Low-frequency load shedding cooperative control method, system and device based on GOOSE communication and readable storage medium

Abstract

The invention discloses a low-frequency load shedding cooperative control method, system and device based on GOOSE communication and a readable storage medium, and the method specifically comprises the steps: calling a topology analysis service to confirm that a home terminal is in a receiving end power grid, sending a state query request to an adjacent terminal, and receiving an action signal of the adjacent terminal through GOOSE communication; based on the dynamic load shedding sequence, executing load shedding operation; a marginal cost function of each terminal is calculated, distributed consensus verification is carried out in combination with a block chain technology, and then load shedding parameters are optimized, issued and updated; and after load shedding is completed, a fault section is positioned through residual voltage detection and direction discrimination, a personalized reconstruction scheme is generated by combining topology identification and fault type matching, and power supply of a non-fault section is recovered within a preset distance. According to the method, quick response, cooperative control, accurate load shedding, intelligent optimization and quick fault recovery of low-frequency load shedding are realized, and the frequency stability and the power supply reliability of a power system in a new energy high-proportion access scene are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a GOOSE communication-based low-frequency load shedding coordinated control method, system, device and readable storage medium. Background Art

[0002] With the transformation of the global energy structure, the proportion of new energy (such as wind power, photovoltaics, etc.) in the power system has increased day by day, forming a power system operation scenario with a high proportion of new energy access. However, new energy power generation has the characteristics of intermittency, volatility and uncertainty, which poses a severe challenge to the frequency stability of the power system. When new energy power generation is disconnected from the grid on a large scale or the load suddenly changes, the frequency of the power system may drop sharply. If effective control measures are not taken in time, it is very easy to cause the system frequency to collapse, which will lead to large-scale power outages, seriously threatening the safe and reliable operation of the power system.

[0003] In the existing technology, low-frequency load shedding devices are key equipment to ensure the stability of the power system frequency. They mainly prevent abnormal frequency drops by automatically cutting off non-critical loads. However, the low-frequency load shedding devices in the current distribution network mostly adopt a centralized control mode, which has many technical defects and is difficult to adapt to the frequency emergency control needs in the scenario of high proportion of new energy access. The specific performance is as follows:

[0004] 1. Response delay problem: Conventional low-frequency load shedding devices rely on the master station for centralized decision-making, and it usually takes 300-500ms from detecting the frequency limit to executing the load shedding operation. In the millisecond-level frequency drop scenario caused by the disconnection of new energy, this delay cannot meet the needs of rapid control, and may cause the system frequency to fail to recover in time, thereby causing more serious system oscillations or even collapse.

[0005] 2. Strategy rigidity: The load shedding strategy of existing low-frequency load shedding devices is mostly to cut off the load in fixed cycles, lacking the ability to dynamically adjust the load priority. In actual operation, the importance and interruptibility of different loads vary. The fixed-cycle load shedding method is prone to over-cutting or under-cutting, which not only wastes precious load resources, but also may aggravate system oscillations and affect the stable operation of the power system.

[0006] 3. Poor coordination: In the distribution network with high penetration of distributed power sources, there is a lack of effective coordination mechanism between multiple low-frequency load reduction terminals. When the system frequency is abnormal, each terminal may act at the same time, resulting in overload and affecting power supply reliability. At the same time, there is a lack of effective coordination between the existing feeder automation terminals and low-frequency protection devices, making it difficult to achieve synchronous execution of fault location, isolation and load control operations, further reducing the stability and reliability of power supply.

[0007] 4. Single criterion protection misoperation problem: Traditional low-frequency load shedding devices usually operate only based on frequency criteria, without fully considering the impact of factors such as voltage disturbances on frequency measurement. In actual operation, voltage disturbances may cause the illusion of frequency fluctuations, resulting in misoperation of low-frequency load shedding devices, cutting off unnecessary loads, and affecting the normal operation of the power system. Summary of the invention

[0008] The object of the present invention is to provide a low-frequency load shedding collaborative control method, system, device and readable storage medium based on GOOSE communication, which realizes rapid response, collaborative control, precise load shedding, intelligent optimization and rapid fault recovery of low-frequency load shedding, and significantly improves the frequency stability and power supply reliability of the power system in the scenario of high proportion of new energy access, so as to solve at least one of the above-mentioned prior art problems.

[0009] In a first aspect, the present invention provides a low-frequency load reduction coordinated control method based on GOOSE communication, the method specifically comprising:

[0010] By real-time monitoring of the frequency, voltage and phase angle parameters of the power system, the frequency change rate is calculated. When the frequency change rate exceeds the preset change rate threshold, the frequency protection thread is triggered;

[0011] After calling the topology analysis service to confirm that the local terminal is in the receiving-end power grid, it sends a status query request to the adjacent terminal and receives the action signal of the adjacent terminal through GOOSE communication. If the action signal of at least two adjacent terminals is received, the load shedding action of the local terminal is delayed and the topology check is started. Otherwise, it directly enters the load shedding execution stage.

[0012] Generate dynamic load shedding sequence according to the built-in programmable load characteristic database. Based on the dynamic load shedding sequence, perform load shedding operation through preset multi-level load shedding logic, and synchronously feed back the action signal to the master station;

[0013] The marginal cost function of each terminal is calculated at the main station based on the game theory model, and after distributed consensus verification using blockchain technology, the load shedding parameters are optimized and updated;

[0014] After the load shedding is completed, the faulty section is located through residual voltage detection and direction determination, and a personalized reconstruction plan is generated by combining topology identification and fault type matching to restore power supply to the non-faulty section within the preset distance.

[0015] In a second aspect, the present invention provides a low-frequency load reduction coordinated control system based on GOOSE communication, the system specifically comprising:

[0016] The hardware frequency measurement module is used to calculate the frequency change rate by real-time monitoring of the frequency, voltage and phase angle parameters of the power system. When the frequency change rate exceeds the preset change rate threshold, the frequency protection thread is triggered;

[0017] The collaborative control module is used to call the topology analysis service to confirm that the local terminal is in the receiving end power grid, send a status query request to the adjacent terminal, receive the action signal of the adjacent terminal through GOOSE communication, and if the action signal of at least two adjacent terminals is received, delay the load reduction action of the local terminal and start the topology verification, otherwise directly enter the load reduction execution stage;

[0018] Load priority decision module, used to generate dynamic load shedding sequence according to the built-in programmable load characteristic database, based on the dynamic load shedding sequence, perform load shedding operation through preset multi-level load shedding logic, and synchronously feed back the action signal to the master station;

[0019] The master station linkage module is used to calculate the marginal cost function of each terminal based on the game theory model at the master station, optimize the load shedding parameters and issue updates after distributed consensus verification combined with blockchain technology;

[0020] The self-healing reconstruction module is used to locate the faulty section through residual voltage detection and direction determination after load shedding is completed, and to generate a personalized reconstruction plan by combining topology identification and fault type matching to restore power supply to non-faulty sections within a preset distance.

[0021] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, a low-frequency load reduction collaborative control method based on GOOSE communication as described in any one of the above methods is implemented.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for coordinated control of low-frequency load reduction based on GOOSE communication as described in any one of the above methods is implemented.

[0023] Compared with the prior art, the present invention has at least one of the following technical effects:

[0024] 1. The present invention realizes rapid response, coordinated control, precise load shedding, intelligent optimization and rapid fault recovery of low-frequency load shedding, significantly improving the frequency stability and power supply reliability of the power system in scenarios with a high proportion of new energy access.

[0025] 2. Through real-time monitoring and distributed computing, the present invention can detect frequency anomalies within milliseconds and trigger protection actions, significantly shortening the response time and meeting the control requirements of millisecond-level frequency drop scenarios caused by new energy grid disconnection.

