integrity of remote command transmission. It can not only detect common single-bit errors, but also more complex error modes such as multi-bit errors and burst errors. Due to its low computational complexity, CRC is suitable for scenarios with high real-time requirements, especially in the remote control system of smart substations, ensuring the reliable transmission and execution of remote commands.

[0032] In other embodiments, the system can use different generating polynomials to adapt to different data lengths or system requirements. The polynomial length of CRC can be adjusted according to the requirements of the communication protocol, and commonly used ones are CRC-8, CRC-16 and CRC-32. In addition, the verification method can also be replaced according to different scenarios. For example, when higher security or more complex verification is required, a more advanced verification algorithm such as SHA-256 or MD5 hash value verification can be used.

[0033] In a possible implementation, the instruction parsing module uses a finite state machine to parse the operation instruction, ensuring that the system goes through different states in sequence during the instruction processing, each state corresponds to a part of the instruction, and gradually parses and determines the operation parameters. The finite state machine can ensure the accuracy and orderliness of instruction parsing, that is, each step of parsing depends on the parsing result of the previous state. The specific formula is: , in, Indicates the next parsing state, represents the state transition function, Indicates the current parsing status. Indicates part of the current input command. Indicates the first status.

[0034] The core of the instruction parsing module is the finite state machine model. The finite state machine divides each instruction into multiple parts, each of which corresponds to a state. During instruction processing, the system will and the input command part Perform state transition and enter the next parsing state , the state transition is determined by the state transition function The function defines different state transition rules to implement the step-by-step parsing of instructions. In this way, the system can process complex instructions in sequence to ensure that each operation parameter is accurately parsed.

[0035] By introducing the finite state machine parsing method, the system can ensure the orderly parsing of instructions and avoid errors and ambiguities in complex instruction processing. At the same time, the state transition function of the finite state machine is flexible and extensible, and can define corresponding parsing paths according to different instruction types and structures, enhancing the system's ability to handle diversified instructions. The advantage of the finite state machine parsing method is that it can ensure the dependency of each step of the operation, enabling the system to accurately parse multi-step instructions, improving the security and reliability of the system.

[0036] In other embodiments, the state transition function of the finite state machine It can be dynamically adjusted according to the complexity of the instruction. For complex instructions, a hierarchical state machine can be introduced, which is divided into multiple sub-state machines to handle the instruction parsing at each stage. In addition, the instruction parsing module can also be combined with artificial intelligence algorithms, such as natural language processing or pattern recognition technology, to automatically identify and parse different parts of the instruction, further improving the automation and intelligence level of instruction parsing.

[0037] In a possible implementation, when parsing the operation instruction and determining the corresponding operation parameters, the instruction parsing module further combines the historical operation data and the actual operation status of the device to dynamically adjust the range of the operation parameters to ensure the safe and efficient operation of the device. The system will detect abnormal conditions in the instruction. If the operation parameters exceed the historical working range of the device, the model will identify potential risks and issue a warning. This process is calculated by the following formula: , in, Indicates the parameters, Represents a given data, Indicates that given data Under these conditions, parameters The conditional probability of Indicated in the parameter Under the given conditions, the data The probability of Representation parameters The prior probability of Representation data The marginal probability of .

[0038] The instruction parsing module first analyzes the historical operation data of the equipment, extracts the operating parameter range of the equipment under different states, and dynamically adjusts the working range of the operating parameters in combination with the real-time equipment operation status. When the system receives a new operation instruction, it compares the parsed operating parameters with the historical data and uses the Bayesian formula to to calculate the conditional probability of the parameter. If the conditional probability of the resolved parameter value deviates significantly from the historical working range, the system will consider that there is an abnormality, which may mean that there is an error in the instruction or there is a potential risk in the operation of the equipment. At this time, the system will trigger a warning to remind the operator or automatically adjust the parameters. This method can effectively improve the system's ability to detect abnormal operating parameters and ensure that the equipment operates within a safe range by introducing historical data analysis and Bayesian inference. By adjusting the range of operating parameters in real time, the system can not only adapt to the current state of the equipment, but also prevent equipment failure or damage caused by excessive or small parameters. This mechanism greatly improves the safety and intelligence level of the system, enabling it to self-adjust and optimize in complex operating environments and reduce human operational errors.

[0039] In other embodiments, the model can use other statistical methods or machine learning algorithms for anomaly detection, such as using decision trees, support vector machines (SVM) or neural networks to replace the Bayesian inference model to improve the accuracy and speed of anomaly detection. In addition, the system can also combine prediction algorithms to determine the operating trend of the equipment in advance and further optimize the parameter adjustment strategy. For example, a time series analysis model (such as ARIMA) can be introduced to predict future load changes of the equipment, so as to adjust the operating parameters in advance.

