Power system fault self-healing method, device and system based on internet of things

Abstract

This application relates to the field of power system protection technology, specifically to a power system fault self-healing method, device, and system based on the Internet of Things (IoT). The method includes: calculating the difference between the subgrid net load and the active power of the energy storage station to quantify the degree of supply-demand matching; calculating the correlation coefficient between subgrid voltage fluctuations and active power fluctuations of power resources to determine the degree of voltage coordination; calculating the cross-correlation between subgrid voltage deviations and reactive power of power resources to determine the reactive power regulation coefficient; and weighted fusion to obtain a closeness score; constructing an association weight by combining the closeness score and the electrical distance between the subgrid and the power resources; and solving the scheduling priority based on the association weight to control the energy storage station to supply power to the subgrid to achieve fault self-healing. This application solves the problem of poor recovery effect caused by the lack of global coordination in local self-healing decisions, improving the efficiency of power system fault recovery and the stability of power system operation.

Description

Technical Field

[0001] This application relates to the field of power system protection technology, specifically to a method, device, and system for power system fault self-healing based on the Internet of Things. Background Technology

[0002] As the energy transition deepens, the Internet of Things (IoT) technology provides comprehensive sensing and intelligent control support for the power system, significantly enhancing the self-healing capabilities of the distribution network. Through dense deployment at key nodes, the system achieves millisecond-level monitoring and response, making fault self-healing technology a core means to ensure power supply reliability and build a resilient power grid.

[0003] However, in practical applications, to improve the speed of fault self-healing response, current mainstream solutions tend to decentralize self-healing decisions to the edge, relying on edge computing to achieve rapid fault isolation and power restoration within local areas or subsystems. But with the large-scale integration of distributed energy resources, energy storage systems, and flexible loads, the distribution network has transformed from a traditional unidirectional radial structure into a complex network with multiple sources, bidirectional connections, and high uncertainty. In this environment, a single edge node, lacking global information, struggles to achieve effective coordination, leading to conflicts between local decisions and global objectives. This results in suboptimal recovery solutions, reducing the efficiency of power system fault self-healing and the stability of power system operation. Summary of the Invention

[0004] In a first aspect, embodiments of this application provide a power system fault self-healing method based on the Internet of Things, the method comprising the following steps:

[0005] Real-time acquisition of net load of subnets in the power system, voltage of each node, active power and reactive power of each power resource;

[0006] The differences between the active power of each power resource and the net load of the subgrid are compared to quantify the supply-demand matching degree between each power resource and the subgrid; the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of nodes within the subgrid is analyzed to determine the voltage coordination degree between each power resource and the subgrid; the cross-correlation between the deviation of the node voltage within the subgrid from the rated voltage and the reactive power of each power resource is compared to calculate the reactive power regulation coefficient between each power resource and the subgrid, and the supply-demand matching degree and the voltage coordination degree are integrated to determine the closeness score between each power resource and the subgrid.

[0007] By combining the electrical distance and the degree of closeness between each power resource and the subgrid, a correlation weight between each power resource and the subgrid is constructed. Based on the correlation weight, a particle swarm optimization algorithm is used to construct an objective function to solve the power supply priority of each power resource to the subgrid, so as to control the power resources to deliver power to the subgrid and thus achieve fault self-healing.

[0008] Preferably, the quantification process for the supply and demand matching degree between each power resource and the subgrid is as follows:

[0009] Obtain the rated power of each power resource and the historical maximum load of the subgrid, and select the maximum value from the rated power and the historical maximum load. Normalize the result of the difference between the active power of each power resource and the net load of the subgrid as the maximum value mentioned above, and record it as the supply and demand difference degree between each power resource and the subgrid.

[0010] The degree of supply and demand matching between various power resources and subgrids is negatively correlated with the degree of supply and demand disparity.

[0011] Preferably, the method for determining the degree of voltage coordination between each power resource and the subgrid is as follows:

[0012] Based on the degree of voltage fluctuation of nodes within the subnet, representative nodes are selected from all nodes within the subnet;

[0013] The correlation coefficient between the fluctuation of active power of each power resource and the voltage fluctuation of representative nodes in the subgrid is used as the degree of voltage coordination between each power resource and the subgrid.