[0026] 3. Based on dynamic load shedding sequence and multi-level load shedding logic, the present invention can accurately grade and cut off the load according to its importance and interruptibility, thus avoiding over-cutting or under-cutting, improving the utilization efficiency of load resources and reducing the risk of system oscillation.

[0027] 4. The present invention realizes information interaction and coordinated control among multiple terminals through GOOSE communication, ensuring that each terminal can synchronously perform fault location, isolation and load control operations when the system frequency is abnormal, thereby improving the reliability and stability of power supply.

[0028] 5. The present invention combines game theory models and blockchain technology to dynamically optimize load shedding parameters, realize intelligent adjustment of load shedding strategies and distributed consensus verification, and improve the scientificity and accuracy of load shedding decisions.

[0029] 6. The present invention can quickly locate the faulty section and generate a personalized reconstruction plan through technical means such as residual voltage detection, direction determination and topology identification, so as to realize rapid power restoration of non-faulty sections and further improve the power supply reliability and stability of the power system.

[0030] 7. The present invention can quickly and accurately trigger the frequency protection thread by real-time acquisition of three-phase voltage signal parameters and accurate calculation of the frequency change rate, thereby improving the low-frequency load reduction response speed and accuracy.

[0031] 8. The present invention utilizes topological analysis to confirm the receiving-end power grid and coordinates the action signals of adjacent terminals, thereby avoiding overload caused by simultaneous action of multiple terminals and improving the coordination and reliability of low-frequency load shedding.

[0032] 9. The present invention generates a dynamic load reduction sequence based on a programmable load characteristic database, which can achieve accurate graded load reduction according to load characteristics, ensure power supply to key loads, and improve the rationality of load reduction.

[0033] 10. The present invention executes multi-level load shedding logic based on a dynamic load shedding sequence and synchronously feeds back action signals, which can achieve accurate load shedding and real-time monitoring, and provide data support for load shedding parameter optimization.

[0034] 11. The master station of the present invention optimizes load shedding parameters based on game theory models and blockchain technology, which can minimize load shedding costs and dynamically adjust parameters, thereby improving the rationality and adaptability of load shedding strategies.

[0035] 12. After load shedding, the present invention generates a personalized reconstruction plan through residual voltage detection and topology identification, which can quickly restore power supply to non-fault sections, reduce the scope of power outages, and improve power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a flow chart of a low-frequency load shedding coordinated control method based on GOOSE communication provided by one embodiment of the present invention;

[0038] Figure 2 It is a structural diagram of a low-frequency load reduction cooperative control system based on GOOSE communication provided by an embodiment of the present invention;

[0039] Figure 3 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0041] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0042] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0043] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0044] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0045] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0046] In the embodiment of the present application, the execution subject of the process includes a terminal device, which includes but is not limited to: a server, a computer, a smart phone, a tablet computer, and other devices capable of executing the method disclosed in the present application. Figure 1 A flow chart of a low-frequency load shedding coordinated control method based on GOOSE communication disclosed in the first embodiment of the present invention is shown, and the details are as follows:

[0047] S101, by real-time monitoring of the frequency, voltage and phase angle parameters of the power system, the frequency change rate is calculated, and when the frequency change rate exceeds a preset change rate threshold, the frequency protection thread is triggered.

[0048] In this embodiment, the frequency, voltage and phase angle parameters of the power system are monitored in real time to calculate the frequency change rate. When the frequency change rate exceeds a preset change rate threshold, the frequency protection thread is triggered to achieve fast and accurate frequency control.

[0049] Specifically, high-precision synchronous phasor measurement units (PMUs) are deployed at each terminal of the distribution network (such as substations, distributed power access points, etc.) to collect frequency, voltage and phase angle parameters of the power system in real time. The sampling frequency of the PMU is not less than 10kHz to ensure high accuracy and real-time performance of parameter measurement.

[0050] The terminal has a built-in frequency change rate calculation module, which uses a sliding window algorithm to process the collected frequency data in real time. The specific calculation method is as follows: Set the sliding window length to N (such as N = 100 sampling points), and the corresponding time window length is T = N / sampling frequency (such as T = 10ms). In each time window, calculate the average frequency and calculate the frequency change rate of two adjacent time windows. ,in, represents the frequency average of the current time window, represents the frequency average of the previous time window, Indicates the length of the time window. According to the power system operation experience and simulation analysis, the frequency change rate threshold is set. When the frequency change rate exceeds the threshold, the frequency protection thread is triggered.

[0051] In this embodiment, the real-time calculation of the frequency change rate is realized through high-precision PMU and sliding window algorithm, and the response time is shortened to 10ms, which is much lower than the 300-500ms delay of the traditional low-frequency load reduction device. The setting of the frequency change rate threshold is based on the actual operating characteristics of the power system, avoiding false operation caused by frequency fluctuation illusions and improving the accuracy of frequency protection.

[0052] S102, after calling the topology analysis service to confirm that the local end is in the receiving end power grid, send a status query request to the adjacent terminal, receive the action signal of the adjacent terminal through GOOSE communication, if the action signal of at least two adjacent terminals is received, delay the load reduction action of the local end and start the topology verification, otherwise directly enter the load reduction execution stage.

[0053] In this embodiment, when multiple low-frequency load reduction terminals act independently, it is easy to cause over- or under-load cutting, affecting the stable operation of the power system. Therefore, the present invention confirms the location of the local power grid by calling the topology analysis service, and makes collaborative decisions based on the action signals of adjacent terminals to achieve precise load reduction control.

[0054] Specifically, a topology analysis service module is built into each terminal of the distribution network (such as a substation, distributed power access point, etc.). This module obtains the topological structure information of the current power grid by interacting with the master station or adjacent terminals in real time. The terminal determines whether the local end is in the receiving end power grid based on the information returned by the topology analysis service. The specific judgment logic is as follows: If the power flow direction of the local power grid is in the receiving direction (that is, power flows into the local power grid from other areas), it is determined that the local end is in the receiving end power grid; if the power flow direction of the local power grid is in the sending direction (that is, power flows out of the local power grid to other areas), it is determined that the local end is in the sending end power grid.

[0055] When the terminal confirms that it is in the receiving power grid, it immediately sends a status query request to the adjacent terminal through GOOSE communication. GOOSE communication is a high-speed and reliable substation automation communication protocol suitable for scenarios with high real-time requirements. After receiving the status query request, the adjacent terminal generates an action signal based on its own operating status (such as whether load shedding has been started, current frequency value, etc.) and returns it to the requesting terminal through GOOSE communication. The action signal is binary coded, for example, "1" indicates that load shedding has been started, and "0" indicates that load shedding has not been started.

[0056] After receiving the action signal from the adjacent terminal, the requesting terminal makes a judgment based on the preset collaborative decision logic: if the action signal is received from at least two adjacent terminals (that is, at least two adjacent terminals have started load shedding), the load shedding action on this end is delayed and the topology verification is started; if the action signal is not received from at least two adjacent terminals, the load shedding execution phase is directly entered.

[0057] During the delayed load shedding action, the terminal starts the topology verification process, and the specific steps are as follows: the terminal collects real-time operating data of the terminal and adjacent terminals, including frequency, voltage, flow direction, etc.; based on the collected data, the terminal reconstructs the topology of the power grid and analyzes the position and role of the local power grid in the overall power grid; based on the topology reconstruction results, the terminal adjusts the load shedding strategy of the terminal, such as reducing the load shedding amount, adjusting the load shedding sequence, etc., to avoid over- or under-loading. After the collaborative decision-making or topology verification is completed, the terminal performs the load shedding operation according to the adjusted load shedding strategy, and synchronously feeds back the action signal to the master station through GOOSE communication.

