Power system fault self-recovery method, device and system based on Internet of Things

Abstract

The invention relates to the technical field of power system protection, in particular to a power system fault self-recovery method, device and system based on the Internet of Things. The method comprises the steps that the difference between the net load of a subnet and the active power of an energy storage station is calculated to quantify the supply and demand matching degree; calculating a correlation coefficient of subgrid voltage fluctuation and power resource active power fluctuation so as to determine a voltage cooperation degree, calculating a cross correlation between subgrid voltage deviation and power resource reactive power so as to determine a reactive power adjustment coefficient, and carrying out weighted fusion to obtain a closeness degree score; constructing an association weight by integrating the closeness degree score and the electrical distance between the subnet and the power resource; and solving the scheduling priority based on the association weight, and controlling the energy storage station to supply power to the subnet so as to realize fault self-healing. According to the method, the problem of poor recovery effect caused by the lack of global coordination of the local self-healing decision is solved, and the power system fault recovery efficiency and the power system operation stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system protection, in particular to a power system fault self-healing method, device and system based on Internet of Things. BACKGROUND

[0002] With the deepening of energy transformation, the Internet of Things technology provides comprehensive sensing and intelligent control support for the power system, significantly improving the self-healing ability of the distribution network. Through the intensive deployment of key nodes, the system realizes millisecond-level monitoring response, making the fault self-healing technology a core means to ensure power supply reliability and build a resilient power grid.

[0003] However, in actual application, in order to improve the fault self-healing response speed, the current mainstream scheme tends to sink the self-healing decision to the edge side, relying on edge computing to realize rapid fault isolation and power restoration in the local area or subsystem. However, with the large-scale access of distributed energy, energy storage systems and flexible loads, the distribution network has changed from the traditional single-directional radial structure to a complex network with multiple sources, bidirectional flow and high uncertainty. In this environment, a single edge node lacks global information and is difficult to achieve effective coordination, leading to conflicts between local decision and global target, resulting in suboptimal restoration scheme and reducing the efficiency of power system fault self-healing and the stability of power system operation. SUMMARY

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

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

[0006] Comparing the difference between the active power of each power resource and the net load of the subnetwork to quantify the supply-demand matching degree between each power resource and the subnetwork; analyzing the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree within the subnetwork to determine the voltage coordination degree between each power resource and the subnetwork; comparing the mutual correlation between the deviation of the node voltage in the subnetwork compared with the rated voltage and the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the subnetwork, and fusing the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the subnetwork;

[0007] Integrating the electrical distance between each power resource and the subnetwork and the closeness score to construct the correlation weight between each power resource and the subnetwork; based on the correlation weight, using the particle swarm optimization algorithm to construct the objective function to solve the power supply priority of each power resource to the subnetwork to control the power resource to deliver power to the subnetwork, thereby realizing fault self-healing.

[0008] Preferably, the process of quantifying the supply-demand matching degree between each power resource and the subnetwork is as follows:

[0009] The rated power of each power resource and the historical maximum load of the subnetwork are obtained, and the maximum value is selected from the rated power and the historical maximum load. The normalized value of the difference between the active power of each power resource and the net load of the subnetwork is taken as the supply-demand difference degree between each power resource and the subnetwork.

[0010] The supply-demand matching degree between each power resource and the subnetwork is negatively correlated with the supply-demand difference degree.

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

[0012] Based on the fluctuation degree of the node voltage in the subnetwork, a representative node is selected from all nodes in the subnetwork.

[0013] The correlation coefficient between the fluctuation degree of the active power of each power resource and the fluctuation degree of the voltage of the representative node in the subnetwork is taken as the voltage coordination degree between each power resource and the subnetwork.

[0014] Preferably, the representative node is the node with the largest voltage fluctuation degree in the subnetwork.

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

[0016] The deviation between the real-time voltage of the representative node in the subnetwork and the rated voltage is calculated and recorded as the real-time voltage deviation.