[0014] Preferably, the representative node is the node with the greatest voltage fluctuation within the subnet.

[0015] Preferably, the calculation process for the reactive power regulation coefficient between each power resource and the subgrid is as follows:

[0016] Calculate the deviation between the real-time voltage and the rated voltage of the representative node in the subnet, and denot it as the real-time voltage deviation;

[0017] The cross-correlation coefficient between the voltage deviation of the representative node at a historical moment in the subnetwork and the reactive power of each power resource at a historical moment is denoted as the reactive power regulation coefficient between each power resource and the subnetwork.

[0018] Preferably, the score for the closeness between each power resource and the subgrid is positively correlated with the degree of supply and demand matching, the degree of voltage coordination, and the reactive power regulation coefficient.

[0019] Preferably, the association weights between each power resource and the subgrid are negatively correlated with the electrical distance between each power resource and the subgrid, and positively correlated with the degree of closeness score.

[0020] Preferably, the expression for the objective function is: In the formula, This represents the objective function with electrical energy q as the independent variable; This represents the electrical energy supplied by power resource h to the u-th subgrid; This represents the association weight between power resource j and the i-th subnet; This indicates the quantity of all electrical resources in the power system; This indicates the number of all subnets in the power system.

[0021] Secondly, embodiments of this application provide an Internet of Things-based power system fault self-healing device, wherein the device stores a computer program, and when the computer program is executed by a processor, it implements the Internet of Things-based power system fault self-healing method described above.

[0022] Thirdly, embodiments of this application also provide an Internet of Things-based power system fault self-healing system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any of the above-described Internet of Things-based power system fault self-healing methods.

[0023] As can be seen from the above embodiments, the power system fault self-healing method based on the Internet of Things provided in this application has at least the following beneficial effects:

[0024] This application constructs a tightness score by quantifying the correlation characteristics between power resources and subgrids in three dimensions: supply and demand matching, voltage coordination, and reactive power regulation. This effectively resolves the contradiction between local decision-making in edge computing and global optimization objectives. The strategy utilizes offline analysis to accurately capture the inherent coupling relationship between resources and subgrids, providing a scientific scheduling basis for online fault self-healing. Thus, while ensuring response speed, it achieves efficient coordination and accurate matching of global resources, which helps improve the efficiency of power system fault recovery and the stability of power system operation.

[0025] Furthermore, this application introduces electrical distance to spatially correct the density score, constructs a correlation weight characterizing the actual dispatch value of power resources, and uses this as the core to construct a particle swarm optimization model to solve the optimal power supply scheme, effectively overcoming the limitation of simply relying on operational coupling and ignoring transmission losses. Under the premise of ensuring that load demand and resource constraints are met, this method realizes secondary optimization allocation of power in the fault area, significantly improves the economy and response speed of dispatch decisions, ensures the efficiency and stability of the fault self-healing process, and ultimately improves the efficiency of power system fault self-healing and the stability of power system operation. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating the steps of an IoT-based power system fault self-healing method provided in one embodiment of this application;

[0028] Figure 2 This is a schematic diagram of the association weight extraction process provided in one embodiment of this application. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the Internet of Things-based power system fault self-healing method, apparatus, and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0031] The following description, in conjunction with the accompanying drawings, details the specific solutions for the Internet of Things-based power system fault self-healing method, device, and system provided in this application.

[0032] Please see Figure 1 The diagram illustrates a flowchart of a power system fault self-healing method based on the Internet of Things (IoT) according to an embodiment of this application. The method includes the following steps:

[0033] S1: Real-time acquisition of the net load of the subnet in the power system, the voltage of each node, and the active and reactive power of each power resource.

[0034] In each subgrid of the power system, the total active power of the subgrid is collected in real time through smart meters at each grid connection point. At each node in each subgrid, the voltage of each node is collected in real time through voltage transformers. In this embodiment, the nodes include at least the feeder head (substation side), feeder middle section (voltage drop zone), feeder end, distributed energy resources (DER), and energy storage system access point. In practical applications, as other implementation methods, implementers can also set the nodes participating in the analysis according to specific circumstances; this embodiment does not impose special restrictions. At each power resource grid connection point, the voltage of each node is collected in real time through an energy storage converter monitoring module. The active and reactive power of the power resources are included in this embodiment. Each power resource includes distributed energy resources (DER) and energy storage stations. The sampling rate of all the above data is f, where f is set manually. In this embodiment, f is set to 100Hz. In actual applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions. Finally, based on the total active power of the subnet and the active power of the power resources, the net load of the subnet is determined. In this embodiment, the net load of the subnet is the difference between the total active power of the subnet and the total active power of all power resources.