[0058] In this embodiment, through the topology analysis service, the terminal can obtain the topological structure information of the power grid in real time, accurately determine the location of the local power grid, and provide a basis for subsequent collaborative decision-making. Through GOOSE communication, the terminal can exchange information with adjacent terminals in real time to ensure the timeliness and accuracy of collaborative decision-making. Through the interaction of action signals and collaborative decision-making logic between adjacent terminals, overload caused by simultaneous action of multiple terminals is avoided, and the coordination and stability of the system are improved. The topology verification process can adjust the load reduction strategy according to the real-time topology of the power grid, realize precise load reduction control, and reduce resource waste and system oscillation. Through collaborative decision-making and topology verification, precise load reduction control is achieved, the risk of overload is reduced, and the stability of the system and power supply reliability are improved.

[0059] S103, generating a dynamic load shedding sequence according to the built-in programmable load characteristic database, and executing the load shedding operation through the preset multi-level load shedding logic based on the dynamic load shedding sequence, and synchronously feeding back the action signal to the master station.

[0060] In this embodiment, the traditional low-frequency load shedding device usually adopts a fixed load shedding strategy, which is difficult to adapt to the dynamic changes of loads and the complexity of the grid topology under the high penetration rate of new energy. A single load shedding strategy may cause over- or under-loading of the load, affecting the stability of the system and the reliability of power supply. To solve the above problems, the present invention generates a dynamic load shedding sequence through a built-in load characteristic database, combines multi-level load shedding logic to achieve hierarchical load shedding, and synchronously feeds back the action signal to the master station to achieve precise load shedding control.

[0061] Specifically, a programmable load characteristic database is built into the terminal equipment (such as substation RTU, distributed power supply controller, etc.), which contains dynamic characteristic parameters of different types of loads, such as frequency-load response curve, recovery time, importance level, etc. The database supports online update, and through data interaction with the master station or adjacent terminals, it obtains real-time information on changes in load characteristics (such as new energy access, load fluctuations, etc.), and dynamically adjusts the database content.

[0062] The terminal equipment collects the operating data of the power grid frequency, voltage, power flow, etc. in real time, and evaluates the load importance by combining the load characteristic parameters in the programmable load characteristic database. Based on the evaluation results, an optimization algorithm (such as genetic algorithm, particle swarm algorithm, etc.) is used to generate a dynamic load shedding sequence. The sequence contains information such as the priority, shedding order, and shedding amount of the load to be shelved.

[0063] According to the dynamic load shedding sequence, the terminal equipment uses multi-level load shedding logic to perform load shedding operations. The multi-level load shedding strategy includes but is not limited to: Level 1 load shedding: shedding the lowest priority load, triggering the frequency recovery threshold 1; Level 2 load shedding: if the frequency does not recover to threshold 1, shedding the second lowest priority load, triggering the frequency recovery threshold 2; Level 3 load shedding: and so on, until the frequency returns to the normal range.

[0064] The terminal equipment removes the load step by step according to the multi-level load reduction strategy and the order of the dynamic load reduction sequence; after each level of load reduction, the terminal equipment monitors the changes in the grid frequency in real time to determine whether it is necessary to enter the next level of load reduction.

[0065] After each level of load reduction is completed, the terminal device generates a corresponding action signal, including the load reduction level, load removal information, current grid frequency, etc. The terminal device synchronously feeds back the action signal to the master station through GOOSE communication or IEC 61850 protocol. After receiving the signal, the master station performs global status evaluation and strategy optimization.

[0066] For example, assume that there are multiple load nodes in a distribution network, including industrial loads, commercial loads, residential loads, etc., and the proportion of new energy access is relatively high. When the system frequency drops, the terminal device executes a dynamic and precise load reduction method based on a programmable load characteristic database and multi-level load reduction logic. First, the terminal device collects the grid frequency (such as 49.5Hz) and load data in real time; combines the programmable load characteristic database to evaluate the importance of each load; uses an optimization algorithm to generate a dynamic load reduction sequence, for example: prioritize the removal of commercial load B (high priority, short recovery time); secondly, remove industrial load C (medium priority, medium recovery time); and finally retain residential load A (low priority, long recovery time). Then, perform multi-level load reduction, in which, in the first level of load reduction, commercial load B is removed and the monitoring frequency is restored to 49.7Hz; in the second level of load reduction, if the frequency has not recovered to the threshold (such as 50Hz), industrial load C is removed and the frequency is restored to 49.9Hz; in the third level of load reduction, if the frequency has not recovered, the residential load A is retained and no further load reduction is performed. After each level of load reduction is completed, the terminal device generates an action signal and feeds it back to the master station; after receiving the signal, the master station performs a global status assessment to confirm the system frequency recovery.

[0067] In this embodiment, accurate load shedding is achieved through dynamic load shedding sequence and multi-level load shedding logic, important loads are retained preferentially, and the stability of the system and power supply reliability are improved. The programmable load characteristic database can adapt to the changes in different load characteristics and provide basic data support for the generation of dynamic load shedding sequences. The dynamic load shedding sequence is generated in real time according to the load characteristics and the operating status of the power grid, and can accurately match the load shedding requirements to avoid over- or under-loading of the load. The multi-level load shedding logic avoids the "one-size-fits-all" problem of traditional load shedding strategies by shedding loads in stages, and improves the flexibility and accuracy of load shedding control. The synchronous feedback of the action signal realizes information sharing between the master station and the terminal equipment, and provides data support for the global collaborative control of the master station.

[0068] S104, the marginal cost function of each terminal is calculated at the main station based on the game theory model, and after distributed consensus verification combined with blockchain technology, the load shedding parameters are optimized and updated.

[0069] In this embodiment, the traditional power system load shedding parameter optimization method usually relies on centralized control, which has the risk of single point failure, low computational efficiency, and difficulty in adapting to multi-agent collaborative decision-making. With the widespread application of distributed energy and intelligent terminals, there is an urgent need for a decentralized, safe, reliable and efficient load shedding parameter optimization method. To solve the above problems, the present invention calculates the marginal cost function of each terminal based on the game theory model at the master station, combines blockchain technology to perform distributed consensus verification, and optimizes the load shedding parameters and securely issues updates.

[0070] Specifically, in the master station, a game model containing multiple terminal devices (such as distributed power supply controllers, load control terminals, etc.) is constructed. Each terminal is regarded as a game participant, and its strategy space includes load reduction amount, load reduction timing, etc. According to the game model, the master station calculates the marginal cost function of each terminal based on the load characteristics of each terminal, new energy power generation forecast, power grid operation status and other information. The marginal cost function reflects the cost changes of the terminal during the load reduction process, including economic losses, equipment losses, etc.

[0071] A blockchain network is built between the main station and each terminal. Each terminal acts as a blockchain node and is responsible for storing and verifying transaction information. The blockchain uses consensus algorithms (such as PBFT, Raft, etc.) to ensure data consistency and security. The main station broadcasts the calculated marginal cost function of each terminal as transaction information to the blockchain network; each terminal node verifies the transaction information and reaches a consensus through the consensus algorithm to confirm the correctness of the marginal cost function.

[0072] Based on the marginal cost function of each terminal and the blockchain consensus verification results, the master station uses optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.) to calculate the optimal load shedding parameters. The optimal load shedding parameters include the load shedding amount and load shedding timing of each terminal, aiming to minimize the total system cost. The master station broadcasts the optimized load shedding parameters as new transaction information to the blockchain network; each terminal node verifies the new transaction information and reaches a consensus through the consensus algorithm to confirm the correctness of the parameter update; after the verification is passed, the master station sends the new load shedding parameters to each terminal, and the terminal performs the load shedding operation according to the new parameters.

[0073] In this embodiment, the marginal cost function based on game theory can accurately quantify the cost changes of each terminal during the load shedding process, providing a scientific basis for subsequent parameter optimization. Blockchain technology realizes decentralized distributed consensus verification, avoids the risk of single point failure, and improves the reliability and security of the system. The consensus algorithm ensures the consistency and immutability of the data, providing a reliable data basis for subsequent parameter optimization. The parameter optimization method based on game theory and blockchain technology can achieve the global optimal load shedding parameter configuration, improving the economy and stability of the system. Blockchain technology enables parameter updates to be quickly and securely transmitted to each terminal, realizing dynamic adjustment of load shedding parameters. Based on game theory and blockchain technology, decentralized parameter optimization and update are realized, improving the reliability and economy of the system.

