A remote control system for an intelligent substation and a data processing method thereof

Abstract

The present invention discloses a smart substation remote control system and a data processing method thereof. The system includes a remote control terminal for sending operation instructions, an instruction receiving module for receiving operation instructions from the remote control terminal, an instruction parsing module for parsing the operation instructions to determine corresponding operation parameters, a command generating module for generating substation control commands based on the operation parameters, and a control command sending module for sending control commands to the smart substation to perform corresponding operations. The smart substation remote control system and the data processing method thereof ensure accurate synchronization of equipment status information and avoid asynchrony, thereby improving the overall operating efficiency and stability of the system, enhancing the adaptability and expansion capability of the system, and meeting the requirements for efficient operation of modern smart substations.

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] Remote control is a key technology in modern smart substation systems. Leveraging advanced information and communication technologies, operators can remotely monitor and control substation equipment. This technology plays a crucial role in ensuring the stability and efficiency of power grid operations, particularly in high-voltage direct current (HVDC) transmission systems. However, existing remote control methods face several technical challenges in practical operation.

[0003] First, in HVDC transmission systems, communication delays have a significant impact on the real-time control of the system. Due to the data transmission delay, 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, thereby affecting the stable operation of the system. Second, the remote control of smart substations also faces network security risks. Since the system transmits control commands through the network, if the network protection mechanism is not perfect, the control signals may be tampered with or intercepted by illegal intruders, thereby threatening the security and reliability of the entire power grid. Existing network security measures may be insufficient 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 challenge. 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, during the rapid switching between multiple devices, if there is a lack of an effective synchronization mechanism, asynchrony will occur, resulting in inaccurate device status information and 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 an intelligent 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 significant impact on the real-time control of the system, the remote control of the intelligent substation is also faced with network security risks, there are a large number of devices in the intelligent substation, and large-scale data concurrent processing is relatively 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 entire system.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a remote control system for an intelligent substation, the system comprising:

[0006] Remote control terminal, used to send operation instructions;

[0007] An instruction receiving module connected to the remote control terminal, used to receive operation instructions from the remote control terminal;

[0008] An instruction parsing module connected to the instruction receiving module is used to parse the operation instruction to determine the corresponding operation parameters;

[0009] a command generation module connected to the instruction parsing module, configured to generate a substation control command based on the operation parameters;

[0010] The control command sending module connected to the command generating module is used to send control commands to the smart substation and perform corresponding operations;

[0011] The command generation module generates a substation control command based on the operation parameters, including:

[0012] When the equipment is in a situation where the load changes significantly, the control command automatically adjusts the operating parameters. The specific formula is:

[0013] ,

[0014] in, Indicates the generated control command, Indicates the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time;

[0015] Adaptive gain coefficients can be dynamically adjusted based on the different operating states of the equipment, or historical data can be analyzed through machine learning algorithms to predict the future load conditions of the equipment;

[0016] The control command sending module sends a control command to the smart substation, and performs corresponding operations including:

[0017] The generated control command is sent to each device in the smart substation through the control command sending module. The command is executed locally on the substation edge computing node. The edge node receives the remote control command in real time and generates the corresponding sub-command locally. The command execution time is calculated using the following formula:

[0018] ,

[0019] 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;

[0020] By calculating the command generation time and command transmission time, evaluating the total execution time of each command, monitoring the efficiency of the operation execution process in real time, and monitoring the execution progress in real time, we can ensure that the operation is completed smoothly within the specified time.

[0021] Preferably, the instruction receiving module receives an operation instruction from the remote control terminal including:

[0022] 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, the network layer receives the connection request from the remote control terminal and establishes a secure and encrypted communication connection through steps including identity authentication and key exchange.

[0023] Before receiving the operation instruction, the remote control terminal needs to be authenticated;

[0024] 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;

[0025] When receiving instruction data, an integrity check is performed on the operation instruction, and the integrity check method includes a cyclic redundancy check.