[0035] Thus, the data acquisition of the power system is completed, and the various types of data collected are normalized respectively. In this embodiment, the normalization method adopts the maximum-minimum value normalization method. In actual application, as other implementation methods, implementers may also adopt other normalization methods to normalize the data according to specific circumstances. This embodiment does not impose any special restrictions.

[0036] The process of normalizing data using the maximum-minimum normalization method is a well-known technique and will not be elaborated further.

[0037] S2: Compare the differences between the active power of each power resource and the net load of the subgrid to quantify the supply-demand matching degree between each power resource and the subgrid; analyze the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of the nodes within the subgrid to determine the voltage coordination degree between each power resource and the subgrid; compare the cross-correlation between the deviation of the node voltage within the subgrid from the rated voltage and the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the subgrid, and integrate the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the subgrid.

[0038] Fault self-healing technology in power systems is of great significance for improving the reliability and security of power grids. With the development of IoT technology, the ability to comprehensively perceive and intelligently control power system fault self-healing has been significantly enhanced. However, this has also brought about new problems. In order to improve the response speed of power systems, existing solutions generally push self-healing decision-making and scheduling down to the edge side through edge computing. However, this method of decision optimization by the edge side only considers local or local decision-making situations, which can easily lead to inconsistencies between local optimization objectives and global system objectives. This is detrimental to the fault self-healing scheduling of the entire power system and may even lead to decision failure or affect the operational stability of the power system. Therefore, a power system fault self-healing method that can take into account both local autonomy and global coordination is needed.

[0039] Therefore, to address the aforementioned issues, this embodiment quantifies the supply-demand matching degree between each power resource and the subgrid by comparing the difference between the active power of each power resource and the net load of the subgrid; it analyzes the correlation between the fluctuation of the active power of each power resource and the voltage fluctuation of nodes within the subgrid to determine the voltage coordination degree between each power resource and the subgrid; it compares the cross-correlation between the deviation of the node voltage within the subgrid from the rated voltage and the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the subgrid, and integrates the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the subgrid. This allows for the rapid and accurate identification of the most suitable power resource during power system faults, delivering electricity to where it is most needed, thereby achieving high-quality fault self-healing. The specific process is as follows:

[0040] Power system fault self-healing has high timeliness requirements, aiming to minimize the impact of faults on the safety and stability of the power system. Existing technologies often rely on edge computing to achieve rapid local optimization, but decisions limited to local information cannot guarantee global quality; while introducing global optimization at the moment of fault occurrence faces the dilemma of excessive computation time and slow response speed. To resolve this contradiction, this embodiment adopts a strategy of "offline global analysis and online real-time invocation," placing global optimization calculations offline, clarifying the power allocation logic of various power resources in the global scope in advance, and directly invoking offline analysis results during the fault self-healing process, achieving efficient collaborative support of global resources while ensuring response speed.

[0041] Specifically, the load change trends of each subgrid in a power system and the output fluctuation characteristics of power resources can objectively reflect the degree of correlation between them. This is manifested in three dimensions: First, in terms of load matching, the closer a power resource is to the subgrid, the higher the overlap between its output change curve and the subgrid load change curve; second, in terms of output characteristics, given the strong randomness of power resources, their output fluctuations often cause synchronous fluctuations in subgrid voltage, and the consistency of their fluctuations forms the basis of the correlation; finally, in terms of voltage support, when the subgrid voltage fluctuates, closely related resources can more sensitively perform reactive power regulation, and their reactive power injection changes are significantly more strongly correlated with voltage fluctuations.