[0074] S105, after the load shedding is completed, the faulty section is located through residual voltage detection and direction determination, and a personalized reconstruction plan is generated by combining topology identification and fault type matching to restore power supply to the non-faulty section within a preset distance.

[0075] In this embodiment, the traditional power grid fault location and power supply restoration method usually relies on centralized control or a single criterion, and has problems such as low fault location accuracy, long power supply restoration time, and difficulty in adapting to complex topological structures. To solve the above problems, the present invention accurately locates the faulty section after load shedding is completed, combines topological information and fault type to generate a personalized reconstruction plan, and realizes rapid power supply restoration of non-faulty sections.

[0076] Specifically, after the load shedding operation is completed, the master station collects voltage data from each terminal (such as distribution switches, distributed power supply controllers, etc.) and calculates the residual voltage value (i.e., the voltage amplitude after the fault). By comparing the residual voltage value with the preset threshold, the section where the fault may exist is preliminarily screened out. The fault section is further located by using the direction information of the fault current and combining the current phase difference of each terminal. By comparing the consistency of the fault current direction with the preset direction, the fault range is narrowed.

[0077] The master station constructs a topological model of the power grid based on the topological structure of the power grid, combined with the connection relationship and switch status of each terminal. The isolation status of the non-fault section and the fault section is determined through topological recognition technology. According to the residual voltage value, fault current characteristics and other information of the fault section, the preset fault type library is matched to determine the fault type (such as short circuit fault, ground fault, etc.). Combining the topological model and fault type, the master station uses optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) to generate personalized reconstruction plans. The plan includes the power supply recovery path of the non-fault section, the switch operation sequence, etc., aiming to minimize the power supply recovery time and impact range.

[0078] According to the personalized reconstruction plan, the master station selects the optimal power supply restoration path. Path selection considerations include path length, number of switch operations, load transfer capacity, etc. The master station sends switch operation instructions to the relevant terminals, and the terminals perform switch operations according to the instructions to restore power supply to the non-faulty sections. During the power supply restoration process, ensure that the length of the restoration path does not exceed the preset distance (such as 5 kilometers) to reduce the impact on other parts of the power grid.

[0079] For example, suppose a short circuit fault occurs in a distribution network. After the master station stabilizes the system through load shedding operation, it first performs residual voltage detection and direction discrimination. For example, the residual voltage value of terminal A is 45V, and the residual voltage value of terminal B is 110V. It is preliminarily determined that there may be a fault in the section where terminal A is located; the direction of the fault current of terminal A is opposite to the preset direction, and the direction of the fault current of terminal B is consistent with the preset direction, and it is determined that the fault section is located downstream of terminal A. The topological model shows that there is a remotely controlled switch between terminal A and terminal B, and the switch is in the disconnected state, and the topological identification result is obtained; the residual voltage value of the fault section is low and the fault current amplitude is large, which is matched as a short circuit fault, and the fault type matching result is obtained; according to the topological identification result and the fault type matching result, a personalized reconstruction plan is generated: close the standby switch between terminal C and terminal D to restore power supply to the non-fault section. According to the reconstruction plan, the path of terminal C→standby switch→terminal D→non-fault section is selected. After receiving the instruction, terminal C and terminal D close the standby switch, and the non-fault section resumes power supply. The length of the restoration path is 3 kilometers, which meets the preset distance constraint.

[0080] In this embodiment, by combining residual voltage detection and direction discrimination, the fault section can be quickly and accurately located, thereby improving the accuracy of fault location. Combined with topology identification and fault type matching, a personalized reconstruction plan for a specific fault scenario can be generated to improve the efficiency and adaptability of power supply restoration. Through personalized reconstruction plans and preset distance constraints, power supply to non-fault sections can be restored in a short time, reducing power outage time and impact range. Based on residual voltage detection, direction discrimination, topology identification and fault type matching, accurate positioning and personalized reconstruction of the fault section are achieved, significantly improving the efficiency and adaptability of power supply restoration.

[0081] In some embodiments, in the above step S101, the frequency, voltage and phase angle parameters of the power system are monitored in real time to calculate the frequency change rate, and when the frequency change rate exceeds a preset change rate threshold, the frequency protection thread is triggered, specifically including:

[0082] Real-time acquisition of three-phase voltage signal parameters of the power system, wherein the three-phase voltage signal parameters include frequency, voltage and phase angle;

[0083] Based on the three-phase voltage signal parameters, the instantaneous frequency is calculated by the equal-precision frequency measurement method;

[0084] Based on the instantaneous frequency, the frequency change rate is obtained by differential calculation within multiple sliding time windows;

[0085] The change rate threshold is dynamically adjusted according to the system inertia constant and the new energy penetration rate. When it is detected that the frequency change rate is greater than the change rate threshold, the frequency protection thread is immediately triggered.

[0086] In this embodiment, as the proportion of new energy (such as wind energy and solar energy) in the power system continues to increase, the frequency stability of the power system faces new challenges. Traditional frequency protection methods usually use fixed thresholds, which are difficult to adapt to the changes in the dynamic characteristics of the system brought about by the access of new energy, and may cause protection misoperation or refusal to operate. Therefore, there is an urgent need for a method that can dynamically adjust the change rate threshold and improve the reliability of frequency protection. To solve the above problems, the present invention monitors the frequency, voltage and phase angle parameters of the power system in real time, calculates the frequency change rate, and dynamically adjusts the change rate threshold according to the system inertia constant and the new energy penetration rate, triggers the frequency protection thread, and improves the accuracy and adaptability of frequency protection.

[0087] Specifically, high-precision voltage sensors are arranged at key nodes of the power system (such as power plants, substations, new energy grid connection points, etc.) to collect three-phase voltage signal parameters in real time, including frequency, voltage and phase angle. The equal-precision frequency measurement method is used to measure the frequency of the preprocessed voltage signal. The equal-precision frequency measurement method achieves high-precision frequency measurement by comparing the phase difference between the measured signal and the reference signal. Set multiple sliding time windows (such as 10ms, 50ms, 100ms, etc.), and calculate the frequency change rate in each time window. Obtain the inertia constant of the power system through system identification or online estimation methods. Calculate the new energy penetration rate based on the ratio of new energy power generation to the total system power generation. Dynamically adjust the threshold of the frequency change rate based on the system inertia constant and the new energy penetration rate. The threshold adjustment formula is: ,in, represents the change rate threshold, represents the proportionality coefficient, represents the system inertia constant, represents the weight coefficient of new energy penetration rate, Indicates the penetration rate of new energy. When it is detected that the frequency change rate is greater than the dynamically adjusted threshold, the frequency protection thread is immediately triggered and the corresponding protection action (such as machine cutting, load shedding, etc.) is executed.

[0088] For example, assume that in a certain regional power grid, the proportion of renewable energy power generation is 25%, and the system inertia constant is 5s. At a certain moment, due to the sudden drop in renewable energy power generation, the system frequency begins to drop rapidly. A three-phase voltage sensor is arranged at the wind farm grid connection point to collect voltage signals in real time with a sampling frequency of 10kHz. The instantaneous frequency is calculated using the equal-precision frequency measurement method with a measurement accuracy of 0.001Hz. Within the 10ms time window, the differential value of the instantaneous frequency is calculated to obtain the frequency change rate. According to the system inertia constant of 5s and the renewable energy penetration rate of 25%, the change rate threshold is dynamically adjusted to 2.5Hz / s. When the frequency change rate is detected to be greater than 2.5Hz / s, the frequency protection thread is immediately triggered to cut off part of the renewable energy power generation to prevent the system frequency from further decreasing.