[0026] Preferably, when receiving the instruction data, the operation instruction is checked for integrity, and the integrity check method includes a cyclic redundancy check, and the cyclic redundancy check is specifically:

[0027] First, the command data to be transmitted is represented as a binary number. Before sending the data, the command data is expanded and zeros are added after it. The number of zeros depends on the number of bits of the generator polynomial. The expanded data is divided by the generator polynomial in binary. The highest bit of the data is first taken and XORed with the generator polynomial until all data bits are processed. The final remainder is the CRC check code. The generated CRC check code is appended to the end of the data to form the final transmission data packet.

[0028] The instruction receiving module on the receiving end will perform the same binary division operation on the entire received data packet, including the instruction data and CRC check code. The receiving end performs the following operations on the received data:

[0029] Divide the received data and the appended check code by the generator polynomial;

[0030] 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.

[0031] Preferably, the instruction parsing module parses the operation instruction to determine the corresponding operation parameters, including:

[0032] Use a finite state machine to parse the operation instructions and 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:

[0033] ,

[0034] in, Indicates the next parsing state, represents the state transition function, Indicates the current parsing status. Indicates a part of the current input instruction. Indicates the first status.

[0035] Preferably, the instruction parsing module parses the operation instruction to determine the corresponding operation parameter further comprising:

[0036] Analyze historical operation data and combine it with the actual equipment operating status to dynamically adjust the parameter range and detect abnormal instructions. If the operating parameters exceed the historical operating range of the equipment, the model identifies potential risks and issues a warning. The specific formula is:

[0037] ,

[0038] in, Indicates parameters, Represents a given data, Indicates that given data Under these conditions, the parameters The conditional probability of Indicates that the parameter Under given conditions, the data The probability of Representation parameters The prior probability of Representation data The marginal probability of .

[0039] Preferably, the command generation module generates the substation control command based on the operation parameter and further includes:

[0040] When generating control commands, a dynamic priority scheduling algorithm is used to assign an execution order to each device based on the needs of multi-device collaborative operation. Dynamic scheduling adjusts the order in which control commands are issued based on the device's operating status, operation urgency, and task priority. The specific formula is:

[0041] ,

[0042] in, Indicates the index of the device, Indicates time, Representation device In time Priority, Representation device The corresponding task weight, Representation device Time from last execution to waiting time.

[0043] Preferably, the control command sending module sends a control command to the smart substation, and performing the corresponding operation further includes:

[0044] In the substation, the devices work in coordination at the same time. The transmission of control commands ensures the synchronization between devices. Distributed clock synchronization technology ensures that the devices execute the received control commands synchronously at the microsecond level. The specific formula is:

[0045] ,

[0046] 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.

[0047] A smart substation data processing method, using the smart substation remote control system, the method comprising:

[0048] S1. The remote control terminal sends operation instructions related to the substation;

[0049] S2, receiving an operation instruction from a remote control terminal through an instruction receiving module;

[0050] S3, the instruction parsing module parses the received operation instruction and determines the required operation parameters;

[0051] S4. Based on the parsed operating parameters, the command generation module generates corresponding smart substation control commands;

[0052] S5. The generated control command is transmitted to the smart substation through the control command sending module to execute corresponding operations.

[0053] Preferably, the generation of the control command includes performing a logical operation on the operating parameters.

[0054] Preferably, before sending the transmission control command, the current status of the smart substation equipment is detected.

[0055] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0056] The intelligent substation remote control system and data processing method thereof are characterized in that an operation instruction is sent by a remote control terminal, an instruction receiving module receives the operation instruction from the remote control terminal, an instruction parsing module parses the operation instruction to determine corresponding operation parameters, a command generating module generates a substation control command based on the operation parameters, and a control command sending module sends the control command to the intelligent substation to execute the corresponding operation. The intelligent substation remote control system and data processing method thereof provide higher control accuracy and timeliness, ensure the accuracy and timeliness of remote operation, effectively resist high-level network attacks, and protect the remote control instructions of the intelligent substation from being destroyed during transmission, thereby improving the overall security and reliability of the power grid, ensuring accurate synchronization of device status information, and avoiding asynchrony, thereby improving the overall operating efficiency and stability of the system, improving the adaptability and scalability of the system, meeting the requirements for efficient operation of modern intelligent substations, and solving the problems in the prior art of high-voltage direct current transmission systems: communication delay has a significant impact on the real-time control of the system; remote control of intelligent substations also faces network security risks; there are a large number of devices in intelligent substations, 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 and affecting the operating efficiency of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the connection of system modules of the present invention;