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

[0018] Preferably, the close degree score between each power resource and the subnetwork is positively correlated with the supply-demand matching degree, the voltage coordination degree, and the reactive power regulation coefficient, respectively.

[0019] Preferably, the association weight between each power resource and the subnetwork is negatively correlated with the electrical distance between each power resource and the subnetwork, and is positively correlated with the close degree score.

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

[0021] In a second aspect, the embodiments of the present application provide an Internet of Things-based power system fault self-healing device, a computer program is stored in the device, and the computer program is executed by a processor to implement the Internet of Things-based power system fault self-healing method according to any one of the above.

[0022] In a third aspect, the embodiments of the present application further 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, and the processor implements the Internet of Things-based power system fault self-healing method according to any one of the above when executing the computer program.

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

[0024] The present application quantifies the correlation characteristics of power resources and subnets in three dimensions of supply-demand matching, voltage coordination and reactive power regulation, constructs a closeness score, effectively solves the contradiction between local decision of edge computing and global optimization target; the strategy accurately captures the internal coupling relationship between resources and subnets through offline analysis, provides a scientific scheduling basis for online fault self-healing, thereby ensuring the response speed while realizing efficient coordination and accurate matching of global resources, which helps to improve the efficiency of power system fault recovery and the stability of power system operation;

[0025] Further, the present application introduces the electrical distance to correct the closeness score in space, constructs the correlation weight representing the actual scheduling value of the power resource, and constructs a particle swarm optimization model with the correlation weight as the core to solve the optimal power supply scheme, effectively overcoming the limitation of simply relying on the operation coupling degree and ignoring the transmission loss; the method realizes the secondary optimization distribution of the fault area power on the premise of ensuring to meet the load demand and resource constraints, significantly improves the economy and response speed of the scheduling decision, ensures the efficiency and stability of the fault self-healing process, and finally improves the efficiency of the power system fault self-healing and the stability of the power system operation. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0027] Figure 1 A step flow chart of the power system fault self-healing method based on the Internet of Things is provided for one embodiment of the present application;

[0028] Figure 2 A schematic diagram of the associated weight extraction process is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the power system fault self-healing method, device and system based on the Internet of Things according to the present application, its specific implementation, structure, features and effects are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the 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 the present application belongs.

[0031] The specific scheme of the power system fault self-healing method, device and system based on the Internet of Things provided by the present application is described in detail below in combination with the drawings.

[0032] Please refer to Figure 1 which shows a step flow chart of the power system fault self-healing method based on the Internet of Things provided by one embodiment of the present application, which includes the following steps:

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

[0034] The total active power of each sub-network is collected by the smart meter at the grid-connection point of the power system in real time; the voltage of each node in each sub-network is collected by the voltage transformer in real time, and the node in the embodiment includes at least the feeder head (the substation side), the feeder middle section (the voltage drop zone), the feeder end, the distributed energy (DER), and the energy storage system access point; in the actual application process, the implementer can set the node participating in the analysis according to the specific situation, which is not limited in the embodiment; the active power and the reactive power of each power resource are collected by the energy storage converter monitoring module at the grid-connection point of each power resource, and the power resource in the embodiment includes the distributed energy (DER) and the energy storage station; the sampling rate of the above-mentioned data is f, wherein the value of f is artificially set, and the value of f in the embodiment is 100 Hz, which can be set by the implementer according to the specific situation in the actual application process, and the embodiment is not limited in particular; finally, the net load of the sub-network is determined based on the total active power of the sub-network and the active power of the power resource, and the net load of the sub-network in the embodiment is the difference between the total active power of the sub-network and the total active power of all power resources.

[0035] Thus, the data collection of the power system is completed, and the above-mentioned collected data is normalized respectively, and the normalization method in the embodiment adopts the maximum and minimum value normalization method; in the actual application process, other normalization methods can be used to normalize the data according to the specific situation, which is not limited in the embodiment.