[0089] In this embodiment, by dynamically adjusting the change rate threshold, the accuracy and adaptability of frequency protection are improved, and the probability of protection misoperation and refusal is reduced. By real-time monitoring of the frequency, voltage and phase angle parameters of the power system, calculating the frequency change rate, and dynamically adjusting the change rate threshold according to the system inertia constant and new energy penetration rate, the frequency protection thread is triggered, which significantly improves the accuracy and adaptability of frequency protection, and provides a strong guarantee for complex power systems with a high proportion of new energy access.

[0090] In some embodiments, in the above step S102, after the calling of the topology analysis service confirms that the local end is in the receiving end power grid, a status query request is sent to the adjacent terminal, and an action signal of the adjacent terminal is received through GOOSE communication. If the action signal of at least two adjacent terminals is received, the load reduction action of the local end is delayed and the topology check is started, otherwise the load reduction execution phase is directly entered, which specifically includes:

[0091] Call the topology analysis service to build a power grid equivalent model based on the node admittance matrix and real-time measurement data;

[0092] By solving the power grid equivalent model, it is determined whether the local end is a receiving end power grid. If it is a receiving end power grid, a status query request in GOOSE format is sent to the adjacent terminal, and the status query request includes a terminal ID, a timestamp and an action flag;

[0093] Receive action signals from adjacent terminals through GOOSE communication, count the number of actions, and if the number of actions is greater than or equal to 2, delay the load reduction action at the local end;

[0094] In the delay phase, the topology deviation rate is calculated based on the real-time measurement data. If the topology deviation rate is less than the preset deviation rate threshold, the load reduction operation is performed, otherwise the action is terminated and reported to the master station.

[0095] In this embodiment, in the power system, when a fault or overload occurs, the receiving end power grid needs to respond quickly and perform load reduction operations to maintain system stability. However, existing load reduction methods are mostly based on local measurement data and lack a global analysis of the power grid topology, which can easily lead to misjudgment or uncoordinated load reduction actions. The present invention improves the accuracy and reliability of load reduction actions through global topology analysis and coordinated control of adjacent terminals.

[0096] For example, a regional power grid includes three adjacent substations A, B, and C. Substation A is the receiving-end power grid, and substations B and C are the sending-end power grids. When substation A detects an overload, it needs to cooperate with adjacent substations to perform load reduction operations. The monitoring system of substation A collects node admittance matrices (such as the admittance matrix Y_A of station A) and measurement data (such as voltage U_A and current I_A) in real time. An equivalent model of station A is constructed based on Y_A and measurement data, and power flow calculation is used to determine whether station A is a receiving-end power grid (such as the power inflow is greater than the outflow). If station A is a receiving-end power grid, a status query request in GOOSE format is generated, including: terminal ID: unique identifier of station A (such as "A_ID"); timestamp: current time (such as "2023-10-1012:00:00"); action flag: initial state (such as "0" means no action). Send a request to adjacent stations B and C through the GOOSE communication protocol. After receiving the request, if Station B and Station C detect an overload, they reply with an action signal (such as "1" indicates that the action has been taken). Station A counts the number of action signals received. If the number of actions is ≥2 (such as Station B and Station C both reply "1"), Station A delays the load reduction action and starts the topology check; otherwise, Station A directly enters the load reduction execution phase. During the delay phase (such as 100ms), Station A recalculates the topology deviation rate (such as |ΔP| / P_rated, ΔP is the power deviation, and P_rated is the rated power) based on real-time measurement data. If the deviation rate is less than the preset threshold (such as 5%), the load reduction operation is performed (such as cutting off part of the load); otherwise, the action is terminated and reported to the master station (such as sending an alarm message "Topology deviation is too large, load reduction is terminated").

[0097] In this embodiment, the receiving power grid is judged globally through the topology analysis service to avoid misjudgment; the adjacent terminals are collaboratively controlled based on GOOSE communication to ensure the coordination and consistency of load reduction actions; the topology verification mechanism prevents false operations caused by topology changes and ensures the safe operation of the power grid.

[0098] In some embodiments, in the above step S103, generating a dynamic load reduction sequence according to a built-in programmable load characteristic database specifically includes:

[0099] Through the built-in programmable load characteristics database, call the user's important level label, real-time power factor and load type parameters of each load;

[0100] Based on the user's importance level label, real-time power factor and load type parameters, the score of each load is calculated through the priority scoring formula, and a dynamic load reduction sequence is generated in descending order of the score;

[0101] When it is detected that the critical load score is less than the preset score threshold, a safety alarm is triggered and automatic load reduction is suspended, waiting for manual confirmation;

[0102] The LSTM network is used to predict and update the label weight and dynamic coefficient of each load, and then sent to the terminal through the master station.

[0103] In this embodiment, in the power system, the load shedding operation needs to comprehensively consider the importance, power factor and type of the load to avoid affecting the critical load. However, traditional load shedding methods are mostly based on fixed rules and lack the ability to dynamically adjust load characteristics, which can easily lead to unreasonable load shedding strategies. The present invention realizes dynamic optimization of load shedding strategies through load characteristic database and machine learning prediction.

[0104] For example, a regional power grid contains multiple load nodes, including industrial loads (such as steel mills), commercial loads (such as shopping malls) and residential loads (such as residential areas). When the power grid detects an overload, a dynamic load reduction sequence needs to be generated based on the load characteristics. Through the built-in programmable load characteristics database, the user's important level label (such as "high", "medium", "low"), real-time power factor (such as 0.85, 0.90, 0.95) and load type parameters (such as "industrial", "commercial", "residential") of each load are called. The priority score is calculated based on the user level (weight 0.5), power factor (weight 0.3) and load type (weight 0.2): score = user level weight × level value + power factor weight × power factor + load type weight × type value, level value mapping: high = 3, medium = 2, low = 1; type value mapping: industrial = 3, commercial = 2, residential = 1. Among them, the score of load 1 = 0.5×3+0.3×0.95+0.2×3=2.835; the score of load 2 = 0.5×2+0.3×0.90+0.2×2=2.07; the score of load 3 = 0.5×1+0.3×0.85+0.2×1=1.305 are arranged in descending order of the scores to generate a dynamic load reduction sequence load 1→load 2→load 3. If it is detected that the score of a critical load (such as load 1) is less than the preset score threshold (such as 2.5), a safety alarm is triggered and automatic load reduction is suspended. The operation and maintenance personnel confirm whether to continue load reduction or adjust the load reduction strategy through the main station interface. Use historical load data to train the LSTM network to predict the future trend of load characteristic changes. For example, the predicted value of the future power factor of load 1 = 0.96, and the predicted value of load type = industrial (remain unchanged). The label weight and dynamic coefficient of the load are updated according to the prediction results (for example, the user level weight of load 1 is adjusted to 0.6, the power factor weight is adjusted to 0.25, and the load type weight is adjusted to 0.15), and sent to the terminal through the master station.

[0105] In this embodiment, dynamic priority scoring is used to accurately identify the importance of loads and avoid mistakenly reducing critical loads. The safety alarm mechanism prevents misoperation of critical loads and ensures stable operation of the power grid. The LSTM prediction and parameter update mechanism enables the load reduction strategy to adapt to dynamic changes in load characteristics and improve the load reduction effect.

[0106] In some embodiments, in the above step S103, the load shedding operation is performed based on the dynamic load shedding sequence through a preset multi-level load shedding logic, and the action signal is synchronously fed back to the master station, specifically including:

[0107] When the frequency drop triggers the standard load shedding round, the load is removed according to the preset frequency threshold and load shedding ratio;

[0108] If the frequency change rate exceeds the dynamic threshold, a special load shedding round is activated, the dynamic load shedding amount is calculated by the slope sensitivity coefficient, and the interruptible load is preferentially removed from the end of the dynamic load shedding sequence;

[0109] The timestamp, terminal ID, cut-off power and average power factor of the load shedding action are encapsulated into a JSON message and synchronously fed back to the master station through the MQTT protocol;

[0110] After receiving data from multiple terminals, the master station uses the LSTM network to predict the load reduction parameters for the next cycle.