[0058] Figure 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0060] like Figure 1 As shown, in a possible embodiment, 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.

[0061] 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 substation equipment by sending operation instructions. The instruction receiving module is responsible for receiving operation instructions from the terminal and passing these instructions to the instruction parsing module. The instruction parsing module converts the instructions into operation parameters that can be executed by the equipment through the preset parsing logic. The command generation module generates specific control commands based on the parsed parameters. The control command is combined with the current status of the substation equipment and the operation requirements to ensure that the control command can be effectively executed. Finally, the control command sending module transmits the generated control command to each device in 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 operational efficiency of the system. At the same time, the system ensures stable communication and data parsing capabilities between each link 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.

[0062] 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 operating 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 network or fiber optic transmission, to ensure fast and reliable transmission of commands.

[0063] The command generation module generates a substation control command based on the operation parameters, including:

[0064] When the equipment is in a situation where the load changes significantly, the control command automatically adjusts the operating parameters. The specific formula is:

[0065] ,

[0066] in, Indicates the generated control command, Indicates the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time.

[0067] When the device load changes significantly, the command generation module dynamically adjusts the control command using the above formula. In order to adapt to different load conditions, the system introduces an adaptive gain coefficient. By adjusting these two parameters, the control command is optimized, allowing the device to operate stably while ensuring operational efficiency. Over time, the control command will also automatically adjust according to the device's status and load changes to ensure the smooth operation of the device. The system's adaptive control algorithm can automatically adjust the operating parameters according to the device's load conditions to avoid instability or failure of the device when the load changes significantly. By dynamically adjusting the operating parameters, the system can maintain a stable control effect under different load conditions, while improving the device's response speed and operating efficiency. The introduction of the adaptive gain coefficient further enhances the system's flexibility in complex environments, allowing the control command to quickly respond to changes in the device's status and improving the system's robustness.

[0068] In other implementations, 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 system's control accuracy and adaptability. Furthermore, the adaptive gain coefficient can be dynamically adjusted based on the device's varying operating states, or a machine learning algorithm can be used to analyze historical data to predict the device's future load conditions, thereby optimizing the adjustment strategy for operating parameters.

[0069] 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 in 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, thereby directly controlling 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 side, but also generates local sub-commands according to actual needs, thereby ensuring that the equipment can respond to control instructions efficiently and accurately. The execution time of the command is calculated by the following formula:

[0070] ,

[0071] 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.

[0072] Once a 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 uses edge computing to effectively reduce the delay between the execution of the command from the remote terminal to the device, ensuring that the system response speed is improved. 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.

[0073] In other embodiments, the transmission method of the control command can be adjusted according to different communication environments, such as using fiber optic communication or satellite communication. Edge nodes can also use different processor architectures or algorithms to generate sub-commands to meet the needs of different devices and further improve the processing capabilities of the system.

[0074] In one possible implementation, the command receiving module is configured to receive operational commands from a remote control terminal. A secure communication channel must be established between the smart substation system and the remote control terminal. This communication channel can utilize 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. During the connection establishment process, the system performs identity authentication and key exchange to ensure communication security. After successfully establishing an encrypted communication connection, the system begins receiving operational commands from the remote control terminal. Before receiving operational commands, the system must authenticate the remote control terminal to ensure that only authorized terminals can send commands. After successful authentication, the operational commands are transmitted as data packets over the communication channel and stored in the command receiving module's buffer. During the reception of the command data, the system performs an integrity check on the data, typically using a cyclic redundancy check (CRC).