[0040] In a possible implementation, when generating control commands, the command generation module uses a dynamic priority scheduling algorithm to assign an execution order to each device in combination with the requirements of collaborative operation of multiple devices. The scheduling algorithm dynamically adjusts the order in which control commands are issued according to the operating status of the device, the urgency of the operation, and the priority of the task. The specific formula is: , in, Indicates the index of the device. Indicates time, Indicates the device In time The priority of Indicates the device The corresponding task weight, Indicates the device From last execution to time waiting time.

[0041] In the process of multi-device collaborative operation, different devices may have different execution priorities. The command generation module uses a dynamic priority scheduling algorithm, combined with the task weight of each device. and device waiting time , assigning an appropriate execution order to each device. The priority of a device increases as its waiting time increases, thus preventing a device from not executing an operation for a long time. By introducing weights The system can give priority to more urgent or important task equipment to ensure the orderly progress of operations. Through this dynamic priority scheduling algorithm, the system can intelligently assign execution orders to multiple devices, improving the overall coordination and response speed of the system. Especially in the case of different task urgency, the system can give priority to important task equipment to ensure the timely completion of key tasks. At the same time, the scheduling algorithm takes the waiting time of the equipment into consideration to avoid the situation where some equipment cannot be executed for a long time due to low priority, thereby achieving fair and efficient resource scheduling.

[0042] In other embodiments, the task weight It can be dynamically adjusted according to different task types. For example, for tasks with high real-time requirements, a higher weight value can be assigned so that they are executed first during scheduling. In addition, the scheduling algorithm can further adjust the priority based on the operating status of the device (such as the load or failure rate of the device), so that more factors can be considered during the scheduling process to optimize the overall performance of the system. The scheduling strategy can also be expanded to a multi-level priority algorithm, or combined with a machine learning algorithm to automatically adjust the weight and priority allocation strategy for each device.

[0043] In a possible implementation, the control command sending module sends control commands to the smart substation to ensure synchronous execution of the equipment. The various devices in the substation need to work in coordination at the same time, and the sending of control commands must ensure synchronization between the devices. To achieve this goal, the system uses distributed clock synchronization technology to ensure that the devices synchronously execute the received control commands within the microsecond accuracy range. The specific formula is: , in, Indicates the base time for synchronization. Indicates the command execution time of the device. Indicates the time deviation caused by network delay or device difference during command transmission. Indicates time.

[0044] In smart substations, synchronous execution between devices is crucial, especially when multiple devices are operating in coordination. The system uses distributed clock synchronization technology to ensure that all devices can execute control commands within a unified reference time. The execution time of each device The time deviation will be caused by network delay or device difference Adjustments are made to ensure that all devices can execute commands synchronously within microsecond accuracy. This mechanism ensures the coordination and consistency of devices under complex operations, avoiding command misalignment or equipment failure due to time differences. The system ensures high-precision synchronous operation of devices in the substation through distributed clock synchronization technology. Synchronously executed control commands effectively improve the collaborative work efficiency between devices and reduce execution errors caused by asynchrony. Microsecond-level synchronization accuracy enables devices to maintain consistency in high-load, high-complexity operating environments, thereby improving the stability and reliability of the entire system.

[0045] In other implementations, clock synchronization can use other synchronization protocols or technologies, such as the Network Time Protocol (NTP) or the Precision Time Protocol (PTP), to adapt to different network conditions and synchronization accuracy requirements. In addition, for some scenarios with higher requirements for time synchronization, optical fiber synchronization technology or satellite clock synchronization technology can be used to further improve synchronization accuracy and reliability. At the same time, the system can introduce a dynamic adjustment mechanism to dynamically adjust the synchronization deviation according to the real-time changes in network delay or device load. , ensuring that devices always execute in sync.