[0111] In this embodiment, in the power system, when the frequency drops or the frequency change rate is abnormal, the load needs to be quickly removed to restore system stability. Traditional load shedding methods are mostly based on fixed rules and lack the ability to dynamically adjust load characteristics, which can easily lead to unreasonable load shedding strategies or mis-cutting of critical loads. The present invention achieves accurate execution of load shedding strategies through preset logic and dynamic parameter optimization.

[0112] Among them, standard round (frequency threshold trigger):

[0113] Preset 6-wheel load shedding threshold , load reduction per round ,in, It indicates the load reduction in the mth round of the standard round. Indicates the load reduction ratio coefficient of the standard round, Indicates the current total load power. Indicates the load shedding threshold of the mth round of standard rounds.

[0114] Among them, special rounds (triggered by frequency change rate):

[0115] Dynamically correct the load reduction according to the real-time frequency change rate , ,in, Indicates the load shedding amount in the nth round of a special round, It represents the slope sensitivity coefficient of the nth special round. Indicates the real-time frequency change rate, represents the instantaneous frequency within any sliding time window, Represents the time step within any sliding time window.

[0116] For example, the power grid frequency monitoring system detects that the frequency drops to 49.5Hz (preset frequency threshold), triggering a standard load shedding round. According to the preset load shedding ratio (such as 10%), the load to be removed is calculated. According to the dynamic load shedding sequence, the low priority load (such as residential load) at the end of the sequence is preferentially removed. The power grid frequency change rate monitoring system detects that the frequency change rate exceeds the dynamic threshold (such as 0.5Hz / s), activating a special load shedding round. The dynamic load shedding amount is calculated by the slope sensitivity coefficient, and the interruptible load (such as commercial load) is preferentially removed from the end of the dynamic load shedding sequence. The timestamp, terminal ID, cut power and average power factor of the load shedding action are encapsulated into a JSON message, and the message is synchronously fed back to the master station through the MQTT protocol. The master station receives the load shedding data uploaded by multiple terminals to form a historical data set. The LSTM network is trained using the historical data set to predict the load shedding parameters (such as frequency threshold, load shedding ratio, slope sensitivity coefficient) of the next cycle. The predicted load shedding parameters are sent to each terminal through the master station to achieve dynamic optimization of the load shedding strategy.

[0117] In this embodiment, multi-level load shedding logic and dynamic load shedding sequence are used to accurately identify and remove low-priority loads to avoid mis-cutting critical loads. Special load shedding rounds are combined with slope sensitivity coefficients to quickly respond to abnormal frequency change rates and improve grid stability. The LSTM prediction and parameter update mechanism enables the load shedding strategy to adapt to changes in grid operating conditions and improve load shedding effects.

[0118] In some embodiments, in the above step S104, the master station calculates the marginal cost function of each terminal based on the game theory model, optimizes the load shedding parameters after distributed consensus verification in combination with blockchain technology, and sends updates, specifically including:

[0119] Considering each terminal as a participant, minimizing the total cost of regional load shedding is taken as the objective function, and a non-cooperative game model is established.

[0120] Based on the non-cooperative game model, the master station solves the optimal load reduction and unified marginal cost of each terminal through KKT condition iteration;

[0121] Submit the optimal load reduction and unified marginal cost of each terminal to the blockchain network, use the improved PBFT algorithm for distributed consensus verification, and generate blockchain verification results after verification;

[0122] Based on the blockchain verification results, the load reduction ratio of the standard round and the slope sensitivity coefficient of the special round are dynamically corrected.

[0123] In this embodiment, in power system load control, traditional load shedding methods are usually based on fixed rules or centralized optimization, lack the ability to dynamically adjust the load characteristics of each terminal, and it is difficult to ensure the fairness and credibility of load shedding parameters. The present invention realizes dynamic optimization and credible issuance of load shedding parameters through distributed game solving and blockchain consensus verification.

[0124] Specifically, the objective function satisfies ,in, represents the regional unified marginal cost, represents the load reduction of the i-th terminal, , and represents the cost coefficient fitted according to historical data, represents the critical load protection weight, represents the critical load proportion of the i-th terminal, It represents the real-time shelvable load ratio of the i-th terminal.

[0125] The optimal load reduction of each terminal meets ,in, Indicates the optimal load reduction.

[0126] For example, a regional power grid contains three load terminals (terminal A, terminal B, terminal C), and the load characteristics of each terminal are different (such as power factor, load type, etc.). The power grid frequency drops due to sudden failures, and the system stability needs to be restored by load reduction. Terminal A, terminal B, and terminal C are regarded as game participants, and the constraints include: total load reduction constraint: load reduction of terminal A + load reduction of terminal B + load reduction of terminal C = total load reduction required by the power grid; load reduction constraint of each terminal: load reduction of terminal A ≤ maximum load reduction of terminal A; load reduction of terminal B ≤ maximum load reduction of terminal B; load reduction of terminal C ≤ maximum load reduction of terminal C. Based on the non-cooperative game model, the master station solves the optimal load reduction and unified marginal cost of each terminal through KKT condition iteration. Through the iterative solution of KKT conditions, the optimal load reduction and unified marginal cost of each terminal are obtained. For example, after iterative calculation, we get: the optimal load reduction of terminal A = 80kW; the optimal load reduction of terminal B = 120kW; the optimal load reduction of terminal C = 50kW; the unified marginal cost = 150 yuan / kW. The optimal load reduction of each terminal and the unified marginal cost are submitted to the blockchain network. The improved PBFT algorithm is used for distributed consensus verification, and the blockchain verification result is generated after the verification passes. For example, multiple nodes in the blockchain network verify the submitted data, and after confirming the consistency and validity of the data, the blockchain verification result is generated. Based on the blockchain verification results, the load reduction ratio of the standard round and the slope sensitivity coefficient of the special round are dynamically corrected. For example, according to the verification results, the load reduction strategy is adjusted: the load reduction ratio of the standard round is adjusted from 10% to 12%, and the slope sensitivity coefficient of the special round is adjusted from 0.8 to 0.85. The revised load reduction parameters are sent to each terminal. For example, the master station sends the updated load reduction parameters to each terminal: Terminal A: standard round load reduction ratio 12%, special round slope sensitivity coefficient 0.85; Terminal B: standard round load reduction ratio 12%, special round slope sensitivity coefficient 0.85; Terminal C: standard round load reduction ratio 12%, special round slope sensitivity coefficient 0.85.

[0127] In this embodiment, the optimal load reduction amount of each terminal is dynamically solved through the game theory model to minimize the total cost of regional load reduction. The blockchain consensus verification mechanism ensures the fairness and credibility of the load reduction parameters and avoids the decision-making deviation of a single centralized node. The load reduction ratio and slope sensitivity coefficient are dynamically corrected so that the load reduction strategy can adapt to changes in the operating status of the power grid and improve the load reduction effect. The immutability and distributed consensus mechanism of blockchain technology improve the security and reliability of the issuance of load reduction parameters.