[0075] The command receiving module operates in two main phases. The first phase involves establishing a communication channel. The system first receives a connection request from the remote control terminal, performs identity authentication, and exchanges keys to ensure secure communication. Once the communication channel is established, the system enters the second phase, where it begins receiving operational commands. Command data is transmitted to the receiving module in the form of data packets and stored in a buffer. To ensure the integrity of the command data, the system uses a cyclic redundancy check (CRC) to detect errors during transmission. The CRC check verifies data transmission by appending a checksum to the end of the data. The receiving end performs the same checksum on the data packet to determine if the checksum matches, thereby verifying the integrity of the data transmission. By establishing a secure, encrypted communication channel and performing identity authentication, the system effectively prevents unauthorized terminal access and enhances system security. Furthermore, the cyclic redundancy check (CRC) ensures data integrity, effectively detecting and correcting errors that may occur during transmission, and ensuring the correct transmission of commands. This mechanism makes remote control more reliable and secure, meeting the high reliability requirements of smart substations.

[0076] In other implementations, communication channels can be encrypted and authenticated based on other security protocols. For example, SSL / TLS protocols can be used to enhance the encryption strength of data transmission. Furthermore, identity authentication methods can also employ multi-factor authentication or digital certificate authentication to enhance security as needed. Regarding integrity verification, in addition to cyclic redundancy checks, hash functions such as SHA-256 and MD5 can also be used to verify data to meet varying security and performance requirements.

[0077] In one possible implementation, the instruction receiving module performs integrity check on the operation instruction when receiving instruction data, using a cyclic redundancy check (CRC). The specific process is as follows:

[0078] First, the command data to be transmitted is represented as a binary number. Before sending the data, the system will expand the command 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 command data is first taken and XORed with the highest bit of the generator polynomial. 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 command data to form the final transmission data packet;

[0079] When the receiving end receives a data packet, the instruction receiving module performs an integrity check on the entire packet. This packet includes not only the instruction data but also a CRC checksum appended to the end. The receiving end performs the same binary division operation on the entire packet (including the instruction data and CRC checksum) using the same generating polynomial as the sending end. If the remainder is zero, the data transmission was error-free and the instruction data is valid. If the remainder is not zero, an error occurred during data transmission and the data is corrupted. The system issues an error message and requires the packet to be retransmitted.

[0080] The cyclic redundancy check (CRC) is a widely used error detection method that processes binary data using a generator polynomial to verify data integrity during transmission. CRC operates by processing binary data through a specific generator polynomial to generate a remainder (the CRC checksum) that is appended to the data. The receiver then rechecks the data packet (including the data and the checksum) using the same generator polynomial to ensure that the data is error-free. By converting data into mathematical operations, the CRC effectively detects various types of transmission errors. Using simple yet effective mathematical operations, the CRC can quickly detect errors in transmitted data, thereby ensuring the accuracy and integrity of remote command transmission. It detects not only common single-bit errors but also more complex error patterns such as multi-bit errors and burst errors. Due to its low computational complexity, the CRC is suitable for scenarios with high real-time requirements, particularly in smart substation remote control systems, ensuring the reliable transmission and execution of remote commands.

[0081] In other implementations, the system can use different generator polynomials to accommodate varying data lengths or system requirements. The CRC polynomial length can be adjusted based on the communication protocol requirements; commonly used polynomials include CRC-8, CRC-16, and CRC-32. Furthermore, the verification method can be altered to suit 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.

[0082] In one possible implementation, the instruction parsing module uses a finite state machine to parse the operation instruction, ensuring that the system sequentially passes through different states during instruction processing. Each state corresponds to a portion of the instruction, and the operation parameters are gradually parsed and determined. The finite state machine ensures the accuracy and orderliness of instruction parsing, that is, each parsing step depends on the parsing result of the previous state. The specific formula is:

[0083] ,

[0084] in, Indicates the next parsing state, represents the state transition function, Indicates the current parsing status. Indicates a part of the current input instruction. Indicates the first status.