[0046] In a possible implementation manner, the present invention further provides a smart substation data processing method, based on the smart substation remote control system, to ensure remote control and efficient data processing of the substation, the method specifically comprises the following steps: S1: The remote control terminal sends an operation instruction. The remote control terminal is used to generate and send operation instructions related to the smart substation. The user inputs specific operation requirements through the terminal, and the remote terminal sends the operation instruction to the control system of the substation through the network; S2: Receive operation instructions. The instruction receiving module receives operation instructions from the remote control terminal. At this time, the system will ensure the secure transmission of instructions through a pre-established secure communication channel, such as TCP / IP, UDP, HTTP or WebSocket, and ensure the correctness of the instructions through identity authentication and integrity verification (such as cyclic redundancy check); S3: Parsing operation instructions. The instruction parsing module parses the received operation instructions and determines the required operation parameters. Through the finite state machine (FSM) or other appropriate parsing mechanism, the system gradually processes each part of the instruction to ensure the accurate execution of the instruction. If necessary, the instruction parsing module will also dynamically adjust the range of operation parameters and detect potential abnormal operations in combination with the historical operation data of the device; S4: Generate control commands. Based on the parsed operation parameters, the command generation module generates corresponding control commands. This process combines the operating status of the device and the urgency of the operation, dynamically adjusts the priority order of tasks, and ensures that the control commands can be executed collaboratively among various devices. The specific scheduling algorithm calculates the device priority based on the task weight and waiting time, thereby dynamically assigning the operation order of the device; S5: Send and execute control commands. The generated control commands are transmitted to the devices in the smart substation through the control command sending module. The system uses distributed clock synchronization technology to ensure that each device executes the received control commands synchronously at the microsecond level, avoiding execution deviations caused by network delays or device differences, and ensuring coordinated work of the devices.

[0047] The entire smart substation data processing process starts with the remote control terminal. The user issues an operation instruction through the terminal. The instruction receiving module ensures the safe transmission and reception of the instruction. Then the instruction parsing module parses and determines the operation parameters. The parsing result is used to generate a control command, which is transmitted to each device in the smart substation for execution through distributed clock synchronization technology. The system ensures the efficient coordination of the equipment through dynamic priority scheduling and adaptive adjustment strategy. This method realizes the remote automation operation of the smart substation through efficient instruction reception, parsing and control command generation mechanism. The introduction of dynamic priority scheduling and distributed clock synchronization technology ensures the efficient coordination of equipment in complex operating environments. Historical data analysis and adaptive adjustment mechanism enhance the flexibility and security of the system, and can detect abnormal operations in time and avoid potential faults. This method not only improves the operating efficiency of the substation, but also ensures the safety and reliability of operation.

[0048] In other embodiments, the instruction parsing process can be combined with more complex algorithms, such as machine learning models, to optimize the selection and adjustment of operating parameters. The generation of control commands can also introduce more scheduling strategies, such as based on the real-time nature of the task or the current load status of the equipment to further optimize the operation. In addition, the system can further reduce the delay in command transmission by introducing redundant communication channels or high-speed communication technology based on 5G, thereby improving the real-time and reliability of substation equipment response.

[0049] In a possible implementation, the generation of the control command is not only based on the parsed operating parameters, but also includes logical operations on these operating parameters. Logical operations are used to ensure that the generated control commands meet the actual operating requirements of each device in the substation and optimize the collaborative work of the equipment. Specifically, after the command generation module receives the operating parameters transmitted by the instruction parsing module, it first performs logical judgments and operations on these parameters. For example, when certain operating conditions are met, the control command needs to activate multiple devices for joint operation; under other conditions, it may only be necessary to execute the operation of a single device. Logical operations can be performed based on the current status of the device, operating requirements, and system preset rules. For example: 1. Conditional judgment The system can use logical operations to conditionally judge the operating parameters and select different control paths. ,in, is the real-time operating parameter of a device, For a preset threshold, the system generates control command A, otherwise it generates control command B; 2. Combination operation When generating control commands for multiple devices, the system can perform logical combinations on multiple parameters, for example: ; This logic operation checks the status of multiple devices to ensure that the corresponding control command is generated only when the parameters of both devices meet certain conditions. Similar combinations can also use logical operators such as "OR" and "XOR", depending on the operational requirements of the system.

[0050] 3. During the arithmetic operation command generation process, the system can also perform arithmetic operations on the operating parameters. For example, the weighted value or correction coefficient of the operating parameters can be adjusted according to the real-time status of different devices to generate the optimal control command: , in, and is the corresponding weight coefficient, and The operating parameters for different devices.

[0051] After receiving the parsed operating parameters, the command generation module uses logical operations (such as conditional judgment, combination operations, arithmetic operations, etc.) to further optimize the generation of control commands. These operations ensure that the generated control commands can be executed safely and efficiently by ensuring that the operating parameters are consistent with the actual operating status of the equipment. The results of the logical operations will directly affect the final form of the control command, and will be issued to each device through the system's scheduling strategy. By performing logical operations on the operating parameters, the system can generate more accurate and flexible control commands to ensure that the equipment can correctly respond to operating instructions in a complex operating environment. Logical operations improve the intelligence of the system, allowing automatic adjustment of operating commands according to real-time conditions to avoid human errors and equipment damage. Logical operations combined with historical data and equipment status can also help the system better adapt to emergencies or load fluctuations, further improving the safety and reliability of the system.