[0042] Therefore, based on the above analysis, this embodiment analyzes the relationship between power resource j and subnet i at the current moment. First, by comparing the difference between the active power of power resource j and the net load of subnet i, the supply-demand matching degree between power resource j and subnet i is quantified. Specifically:

[0043] In this embodiment, the rated power of power resource j at the current moment and the historical maximum load of subnet i before the current moment are obtained. In this embodiment, the historical time period is set to a preset time period before the current moment. The value of the preset time period is 10 minutes in this embodiment. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions. Further, the maximum value is selected from the rated power and the historical maximum load. The result of the ratio of the absolute difference between the active power of power resource j at each moment before the current moment and the net load of subnet i at the current moment to the above maximum value is normalized and recorded as the supply and demand difference degree between power resource j and subnet i at each moment.

[0044] At the current moment, the degree of supply and demand matching between power resource j and subgrid i is negatively correlated with the degree of supply and demand difference.

[0045] It should be noted that there are many commonly used normalization methods. In this embodiment, the maximum-minimum normalization method is used to normalize the result of the difference between the active power of power resource j and the net load of subnet i at each time point and the maximum value mentioned above, and the result is mapped to the range of [0,1]. In actual application, as other implementation methods, implementers may also use other normalization methods according to specific circumstances. This embodiment does not impose any special restrictions.

[0046] It should be noted that, unless otherwise specified, all normalization processes in this embodiment employ the maximum-minimum value normalization method.

[0047] It should be understood that a negative correlation means that the dependent variable decreases as the independent variable increases, and the dependent variable increases as the independent variable decreases. It can be a subtraction relationship or a division relationship, depending on the actual application.

[0048] Preferably, as an implementation method, in this embodiment, the method for determining the supply and demand matching degree between power resource j and subnet i is as follows: within a preset time period before the current time, calculate the difference between 1 and the supply and demand difference at each time, and record it as the supply and demand matching value. The average of the supply and demand matching values ​​at all times is taken as the supply and demand matching degree between power resource j and subnet i at the current time.

[0049] Based on the supply-demand matching degree, it can be understood that the supply-demand matching degree is used to characterize the supply-demand balance characteristics between power resource output and subgrid net load, reflecting the capacity adaptability of power resources as a power supply source. Its calculation is mainly affected by the degree of difference between the active power of power resources and the subgrid net load. The greater the difference, the lower the supply-demand matching degree, reflecting that the resource output cannot effectively cover the load gap, resulting in a decrease in the power supply reliability in fault self-healing dispatch. Conversely, the smaller the difference, the higher the supply-demand matching degree, reflecting that the resource output and load demand are highly matched, and the most accurate power supply support can be achieved with the minimum capacity margin, thereby significantly improving the efficiency and success rate of fault recovery.

[0050] Secondly, this embodiment analyzes the correlation between the fluctuation of active power of power resource j and the voltage fluctuation of nodes within subgrid i to determine the degree of voltage coordination between power resource j and subgrid i. Specifically:

[0051] In this embodiment, firstly, based on the degree of voltage fluctuation of nodes in subnet i, representative nodes are selected from all nodes in subnet i. Specifically, in this embodiment, the representative node is the node with the largest voltage fluctuation in the subnet.

[0052] Furthermore, the correlation coefficient between the fluctuation degree of active power of power resource j and the voltage fluctuation degree of representative nodes in subgrid i is used as the voltage coordination degree between power resource j and subgrid i.

[0053] It should be noted that the specific process for measuring the degree of voltage fluctuation and the degree of active power fluctuation within the subnet in this embodiment is as follows:

[0054] For node a within subnet i, the voltage at node a is composed of voltages at all times within a preset time period prior to the current time, forming a voltage sequence. The moving standard deviation of this voltage sequence is calculated and used as the quantification result of the voltage fluctuation at node a within subnet i within the preset time period prior to the current time. Similarly, for power resource j, the active power of the power resource is composed of active power at all times within a preset time period prior to the current time, forming a power sequence. The moving standard deviation of this power sequence is calculated and used as the quantification result of the active power fluctuation at power resource j at the current time. For each node within subnet i, the mean of all elements in the moving standard deviation of the voltage sequence of each node is calculated, and the node corresponding to the maximum mean is taken as the representative node. The moving standard deviation sequence is obtained using the moving standard deviation algorithm. In this embodiment, the sliding step size in the moving standard deviation sequence algorithm is 1 second, and the sliding window size is 100 seconds. In actual applications, implementers can set these values ​​according to specific circumstances. This embodiment does not impose any special restrictions. The process of obtaining the moving standard deviation sequence using the moving standard deviation algorithm is a well-known technique and will not be described in detail here.