[0085] 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 This function defines different state transition rules to implement step-by-step parsing of instructions. In this way, the system can process complex instructions in sequence and ensure that each operation parameter is accurately parsed.

[0086] By introducing a finite state machine parsing approach, the system ensures orderly instruction parsing, avoiding errors and ambiguities in complex instruction processing. Furthermore, the finite state machine's state transition function is flexible and scalable, allowing for the definition of parsing paths based on different instruction types and structures, enhancing the system's ability to handle diverse instructions. The finite state machine parsing approach also offers the advantage of ensuring the dependencies between each operation step, enabling the system to accurately parse multi-step instructions and improving system security and reliability.

[0087] In other embodiments, the state transition function of the finite state machine Dynamic adjustments can be made based on the complexity of the instruction. For complex instructions, a hierarchical state machine can be introduced, divided into multiple sub-state machines to handle each stage of instruction parsing. Furthermore, the instruction parsing module can incorporate 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 of instruction parsing.

[0088] In one possible implementation, when parsing the operating instructions and determining the corresponding operating parameters, the instruction parsing module further combines historical operating data and the actual operating status of the device to dynamically adjust the range of the operating parameters to ensure safe and efficient operation of the device. The system will detect abnormalities in the instructions. If the operating parameters exceed the historical operating range of the device, the model will identify potential risks and issue a warning. This process is calculated using the following formula:

[0089] ,

[0090] in, Indicates parameters, Represents a given data, Indicates that given data Under these conditions, the parameters The conditional probability of Indicates that the parameter Under given conditions, the data The probability of Representation parameters The prior probability of Representation data The marginal probability of .

[0091] The instruction parsing module first analyzes the historical operation data of the equipment, extracts the operating parameter range of the equipment in different states, and dynamically adjusts the working range of the operating parameters based on 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 calculate the operating parameters. To calculate the conditional probability of the parameter. If the conditional probability of the parsed parameter value deviates significantly from the historical operating range, the system will consider that there is an anomaly, 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 by introducing historical data analysis and Bayesian inference, ensuring that the equipment operates within a safe range. 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 too 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, reducing human operational errors.

[0092] In other implementations, the model can employ other statistical methods or machine learning algorithms for anomaly detection, such as using decision trees, support vector machines (SVMs), or neural networks instead of Bayesian inference models to improve the accuracy and speed of anomaly detection. Furthermore, the system can incorporate predictive algorithms to proactively determine equipment operating trends and further optimize parameter adjustment strategies. For example, a time series analysis model (such as ARIMA) can be introduced to predict future equipment load changes, allowing for proactive adjustment of operating parameters.

[0093] In one possible implementation, the command generation module uses a dynamic priority scheduling algorithm to assign an execution order to each device when generating control commands, taking into account the requirements for coordinated operation of multiple devices. This scheduling algorithm dynamically adjusts the order in which control commands are issued based on the device's operating status, the urgency of the operation, and the priority of the task. The specific formula is:

[0094] ,

[0095] in, Indicates the index of the device, Indicates time, Representation device In time Priority, Representation device The corresponding task weight, Representation device Time from last execution to waiting time.

[0096] 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 , assign an appropriate execution order to each device. The priority of a device increases as its waiting time increases, thus avoiding a device 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 critical tasks. At the same time, the scheduling algorithm avoids the situation where some devices cannot be executed for a long time due to low priority by considering the waiting time of the equipment, thereby achieving fair and efficient resource scheduling.

[0097] In other embodiments, task weights Dynamic adjustments can be made based on different task types. For example, tasks with high real-time requirements can be assigned higher weights, giving them priority during scheduling. Furthermore, the scheduling algorithm can further adjust priorities based on the device's operating status (such as load or failure rate), taking more factors into account during scheduling to optimize overall system performance. Scheduling strategies can also be expanded to multi-level priority algorithms or combined with machine learning algorithms to automatically adjust the weight and priority assignment strategy for each device.