[0128] In some embodiments, in the above step S105, after the load shedding is completed, the faulty section is located by residual voltage detection and direction discrimination, and a personalized reconstruction plan is generated by combining topology identification and fault type matching to restore power supply to non-faulty sections within a preset distance, specifically including:

[0129] After the load shedding is completed, the residual voltage of each node of the power grid is detected, and the nodes with residual voltage lower than the preset residual voltage threshold are marked as suspected fault nodes and a preliminary screening section set is constructed;

[0130] For each section in the initial screening section set, calculate the phase angle difference between the first and the last terminals and the current ratio. If the phase angle difference between the first and the last terminals is greater than the preset phase angle difference threshold and the current ratio is greater than the preset current ratio threshold, it is determined to be a forward fault section, and the fault type matching result is obtained;

[0131] Based on the topology database, the Dijkstra algorithm is called to generate the shortest power supply path from the non-fault section to the power source, and the reconstruction strategy is selected based on the fault type matching result to generate a personalized reconstruction plan. At the same time, the power supply of the non-fault section is restored according to the shortest power supply path from the non-fault section to the power source;

[0132] After the reconstruction is completed, a recovery confirmation signal is sent to the master station through GOOSE communication and the topology database is updated.

[0133] In this embodiment, the traditional power grid fault recovery method is usually based on global topology analysis or fixed rules, lacks the ability to accurately locate the fault section and the personalized recovery of the non-fault section, and is difficult to adapt to complex power grid topology changes. The present invention realizes rapid power supply recovery of the non-fault section through dynamic fault location and topology optimization.

[0134] For example, a regional power grid includes 5 nodes (node ​​A, node B, node C, node D, node E) and 4 transmission lines (line 1-2, line 2-3, line 3-4, line 4-5). Due to a short circuit fault in line 2-3, part of the power grid loses power.

[0135] After the load shedding is completed, the residual voltage of each node is detected, and the preset residual voltage threshold is set to 0.6 times the rated voltage. The detection results are as follows: Node A: residual voltage 0.95 times the rated voltage; Node B: residual voltage 0.55 times the rated voltage; Node C: residual voltage 0.40 times the rated voltage; Node D: residual voltage 0.85 times the rated voltage; Node E: residual voltage 0.90 times the rated voltage.

[0136] Nodes B and C whose residual pressure is lower than the preset residual pressure threshold are marked as suspected fault nodes, and a preliminary screening section set is constructed: line 1-2 (including node A and node B), line 2-3 (including node B and node C), and line 3-4 (including node C and node D).

[0137] For each section in the initial screening section set, the phase angle difference between the first and the last terminals and the current ratio are calculated, and the preset phase angle difference threshold is set to 30 degrees, and the preset current ratio threshold is set to 1.2. The calculation results are as follows: Line 1-2: the phase angle difference between the first and the last terminals is 15 degrees, and the current ratio is 1.05; Line 2-3: the phase angle difference between the first and the last terminals is 45 degrees, and the current ratio is 1.5; Line 3-4: the phase angle difference between the first and the last terminals is 10 degrees, and the current ratio is 0.95. If the phase angle difference between the first and the last terminals is greater than the preset phase angle difference threshold and the current ratio is greater than the preset current ratio threshold, it is determined to be a forward fault section, and the determination result is: Line 2-3: It is determined to be a forward fault section; Fault type matching result: short circuit fault. Based on the topology database, the Dijkstra algorithm is called to generate the shortest power supply path from the non-fault section to the power supply.

[0138] Assume that node A is the power node, and the non-fault sections are nodes D and E. The shortest power supply path is: the shortest power supply path of node D: node A → line 1-2 → line 2-3 (skipped) → line 3-4 → node D; the shortest power supply path of node E: node A → line 1-2 → line 2-3 (skipped) → line 3-4 → line 4-5 → node E.

[0139] During actual reconstruction, skip the faulty section line 2-3 and select an alternative path: the alternative power supply path for node D: node A → line 1-2 → node B (through other non-faulty paths or backup lines) → node D; the alternative power supply path for node E: node A → line 1-2 → node B (through other non-faulty paths or backup lines) → node D → line 4-5 → node E; (or directly connect node A and node E through other non-faulty paths).

[0140] Combined with the fault type matching result (short circuit fault), the reconstruction strategy is selected to isolate the faulty section and restore power supply to the non-faulty section, and reconstruction plan 1 is obtained: isolate line 2-3 and restore power supply to node D and node E; reconstruction plan 2: if there is a backup line, enable the backup line to connect node A with node D and node E.

[0141] After the reconstruction is completed, a recovery confirmation signal is sent to the master station through GOOSE communication to confirm that node D and node E have restored power supply. Update the topology database, mark the fault section line 2-3 as a fault state, and record the reconstructed power grid topology. The updated topology database includes: Node A: connection line 1-2; Node B: connection line 1-2 (upstream of the fault section), line 2-3 (fault section, isolated); Node C: connection line 2-3 (fault section, isolated), line 3-4; Node D: connection line 3-4, line 4-5; Node E: connection line 4-5.

[0142] In this embodiment, the fault section is quickly located through residual voltage detection and direction discrimination, reducing the troubleshooting time. Based on topology identification and shortest power supply path generation, the power supply of non-faulty sections is quickly restored to reduce power outage time. Combined with the fault type matching results, the optimal reconstruction strategy is selected to improve the adaptability and reliability of the reconstruction solution. Through GOOSE communication and topology update, the real-time and accuracy of the reconstruction information is ensured, and the safety and stability of the power grid operation are improved.

[0143] Reference Figure 2 An embodiment of the present invention provides a low-frequency load reduction coordinated control system 2 based on GOOSE communication, and the system 2 specifically includes:

[0144] The hardware frequency measurement module 201 is used to calculate the frequency change rate by real-time monitoring of the frequency, voltage and phase angle parameters of the power system, and trigger the frequency protection thread when the frequency change rate exceeds a preset change rate threshold;

[0145] The collaborative control module 202 is used to call the topology analysis service to confirm that the local terminal is in the receiving end power grid, send a status query request to the adjacent terminal, receive the action signal of the adjacent terminal through GOOSE communication, and if the action signal of at least two adjacent terminals is received, delay the load reduction action of the local terminal and start the topology check, otherwise directly enter the load reduction execution stage;

[0146] The load priority decision module 203 is used to generate a dynamic load shedding sequence according to a built-in programmable load characteristic database, and based on the dynamic load shedding sequence, perform load shedding operations through a preset multi-level load shedding logic, and synchronously feed back an action signal to the master station;

[0147] The master station linkage module 204 is used to calculate the marginal cost function of each terminal based on the game theory model at the master station, optimize the load shedding parameters after distributed consensus verification combined with blockchain technology, and issue updates;

[0148] The self-healing reconstruction module 205 is used to locate the faulty section through residual voltage detection and direction discrimination after the load shedding is completed, generate a personalized reconstruction plan by combining topology identification and fault type matching, and restore power supply to the non-faulty section within a preset distance.

[0149] It is understandable that if Figure 1 The contents of the embodiment of the low-frequency load shedding coordinated control method based on GOOSE communication shown in the figure are applicable to the embodiment of the low-frequency load shedding coordinated control system based on GOOSE communication. The functions specifically implemented by the embodiment of the low-frequency load shedding coordinated control system based on GOOSE communication are the same as those in the embodiment of the figure. Figure 1 The embodiment of the low-frequency load reduction coordinated control method based on GOOSE communication shown in FIG. 1 is the same as that of the embodiment of the low-frequency load reduction coordinated control method based on GOOSE communication shown in FIG. 1 , and the beneficial effects achieved are the same as those of the embodiment of the low-frequency load reduction coordinated control method based on GOOSE communication shown in FIG. Figure 1The beneficial effects achieved by the embodiment of the low-frequency load reduction coordinated control method based on GOOSE communication shown are also the same.

[0150] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0151] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0152] Reference Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, the low-frequency load reduction collaborative control method based on GOOSE communication as described in any one of the above methods is implemented.

[0153] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0154] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0155] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, an application program, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0156] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for coordinated control of low-frequency load reduction based on GOOSE communication as described in any one of the above methods is implemented.