[0052] In other embodiments, the complexity of the logic operation can be expanded according to the operation requirements. For example, a more complex conditional control structure can be introduced, or fuzzy logic can be combined to adapt to decision-making under fuzzy conditions. In addition, the system can also introduce an artificial intelligence-based logic operation algorithm to automatically adjust and optimize the selection strategy of the operating parameters through a machine learning model, thereby further improving the generation efficiency and accuracy of the control command.

[0053] In a possible implementation, before sending a control command, the system will detect the current status of the smart substation equipment to ensure that the equipment is in normal operation and can execute the control command to be issued. This detection step is intended to avoid failure of control command execution or malfunction due to equipment abnormality or status not meeting expectations.

[0054] The status detection steps include: 1. Equipment status monitoring Before sending a control command, the system uses the device status monitoring module to perform real-time detection of the current operating status of each device. The system can monitor the status including but not limited to the voltage, current, load, temperature, communication status and historical operating data of the device. If the device status is abnormal (such as overload, overheating, device offline, etc.), the system will issue an early warning and suspend or adjust the sending of the control command.

[0055] 2. Status threshold judgment The system will judge whether the current status of the device is suitable for executing control commands based on the preset threshold. For example: if, ,in, Indicates the current voltage value of the device. Indicates the preset voltage safety threshold. When the device status is within the preset threshold range, the control command will be issued normally; otherwise, the system will prevent the command from being executed and issue a warning.

[0056] 3. Equipment operation history analysis During the status detection process, the system can also analyze the historical operation data of the device. For example, it can detect whether the device has restarted or failed multiple times in a short period of time, and combine these historical data to decide whether to continue to execute the current control command. If the system recognizes that the device has been abnormal recently, it may stop sending control commands to prevent further damage to the device.

[0057] 4. Communication status check Before sending a command, the system will also detect the communication status between the device and the control center to ensure that the device can receive the control command. If the communication link is unstable or interrupted, the system will delay the sending of the control command to avoid data loss or command execution failure.

[0058] The status detection module ensures that the equipment can safely execute control commands through real-time monitoring and historical data analysis. The system obtains real-time status data by communicating with the equipment, and determines whether the equipment is in a normal state by combining the equipment's status threshold setting and historical records. If the equipment is in an abnormal state or does not meet the execution conditions, the system will stop or adjust the sending of control commands to ensure the safety and reliability of the operation. The introduction of the status detection module can detect potential problems before the command is executed, avoiding operational risks caused by equipment abnormalities. By performing equipment status detection before sending control commands, the system can significantly improve the safety and reliability of control command execution. Status detection ensures that the equipment can execute control instructions under safe conditions, avoiding failures or damage caused by equipment overload or abnormal status. At the same time, the status detection module can also help operation and maintenance personnel to promptly detect equipment problems by identifying potential problems in advance, reducing equipment downtime and improving the overall operation efficiency of the substation.

[0059] In other embodiments, the state detection module can be combined with machine learning or artificial intelligence algorithms to predict the future state of the device through big data analysis. For example, the system can predict the impending failure of the device and terminate the command execution in advance based on the historical state and real-time operation data of the device. In addition, state detection can also be combined with edge computing to process the detection data in real time through local devices, further improving the response speed and accuracy of state detection.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote control system for an intelligent substation, characterized in that: The system comprises: Remote control terminal, used to send operation instructions; A command receiving module connected to the remote control terminal, used to receive operation commands from the remote control terminal; An instruction parsing module connected to the instruction receiving module, used to parse the operation instruction to determine the corresponding operation parameters; A command generation module connected to the instruction parsing module, used to generate a substation control command based on the operation parameters; A control command sending module connected to the command generating module is used to send control commands to the smart substation to perform corresponding operations; The command generation module generates a substation control command based on the operation parameter, including: When the equipment is in a situation where the load changes greatly, the control command automatically adjusts the operating parameters. The specific formula is: ， in, Indicates the generated control command, represents the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time; The control command sending module sends a control command to the smart substation, and performs corresponding operations including: The generated control command is sent to each device in the smart substation through the control command sending module, and the command is executed locally at the substation edge computing node. The edge node receives the remote control command in real time and generates the corresponding sub-command locally. The specific formula is: ， in, Indicates time, Indicates the total execution time of the command. Indicates the time required to generate the command. Indicates the transmission time of the command.