[0098] In one possible implementation, the control command sending module sends control commands to the smart substation to ensure synchronized execution of devices. Each device within the substation needs to coordinate and work at the same time, and the transmission of control commands must ensure synchronization between devices. To achieve this, the system uses distributed clock synchronization technology to ensure that devices execute received control commands synchronously with microsecond accuracy. The specific formula is:

[0099] ,

[0100] 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.

[0101] In smart substations, synchronized 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 is Time deviation due to network delay or device differences Adjustments are made to ensure that all devices execute commands synchronously with microsecond accuracy. This mechanism ensures coordination and consistency among devices during complex operations, preventing command misalignment or device failures due to time differences. This system ensures high-precision synchronization of devices within the substation through distributed clock synchronization technology. Synchronously executed control commands effectively improve the efficiency of collaborative work 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.

[0102] 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, fiber optic 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.

[0103] In one possible embodiment, the present invention further provides a smart substation data processing method, which ensures remote control and efficient data processing of the substation based on the smart substation remote control system. The method specifically includes the following steps:

[0104] 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 enters the specific operation requirements through the terminal, and the remote terminal sends the operation instructions to the substation control system through the network.

[0105] 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 checks (such as cyclic redundancy check);

[0106] S3: Parsing Operation Instructions: The instruction parsing module parses received operation instructions and determines the required operating parameters. Using a finite state machine (FSM) or other appropriate parsing mechanism, the system progressively processes each component of the instruction to ensure accurate execution. If necessary, the instruction parsing module also dynamically adjusts the operating parameter range based on the device's historical operating data and detects potential abnormal operations.

[0107] S4: Generate control commands. Based on the parsed operation parameters, the command generation module generates the corresponding control commands. This process combines the operating status of the device and the urgency of the operation to dynamically adjust the priority of tasks and ensure that the control commands can be executed collaboratively across 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 devices.

[0108] S5: Send and execute control commands. The generated control commands are transmitted to the smart substation devices via the control command sending module. The system uses distributed clock synchronization technology to ensure that each device executes the received control commands synchronously within microseconds, avoiding execution deviations caused by network delays or device differences and ensuring coordinated operation of the devices.

[0109] The entire smart substation data processing process starts with a remote control terminal. Users issue operational instructions through the terminal. The instruction receiving module ensures the secure transmission and reception of the instructions, and the instruction parsing module then parses and determines the operational parameters. The parsing results are used to generate control commands, which are then transmitted to the various devices within the smart substation for execution via distributed clock synchronization technology. The system ensures efficient coordination of equipment through dynamic priority scheduling and adaptive adjustment strategies. This method achieves remote automated operation of the smart substation through efficient instruction reception, parsing, and control command generation mechanisms. The introduction of dynamic priority scheduling and distributed clock synchronization technology ensures efficient coordination of equipment in complex operating environments. Historical data analysis and adaptive adjustment mechanisms enhance the flexibility and security of the system, enabling timely detection of abnormal operations and avoidance of potential failures. This method not only improves the operational efficiency of the substation but also ensures operational safety and reliability.

[0110] In other implementations, the command parsing process can incorporate more complex algorithms, such as machine learning models, to optimize the selection and adjustment of operating parameters. Control command generation can also incorporate additional scheduling strategies, such as those based on the real-time nature of the task or the current load status of the equipment, to further optimize operations. Furthermore, the system can further reduce command transmission delays by introducing redundant communication channels or high-speed communication technologies based on 5G, thereby improving the real-time and reliability of substation equipment responses.

[0111] In one possible implementation, the generation of the control command is not only based on the parsed operating parameters, but also includes performing 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 operation of the devices. Specifically, after the command generation module receives the operating parameters passed by the instruction parsing module, it first performs logical judgment 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:

[0112] 1. Conditional judgment

[0113] The system can use logical operations to make conditional judgments on operating parameters and select different control paths. ,in, is the real-time operating parameter of a device, For the preset threshold, the system generates control command A, otherwise it generates control command B;

[0114] 2. Combinatorial operations

[0115] When generating control commands for multiple devices, the system can logically combine multiple parameters, for example:

[0116] ;

[0117] This logic operation checks the status of multiple devices, ensuring that the corresponding control command is generated only when the parameters of two devices meet specific conditions. Similar combinations can also use logical operators such as "OR" and "XOR", depending on the operational requirements of the system.