[0055] Furthermore, it should be noted that in this embodiment, the Pearson correlation coefficient between the fluctuation of the active power of power resource j and the voltage fluctuation of the representative node in subnet i is used. That is, the Pearson correlation coefficient between the moving standard deviation of the active power sequence of power resource j and the moving standard deviation of the voltage sequence of the representative node in subnet i is used as the correlation coefficient between the fluctuation of the active power of power resource j and the voltage fluctuation of the representative node in subnet i. In practical applications, as other implementation methods, implementers may also use other correlation coefficient calculation methods such as Spearman correlation coefficient according to specific circumstances. This embodiment does not impose any special restrictions.

[0056] The calculation process of the Pearson correlation coefficient is a well-known technique and will not be elaborated further.

[0057] Specifically, if the Pearson correlation coefficient is negative, or if all elements in either the moving standard deviation of the voltage series or the moving standard deviation of the active power series are all 0, then the voltage coherence level is set to 0.

[0058] Based on the voltage coordination degree, it can be understood that the voltage coordination degree is used to characterize the synchronicity of the fluctuation characteristics of the subgrid node voltage and the active power of the power resources, reflecting the inherent coupling strength between the power resources and the subgrid in terms of electrical characteristics. Its calculation is mainly affected by the correlation coefficient between the fluctuation degree of the active power of the power resources and the fluctuation degree of the voltage of the representative node of the subgrid. The larger the correlation coefficient, the higher the voltage coordination degree, reflecting that the power resources and the subgrid are closely connected, the output changes of the power resources can be directly perceived by the power system, and the stronger the effectiveness of its participation in regulation. Conversely, the smaller the correlation coefficient, the lower the voltage coordination degree, reflecting that the power resources and the subgrid are loosely coupled, and its supporting role in the subgrid voltage is weak, making it difficult to play a key stabilization control role in the self-healing process.

[0059] Furthermore, this embodiment calculates the reactive power regulation coefficient between power resource j and subgrid i by comparing the deviation of node voltage within subgrid i from its rated voltage with the reactive power of power resource j. Specifically:

[0060] Calculate the deviation between the real-time voltage of the representative node in subnet i and the rated voltage, and denot it as the real-time voltage deviation of subnet i.

[0061] The cross-correlation coefficient between the voltage deviation of the representative node in subnet i at a historical moment and the reactive power of power resource j at a historical moment is denoted as the reactive power regulation coefficient between power resource j and subnet i. That is, the normalized value of the cross-correlation coefficient between the voltage deviation of the representative node in subnet i and the reactive power of power resource j at all moments in the preset time period before the current moment is denoted as the reactive power regulation coefficient between power resource j and subnet i at the current moment.

[0062] The calculation process of the cross-correlation coefficient is a well-known technique and will not be elaborated further.

[0063] Specifically, if the voltage deviation or reactive power is 0 for all preset time periods during the cross-correlation analysis, the reactive power adjustment coefficient is set to 0.5.

[0064] Based on the reactive power regulation coefficient, it can be understood that the reactive power regulation coefficient is used to characterize the ability of power resources to compensate for subgrid voltage deviations due to changes in reactive power, reflecting the functional contribution of power resources to maintaining local voltage stability. Its calculation is mainly affected by the cross-correlation between subgrid voltage deviation and reactive power of power resources. The larger the cross-correlation coefficient, the higher the reactive power regulation coefficient, reflecting that power resources can keenly capture voltage fluctuations and output reactive power support in a timely manner, which helps to quickly smooth out voltage overshoots during fault self-healing. Conversely, the smaller the cross-correlation coefficient, the lower the reactive power regulation coefficient, reflecting that power resources are slow to respond to voltage fluctuations or lack reactive power regulation capabilities, making it difficult to ensure voltage safety during the self-healing process.