2. The intelligent substation remote control system according to claim 1, characterized in that: The instruction receiving module receives the operation instruction from the remote control terminal including: A communication channel needs to be established between the remote control terminal and the smart substation system. The communication channel includes TCP / IP, UDP, HTTP, and WebSocket. First, a connection request from the remote control terminal is received through the network layer, and a secure and encrypted communication connection is established through steps including identity authentication and key exchange. Before receiving the operation instruction, the remote control terminal needs to be authenticated; After the connection is successfully established, it starts to receive operation instructions from the remote control terminal. These instructions are transmitted in the form of data packets and stored in the buffer; When receiving instruction data, an integrity check is performed on the operation instruction, and the check method includes a cyclic redundancy check.

3. The intelligent substation remote control system according to claim 2, characterized in that: When receiving the instruction data, the operation instruction is checked for integrity, and the checking method includes a cyclic redundancy check, and the cyclic redundancy check is specifically: First, the instruction data to be transmitted is represented as a binary number. Before sending the data, the instruction data is expanded and zeros are added after it. The number of zeros depends on the number of bits of the generating polynomial. The expanded data is divided by the generating polynomial in binary. The highest bit of the data is first taken and XORed with the generating polynomial until all data bits are processed. The final remainder is the CRC check code. The generated CRC check code is attached to the end of the data to form the final transmission data packet. The instruction receiving module at the receiving end will perform the same binary division operation on the entire received data packet, including the instruction data and the CRC check code. The receiving end performs the following operations on the received data: Divide the received data and the additional check code together with the generator polynomial; If the remainder is zero, it means that no error occurred during the transmission process and the instruction data is valid. If the remainder is not zero, it means that an error occurred during the data transmission process, the data is damaged and needs to be retransmitted.

4. The intelligent substation remote control system according to claim 1, characterized in that: The instruction parsing module parses the operation instruction to determine the corresponding operation parameter, including: Use a finite state machine to parse the operation instructions to ensure that the system goes through different states in sequence during instruction processing. Each state corresponds to a part of the instruction. The specific formula is: ， in, Indicates the next parsing state, represents the state transition function, Indicates the current parsing status. Indicates part of the current input command. Indicates the first status.

5. The intelligent substation remote control system according to claim 4, characterized in that: The instruction parsing module parses the operation instruction to determine the corresponding operation parameter and further includes: Analyze historical operation data, and combine it with the actual equipment operation status, dynamically adjust the parameter range, and detect abnormal instructions. If the operation parameters exceed the historical working range of the equipment, the model identifies potential risks and issues warnings. The specific formula for this process is: ， in, Indicates the parameters, Represents a given data, Indicates that given data Under these conditions, parameters The conditional probability of Indicated in the parameter Under the given conditions, the data The probability of Representation parameters The prior probability of Representation data The marginal probability of .

6. The intelligent substation remote control system according to claim 1, characterized in that: The command generation module generates a substation control command based on the operation parameter and further includes: When generating control commands, combined with the needs of multi-device collaborative operation, a dynamic priority scheduling algorithm is used to assign an execution order to each device. Dynamic scheduling adjusts the order of issuing control commands according to the device operating status, operation urgency, and task priority. The specific formula is: ， in, Indicates the index of the device. Indicates time, Indicates the device In time The priority of Indicates the device The corresponding task weight, Indicates the device From last execution to time waiting time.

7. The intelligent substation remote control system according to claim 1, characterized in that: The control command sending module sends a control command to the smart substation, and the execution of the corresponding operation also includes: In the substation, the devices work in coordination at the same time. The sending of control commands ensures the synchronization between the devices. The distributed clock synchronization technology ensures that the devices execute the received control commands synchronously at the microsecond level. The specific formula is: ， in, Indicates the base time for synchronization. Indicates the command execution time of the device. Indicates the time deviation caused by network delay or device difference during command transmission. Indicates time.

8. A method for processing data of a smart substation, using the smart substation remote control system according to any one of claims 1 to 7, characterized in that: The method comprises: S1. The remote control terminal sends operation instructions related to the substation; S2, receiving an operation instruction from a remote control terminal through an instruction receiving module; S3, the instruction parsing module parses the received operation instruction and determines the required operation parameters; S4. Based on the parsed operation parameters, the command generation module generates corresponding smart substation control commands; S5. The generated control command is transmitted to the smart substation through the control command sending module to execute corresponding operations.

9. A smart substation data processing method according to claim 8, characterized in that: The generation of the control command includes performing a logical operation on the operation parameters.

10. The method for processing data of a smart substation according to claim 8, characterized in that: Before sending the control command, the current state of the smart substation equipment is detected.

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