[0118] 3. Arithmetic operations

[0119] During the command generation process, the system can also perform arithmetic operations on the operating parameters. For example, the weighted values ​​or correction coefficients of the operating parameters can be adjusted according to the real-time status of different devices to generate the optimal control command:

[0120] ,

[0121] in, and is the corresponding weight coefficient, and These are the operating parameters for different devices.

[0122] After receiving the parsed operating parameters, the command generation module uses logical operations (such as conditional judgment, combinational operations, and arithmetic operations) to further optimize the generation of control commands. These operations ensure that the operating parameters are consistent with the actual operating status of the equipment, ensuring that the generated control commands can be executed safely and efficiently. The results of the logical operations directly influence the final form of the control commands, which are then distributed 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, ensuring that the equipment can correctly respond to operating instructions in complex operating environments. Logical operations enhance the system's intelligence, allowing automatic adjustment of operating commands based on real-time conditions, thereby avoiding human errors and equipment damage. Logical operations that combine historical data and equipment status also help the system better adapt to emergencies or load fluctuations, further improving system safety and reliability.

[0123] In other implementations, the complexity of the logical operations can be expanded based on operational requirements. For example, more complex conditional control structures can be introduced, or fuzzy logic can be incorporated to accommodate decision-making under ambiguous conditions. Furthermore, the system can incorporate artificial intelligence-based logical operation algorithms, automatically adjusting and optimizing the selection strategy for operating parameters through machine learning models, thereby further improving the efficiency and accuracy of control command generation.

[0124] In one possible implementation, before sending a control command, the system checks the current status of the smart substation equipment to ensure that the equipment is operating normally and capable of executing the upcoming control command. This check is intended to prevent control command execution failures or malfunctions caused by equipment anomalies or unexpected status.

[0125] The status detection steps include:

[0126] 1. Equipment status monitoring

[0127] Before sending control commands, the system uses the device status monitoring module to monitor the current operating status of each device in real time. This monitoring includes, but is not limited to, device voltage, current, load, temperature, communication status, and historical operating data. If a device's status is abnormal (e.g., overload, overheating, or offline), the system issues an alert and temporarily suspends or adjusts the control command.

[0128] 2. Status threshold judgment

[0129] The system will determine whether the current state of the device is suitable for executing control commands based on the preset threshold. For example:

[0130] 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 block the command execution and issue a warning.

[0131] 3. Equipment operation history analysis

[0132] During status monitoring, the system can also analyze the device's historical operating data. For example, it can detect whether the device has repeatedly restarted or experienced failures within a short period of time and use this historical data to determine whether to continue executing the current control command. If the system detects frequent device anomalies, it may suspend control commands to prevent further damage to the device.

[0133] 4. Communication status check

[0134] Before sending a command, the system also checks 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 sending the control command to avoid data loss or command execution failure.

[0135] The status detection module ensures that devices can safely execute control commands through real-time monitoring and historical data analysis. The system communicates with devices to obtain real-time status data. Combined with the device's status threshold settings and historical records, it determines whether the device is in a normal state. If the device is in an abnormal state or does not meet execution conditions, the system will stop or adjust the issuance of control commands to ensure safe and reliable operation. The introduction of the status detection module can detect potential problems before command execution, avoiding operational risks caused by device anomalies. By performing device status detection before issuing control commands, the system significantly improves the safety and reliability of control command execution. Status detection ensures that devices can execute control commands safely, avoiding failures or damage caused by overload or abnormal conditions. Furthermore, by identifying potential problems in advance, the status detection module helps operations and maintenance personnel promptly detect equipment issues, reducing equipment downtime and improving the overall operational efficiency of the substation.