[0065] Finally, based on the supply-demand matching degree, the voltage coordination degree, and the reactive power regulation coefficient, this embodiment determines the closeness score between power resource j and subgrid i, specifically as follows:

[0066] In this embodiment, the correlation weight between power resource j and subnet i is positively correlated with the degree of supply and demand matching, the degree of voltage coordination, and the reactive power regulation coefficient.

[0067] It should be understood that a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. The specific relationship can be additive or multiplicative, etc., and is determined by the actual application. This application does not impose any special restrictions.

[0068] Preferably, as one implementation method, in this embodiment, the degree of closeness between power resource j and subnet i at the current moment is scored. The expression is: In the formula, , , These represent the degree of supply-demand matching, voltage coordination, and reactive power regulation coefficient between power resource j and subgrid i at the current moment, respectively. , , These represent the preset first, second, and third weighting coefficients, respectively. .

[0069] In this embodiment, , , The values ​​are 0.4, 0.3, and 0.3 respectively. In practical applications, implementers can set these values ​​according to specific circumstances. This embodiment does not impose any special restrictions.

[0070] The tightness score, as understood from the data, characterizes the global coordination potential of resources across three dimensions: supply and demand, voltage, and reactive power. It reflects the overall applicability and priority of power resources as backup power sources. Its calculation is positively and comprehensively influenced by the degree of supply-demand matching, voltage coordination, and reactive power regulation coefficient. A higher degree of supply-demand matching, voltage coordination, and reactive power regulation coefficient between the current power resources and the subgrid results in a higher tightness score, indicating that the current power resources are highly compatible with the subgrid in terms of power supply, characteristic coupling, and stability control, making them the optimal backup resource for fault self-healing. Conversely, a lower degree of supply-demand matching, voltage coordination, and reactive power regulation coefficient between the current power resources and the subgrid results in a lower tightness score, indicating a lack of necessary operational interaction and support capabilities between the current power resources and the subgrid, leading to low returns and high risks when dispatching the current power resources.

[0071] Thus, this embodiment quantifies the correlation characteristics between power resources and subgrids in three dimensions: supply and demand matching, voltage coordination, and reactive power regulation, and constructs a closeness score, effectively resolving the contradiction between local decision-making in edge computing and global optimization objectives. This strategy utilizes offline analysis to accurately capture the inherent coupling relationship between resources and subgrids, providing a scientific scheduling basis for online fault self-healing. Therefore, while ensuring response speed, it achieves efficient coordination and accurate matching of global resources, which helps improve the efficiency of power system fault recovery and the stability of power system operation.

[0072] S3: Combining the electrical distance between each power resource and the subgrid and the density score, construct the association weight between each power resource and the subgrid; based on the association weight, use the particle swarm optimization algorithm to construct the objective function, solve the power supply priority of each power resource to the subgrid, so as to control the power resources to deliver power to the subgrid and thus achieve fault self-healing.

[0073] The aforementioned density score, based on historical operational data, measures the correlation between any subnet and resources from a global perspective. However, in actual scheduling and allocation, power transmission is accompanied by power loss, and the extension of transmission distance reduces controllability and stability. Therefore, the spatial distribution characteristics between subnets and power resources are also a key factor determining scheduling efficiency. In view of this, this embodiment introduces spatial distribution characteristics for weighted correction based on the density score. Specifically, by combining the electrical distance between each power resource and the subnet with the density score, a correlation weight between each power resource and the subnet is constructed. Based on the correlation weight, a particle swarm optimization algorithm is used to construct an objective function to solve for the power supply priority of each power resource to the subnet, thereby controlling the power resources' delivery of power to the subnet and achieving fault self-healing. The specific process is as follows:

[0074] First, regarding power resource j and subnet i, this embodiment constructs the association weight between power resource j and subnet i at the current moment by comprehensively considering the electrical distance between power resource j and subnet i and the aforementioned density score. Specifically:

[0075] In this embodiment, the association weight between power resource j and subnet i is negatively correlated with the electrical distance between power resource j and subnet i, and positively correlated with the density score.

[0076] The calculation process for electrical distance is a well-known technique and will not be elaborated further.