[0157] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0158] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0159] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0160] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0161] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A low-frequency load reduction coordinated control method based on GOOSE communication, characterized in that: The method specifically comprises: By real-time monitoring of the frequency, voltage and phase angle parameters of the power system, the frequency change rate is calculated. When the frequency change rate exceeds the preset change rate threshold, the frequency protection thread is triggered; After calling the topology analysis service to confirm that the local terminal is in the receiving-end power grid, it sends a status query request to the adjacent terminal and receives the action signal of the adjacent terminal through GOOSE communication. If the action signal of at least two adjacent terminals is received, the load shedding action of the local terminal is delayed and the topology check is started. Otherwise, it directly enters the load shedding execution stage. Generate dynamic load shedding sequence according to the built-in programmable load characteristic database. Based on the dynamic load shedding sequence, perform load shedding operation through preset multi-level load shedding logic, and synchronously feed back the action signal to the master station; The marginal cost function of each terminal is calculated at the main station based on the game theory model, and after distributed consensus verification using blockchain technology, the load shedding parameters are optimized and updated; After the load shedding is completed, the faulty section is located through residual voltage detection and direction determination, and a personalized reconstruction plan is generated by combining topology identification and fault type matching to restore power supply to the non-faulty section within the preset distance.

2. The method according to claim 1, characterized in that The frequency, voltage and phase angle parameters of the power system are monitored in real time, the frequency change rate is calculated, and when the frequency change rate exceeds a preset change rate threshold, the frequency protection thread is triggered, specifically including: Real-time acquisition of three-phase voltage signal parameters of the power system, wherein the three-phase voltage signal parameters include frequency, voltage and phase angle; Based on the three-phase voltage signal parameters, the instantaneous frequency is calculated by the equal-precision frequency measurement method; Based on the instantaneous frequency, the frequency change rate is obtained by differential calculation within multiple sliding time windows; The change rate threshold is dynamically adjusted according to the system inertia constant and the new energy penetration rate. When it is detected that the frequency change rate is greater than the change rate threshold, the frequency protection thread is immediately triggered.

3. The method according to claim 1, characterized in that After calling the topology analysis service to confirm that the local terminal is in the receiving end power grid, a status query request is sent to the adjacent terminal, and the action signal of the adjacent terminal is received through GOOSE communication. If the action signal of at least two adjacent terminals is received, the load reduction action of the local terminal is delayed and the topology verification is started. Otherwise, the load reduction execution phase is directly entered, which specifically includes: Call the topology analysis service to build a power grid equivalent model based on the node admittance matrix and real-time measurement data; By solving the power grid equivalent model, it is determined whether the local end is a receiving end power grid. If it is a receiving end power grid, a status query request in GOOSE format is sent to the adjacent terminal, and the status query request includes a terminal ID, a timestamp and an action flag; Receive action signals from adjacent terminals through GOOSE communication, count the number of actions, and if the number of actions is greater than or equal to 2, delay the load reduction action at the local end; In the delay phase, the topology deviation rate is calculated based on the real-time measurement data. If the topology deviation rate is less than the preset deviation rate threshold, the load reduction operation is performed, otherwise the action is terminated and reported to the master station.

4. The method according to claim 1, characterized in that The generating of a dynamic load shedding sequence according to a built-in programmable load characteristic database specifically includes: Through the built-in programmable load characteristics database, call the user's important level label, real-time power factor and load type parameters of each load; Based on the user's importance level label, real-time power factor and load type parameters, the score of each load is calculated through the priority scoring formula, and a dynamic load reduction sequence is generated in descending order of the score; When it is detected that the critical load score is less than the preset score threshold, a safety alarm is triggered and automatic load reduction is suspended, waiting for manual confirmation; The LSTM network is used to predict and update the label weight and dynamic coefficient of each load, and then sent to the terminal through the master station.

5. The method according to claim 4, characterized in that The method of performing load shedding operation based on the dynamic load shedding sequence through a preset multi-level load shedding logic and synchronously feeding back the action signal to the master station specifically includes: When the frequency drop triggers the standard load shedding round, the load is removed according to the preset frequency threshold and load shedding ratio; If the frequency change rate exceeds the dynamic threshold, a special load shedding round is activated, the dynamic load shedding amount is calculated by the slope sensitivity coefficient, and the interruptible load is preferentially removed from the end of the dynamic load shedding sequence; The timestamp, terminal ID, cut-off power and average power factor of the load shedding action are encapsulated into a JSON message and synchronously fed back to the master station through the MQTT protocol; After receiving data from multiple terminals, the master station uses the LSTM network to predict the load reduction parameters for the next cycle.

6. The method according to claim 5, characterized in that The master station calculates the marginal cost function of each terminal based on the game theory model, optimizes the load shedding parameters and issues updates after distributed consensus verification in combination with blockchain technology, specifically including: Considering each terminal as a participant, minimizing the total cost of regional load shedding is taken as the objective function, and a non-cooperative game model is established. Based on the non-cooperative game model, the master station solves the optimal load reduction and unified marginal cost of each terminal through KKT condition iteration; Submit the optimal load reduction and unified marginal cost of each terminal to the blockchain network, use the improved PBFT algorithm for distributed consensus verification, and generate blockchain verification results after verification; Based on the blockchain verification results, the load reduction ratio of the standard round and the slope sensitivity coefficient of the special round are dynamically corrected.

7. The method according to claim 1, characterized in that After the load shedding is completed, the faulty section is located by residual voltage detection and direction discrimination, and a personalized reconstruction plan is generated by combining topology identification and fault type matching to restore power supply to the non-faulty section within a preset distance, specifically including: After the load shedding is completed, the residual voltage of each node of the power grid is detected, and the nodes with residual voltage lower than the preset residual voltage threshold are marked as suspected fault nodes and a preliminary screening section set is constructed; For each section in the initial screening section set, calculate the phase angle difference between the first and the last terminals and the current ratio. If the phase angle difference between the first and the last terminals is greater than the preset phase angle difference threshold and the current ratio is greater than the preset current ratio threshold, it is determined to be a forward fault section, and the fault type matching result is obtained; Based on the topology database, the Dijkstra algorithm is called to generate the shortest power supply path from the non-fault section to the power source, and the reconstruction strategy is selected based on the fault type matching result to generate a personalized reconstruction plan. At the same time, the power supply of the non-fault section is restored according to the shortest power supply path from the non-fault section to the power source; After the reconstruction is completed, a recovery confirmation signal is sent to the master station through GOOSE communication and the topology database is updated.

8. A low-frequency load reduction coordinated control system based on GOOSE communication, characterized in that: The system specifically comprises: The hardware frequency measurement module is used to calculate the frequency change rate by real-time monitoring of the frequency, voltage and phase angle parameters of the power system. When the frequency change rate exceeds the preset change rate threshold, the frequency protection thread is triggered; The collaborative control module is used to call the topology analysis service to confirm that the local terminal is in the receiving end power grid, send a status query request to the adjacent terminal, receive the action signal of the adjacent terminal through GOOSE communication, and if the action signal of at least two adjacent terminals is received, delay the load reduction action of the local terminal and start the topology verification, otherwise directly enter the load reduction execution stage; Load priority decision module, used to generate dynamic load shedding sequence according to the built-in programmable load characteristic database, based on the dynamic load shedding sequence, perform load shedding operation through preset multi-level load shedding logic, and synchronously feed back the action signal to the master station; The master station linkage module is used to calculate the marginal cost function of each terminal based on the game theory model at the master station, optimize the load shedding parameters and issue updates after distributed consensus verification combined with blockchain technology; The self-healing reconstruction module is used to locate the faulty section through residual voltage detection and direction determination after load shedding is completed, and to generate a personalized reconstruction plan by combining topology identification and fault type matching to restore power supply to non-faulty sections within a preset distance.

9. A computer device, characterized in that: include: A memory and a processor and a computer program stored in the memory, when the computer program is executed on the processor, implements the low-frequency load reduction coordinated control method based on GOOSE communication as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by the processor, the low-frequency load reduction coordinated control method based on GOOSE communication as described in any one of claims 1 to 7 is implemented.

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