[0136] In other implementations, the status detection module can incorporate machine learning or artificial intelligence algorithms to predict a device's future state through big data analysis. For example, based on a device's historical status and real-time operational data, the system can predict impending failures and prematurely abort command execution. Furthermore, status detection can be combined with edge computing, enabling local devices to process detection data in real time, further improving the response speed and accuracy of status detection.

[0137] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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; An instruction receiving module connected to the remote control terminal, used to receive operation instructions from the remote control terminal; An instruction parsing module connected to the instruction receiving module is used to parse the operation instruction to determine the corresponding operation parameters; a command generation module connected to the instruction parsing module, configured to generate a substation control command based on the operation parameters; The control command sending module connected to the command generating module is used to send control commands to the smart substation and perform corresponding operations; 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 significantly, the control command automatically adjusts the operating parameters. The specific formula is: ， in, Indicates the generated control command, Indicates the expected operating parameter value, Indicates the current device status. represents the adaptive gain coefficient, represents the adaptive gain coefficient, Indicates time; Adaptive gain coefficients can be dynamically adjusted based on the different operating states of the equipment, or historical data can be analyzed through machine learning algorithms to predict the future load conditions of the equipment; 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. The command is executed locally on the substation edge computing node. The edge node receives the remote control command in real time and generates the corresponding sub-command locally. The command execution time is calculated using 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; By calculating the command generation time and command transmission time, evaluating the total execution time of each command, monitoring the efficiency of the operation execution process in real time, and monitoring the execution progress in real time, we can ensure that the operation is completed smoothly within the specified time.

2. The intelligent substation remote control system according to claim 1, characterized in that: The instruction receiving module receives an 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, the network layer receives the connection request from the remote control terminal and establishes a secure and encrypted communication connection 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 integrity check method includes a cyclic redundancy check.

3. The intelligent substation remote control system according to claim 2, characterized in that: When receiving instruction data, the operation instruction is checked for integrity. The integrity check method includes a cyclic redundancy check. The cyclic redundancy check is specifically: First, the command data to be transmitted is represented as a binary number. Before sending the data, the command data is expanded and zeros are added after it. The number of zeros depends on the number of bits of the generator polynomial. The expanded data is divided by the generator polynomial in binary. The highest bit of the data is first taken and XORed with the generator polynomial until all data bits are processed. The final remainder is the CRC check code. The generated CRC check code is appended to the end of the data to form the final transmission data packet. The instruction receiving module on the receiving end will perform the same binary division operation on the entire received data packet, including the instruction data and CRC check code. The receiving end performs the following operations on the received data: Divide the received data and the appended check code by 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 parameters, including: Use a finite state machine to parse the operation instructions and 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 instruction. Indicates the first status.

5. The intelligent substation remote control system according to claim 4, characterized in that: The instruction parsing module parsing the operation instruction to determine the corresponding operation parameter further includes: Analyze historical operation data and combine it with the actual equipment operating status to dynamically adjust the parameter range and detect abnormal instructions. If the operating parameters exceed the historical operating range of the equipment, the model identifies potential risks and issues a warning. The specific formula is: ， in, Indicates parameters, Represents a given data, Indicates that given data Under these conditions, the parameters The conditional probability of Indicates that the parameter Under 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, a dynamic priority scheduling algorithm is used to assign an execution order to each device based on the needs of multi-device collaborative operation. Dynamic scheduling adjusts the order in which control commands are issued based on the device's operating status, operation urgency, and task priority. The specific formula is: ， in, Indicates the index of the device, Indicates time, Representation device In time Priority, Representation device The corresponding task weight, Representation device Time from last execution to 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 performing corresponding operations further includes: In the substation, the devices work in coordination at the same time. The transmission of control commands ensures the synchronization between devices. 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 in 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 operating 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. The method for processing data of a smart substation 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 transmission control command, the current status of the smart substation equipment is detected.

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