[0077] Preferably, as one implementation method, in this embodiment, the association weight between power resource j and subnet i at the current time is... The expression is: In the formula, This represents the electrical distance between power resource j and subnet i, where the electrical distance value is a dimensionless value without units. represents the score of the closeness between power resource j and subnet i at the current moment; exp() represents the exponential function with the natural constant as the base; norm[] represents the normalization function.

[0078] Preferably, the schematic diagram of the association weight extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0079] Based on the correlation weight, it can be understood that the correlation weight is used to characterize the final priority of power resources after comprehensively considering operational coupling and physical constraints, reflecting the actual competitiveness of power resources in fault recovery schemes. Its calculation is jointly affected by the tightness score and electrical distance. If the electrical distance between the current power resource and the subgrid is smaller and the tightness score is larger, the correlation weight is larger, reflecting that the current power resource not only has a high degree of operational coupling but also low transmission loss and strong controllability, and should be prioritized for scheduling to achieve efficient self-healing. Conversely, if the electrical distance between the current power resource and the subgrid is larger and the tightness score is smaller, the corresponding correlation weight is smaller, reflecting that the current power resource has poor dispatch economy and weak control effect, and should be downgraded or excluded in optimized scheduling.

[0080] Based on the above analysis, by traversing all power resources and all subnets, the correlation weight between each power resource and any subnet is obtained. Furthermore, this embodiment uses the particle swarm optimization algorithm to construct an objective function based on the correlation weight, and solves the power supply priority of each power resource to the subnet, so as to control the power resources to deliver power to the subnet and thus achieve fault self-healing. Specifically:

[0081] In this embodiment, the expression for the objective function is: In the formula, This represents the objective function with electrical energy q as the independent variable; This represents the electrical energy supplied by power resource h to the u-th subgrid; This represents the association weight between power resource j and the i-th subnet; This indicates the quantity of all electrical resources in the power system; This indicates the number of all subnets in the power system.

[0082] The objective function is constrained by the following conditions: the total amount of electricity supplied by any power resource to all subgrids is less than or equal to its own rated power supply (which can be obtained through the power system); and the total amount of electricity obtained by any subgrid is less than or equal to its own net load.

[0083] Finally, the output shows the electrical energy that each power resource should supply to each subgrid, along with the priority order of supply.

[0084] As can be understood from the objective function, it reflects the overall merits of the power allocation strategy in meeting load demand and utilizing resource correlation. Its calculation is affected by the combined influence of each power resource on the power supply of each subgrid and the corresponding correlation weight. The higher the correlation weight and the more reasonable the power supply allocation, the larger the objective function value, reflecting that the scheduling scheme makes full use of high-priority resources and achieves efficient, stable and low-cost power allocation. Conversely, the lower the correlation weight or the more improper the power supply allocation, the smaller the objective function value, reflecting that there is a resource mismatch in the scheduling scheme, which may lead to poor recovery effect or decreased system stability.

[0085] Furthermore, after obtaining the fault location in the power system, the circuit breaker is controlled to disconnect and isolate the relevant location. Then, based on the various sub-network areas affected by the power outage, the corresponding objective function and constraints are constructed according to the particle swarm optimization algorithm process to perform secondary scheduling of power resources in the power system, thereby enabling the affected sub-network areas to restore power supply as soon as possible and reduce the impact of the fault.

[0086] Among them, the particle swarm optimization algorithm is a well-known technique, and the process of obtaining the optimal solution using it is also a well-known technique, which will not be elaborated here.

[0087] Thus, this embodiment introduces electrical distance to spatially correct the density score, constructs a correlation weight characterizing the actual scheduling value of power resources, and uses this as the core to build a particle swarm optimization model to solve the optimal power supply scheme, effectively overcoming the limitation of simply relying on operational coupling and ignoring transmission losses. Under the premise of ensuring that load demand and resource constraints are met, this method realizes secondary optimization allocation of power in the fault area, significantly improves the economy and response speed of scheduling decisions, ensures the efficiency and stability of the fault self-healing process, and ultimately improves the efficiency of power system fault self-healing and the stability of power system operation.

[0088] Based on the same inventive concept as the above method, this application also provides an Internet of Things-based power system fault self-healing device, wherein the device stores a computer program, and when the computer program is executed by a processor, it implements the Internet of Things-based power system fault self-healing method described above.

[0089] Based on the same inventive concept as the above methods, this application also provides an Internet of Things-based power system fault self-healing system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described Internet of Things-based power system fault self-healing methods.

[0090] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0091] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0092] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A power system fault self-healing method based on the Internet of Things, characterized in that, The method includes the following steps: Real-time acquisition of net load of subnets in the power system, voltage of each node, active power and reactive power of each power resource; The differences between the active power of each power resource and the net load of the subgrid are compared to quantify the supply-demand matching degree between each power resource and the subgrid; the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of nodes within the subgrid is analyzed to determine the voltage coordination degree between each power resource and the subgrid; the cross-correlation between the deviation of the node voltage within the subgrid from the rated voltage and the reactive power of each power resource is compared to calculate the reactive power regulation coefficient between each power resource and the subgrid, and the supply-demand matching degree and the voltage coordination degree are integrated to determine the closeness score between each power resource and the subgrid. By combining the electrical distance and the degree of closeness between each power resource and the subgrid, a correlation weight between each power resource and the subgrid is constructed. Based on the correlation weight, a particle swarm optimization algorithm is used to construct an objective function to solve the power supply priority of each power resource to the subgrid, so as to control the power resources to deliver power to the subgrid and thus achieve fault self-healing. The expression for the objective function is: In the formula, This represents the objective function with electrical energy q as the independent variable; This represents the electrical energy supplied by power resource h to the u-th subgrid; This represents the association weight between power resource j and the i-th subnet; This indicates the quantity of all electrical resources in the power system; This indicates the number of all subnets in the power system; The constraints of the objective function are: the total amount of electrical energy supplied by any power resource to all sub-networks is less than or equal to its own rated power supply; the total amount of electrical energy obtained by any sub-network is less than or equal to its own net load. Using the particle swarm optimization algorithm, the power energy that each power resource should supply to each subgrid and the priority order of supply are output. 2.The IoT-based power system fault self-healing method of claim 1, wherein, The quantification process of the supply and demand matching degree between each power resource and the subgrid is as follows: Obtain the rated power of each power resource and the historical maximum load of the subgrid, and select the maximum value from the rated power and the historical maximum load. Normalize the result of the difference between the active power of each power resource and the net load of the subgrid as the maximum value mentioned above, and record it as the supply and demand difference degree between each power resource and the subgrid. The degree of supply and demand matching between various power resources and subgrids is negatively correlated with the degree of supply and demand disparity.

3. The power system fault self-healing method based on the Internet of Things as described in claim 1, characterized in that, The method for determining the degree of voltage coordination between each power resource and the subgrid is as follows: Based on the degree of voltage fluctuation of nodes within the subnet, representative nodes are selected from all nodes within the subnet; The correlation coefficient between the fluctuation of active power of each power resource and the voltage fluctuation of representative nodes in the subgrid is used as the degree of voltage coordination between each power resource and the subgrid.

4. The IoT-based power system fault self-healing method of claim 3, wherein, The representative node is the node with the greatest voltage fluctuation within the subnet. 5.The IoT-based power system fault self-healing method of claim 1, wherein, The calculation process for the reactive power regulation coefficient between each power resource and the subgrid is as follows: Calculate the deviation between the real-time voltage and the rated voltage of the representative node in the subnet, and denot it as the real-time voltage deviation; The cross-correlation coefficient between the voltage deviation of the representative node at a historical moment in the subnetwork and the reactive power of each power resource at a historical moment is denoted as the reactive power regulation coefficient between each power resource and the subnetwork. 6.The IoT-based power system fault self-healing method of claim 1, wherein, The scores for the degree of closeness between each power resource and the subgrid are positively correlated with the degree of supply and demand matching, the degree of voltage coordination, and the reactive power regulation coefficient.

7. The IoT-based power system fault self-healing method of claim 1, wherein, The association weights between each power resource and the subgrid are negatively correlated with the electrical distance between each power resource and the subgrid, and positively correlated with the degree of closeness score.

8. A power system fault self-healing device based on the Internet of Things, wherein the device stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the Internet of Things-based power system fault self-healing method as described in any one of claims 1-7.

9. A power system fault self-healing system based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the Internet of Things-based power system fault self-healing method as described in any one of claims 1-7.

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