[0036] The process of normalizing the data by using the maximum and minimum value normalization method is a known technology, and will not be described in detail.

[0037] S2: comparing the difference between the active power of each power resource and the net load of the sub-network to quantify the supply-demand matching degree between each power resource and the sub-network; analyzing the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of the node in the sub-network to determine the voltage coordination degree between each power resource and the sub-network; comparing the mutual correlation between the deviation of the node voltage in the sub-network compared with the rated voltage and the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the sub-network, and fusing the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the sub-network.

[0038] The fault self-healing technology of the power system is of great significance to improve the reliability and safety of the power grid. With the development of IoT technology, the comprehensive perception and intelligent control capability of the power system fault self-healing is significantly enhanced, but at the same time, new problems are caused. In order to improve the response speed of the power system, the existing scheme generally sinks the self-healing decision scheduling to the edge side through edge computing. However, this method of decision optimization by the edge side only considers local or local decision conditions, which is easy to cause the inconsistency between the local optimization goal and the system global goal, which is not conducive to the fault self-healing scheduling of the entire power system, and even causes decision failure or affects the stability of the power system operation. Therefore, a power system fault self-healing method that can balance local autonomy and global coordination is needed.

[0039] Therefore, in order to solve the above problems, the embodiment compares the difference between the active power of each power resource and the net load of the sub-network to quantify the supply-demand matching degree between each power resource and the sub-network; analyzes the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of the nodes in the sub-network to determine the voltage coordination degree between each power resource and the sub-network; compares the mutual correlation between the deviation of the node voltage in the sub-network compared with the rated voltage and the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the sub-network, and fuses the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the sub-network. Thus, in the event of a power system fault, the most suitable power resource can be quickly and accurately found to deliver electricity to the most needed place, thereby achieving high-quality fault self-healing. The specific process is as follows:

[0040] The fault self-healing of the power system has high time efficiency requirements and aims to minimize the impact of faults on the safety and stability of the power system. The existing technology often relies on edge computing to achieve local rapid optimization, but the decision based on local information cannot guarantee global quality. If global optimization is introduced at the moment of fault, it will face the dilemma of long calculation time and slow response speed. To solve this contradiction, the embodiment adopts the strategy of "offline global analysis and online real-time calling", places the global optimization calculation in the offline stage, and clarifies the power distribution logic of each power resource in the global range in advance. In the fault self-healing process, the offline analysis results can be directly called to ensure the response speed while realizing the efficient coordination support of global resources.

[0041] Specifically, the load change trend of each sub-network in the power system and the output fluctuation characteristics of the power resource can objectively reflect the closeness of the correlation between the two. This is manifested in three dimensions: first, in terms of load matching, the closer the correlation between the power resource and the sub-network, the higher the coincidence of the output change curve of the power resource and the load change curve of the sub-network; second, in terms of output characteristics, given the strong randomness of the power resource, its output fluctuation often causes synchronous fluctuation of the sub-network voltage, and the consistency of the fluctuations of the two constitutes the basis of the correlation degree; finally, in terms of voltage support, when the sub-network voltage fluctuates, the closely correlated resource can more sensitively adjust the reactive power, and the correlation between the change of the reactive power injection and the voltage fluctuation is significantly stronger.

[0042] Therefore, based on the above analysis, the present embodiment analyzes the power resource j and the sub-network i at the current time, first, by comparing the difference between the active power of the power resource j and the net load of the sub-network i, the supply-demand matching degree between the power resource j and the sub-network i is quantified, specifically:

[0043] In the present embodiment, the rated power of the power resource j at the current time and the historical maximum load of the sub-network i before the current time are obtained, wherein in the present embodiment, the historical period is set to be a preset period before the current time, and the value of the preset period is 10 minutes in the present embodiment, and the implementer can also set it himself according to the specific circumstances, and the present embodiment does not make special restrictions; further, the maximum value is selected from the rated power and the historical maximum load, and the absolute difference between the active power of the power resource j at each time within the preset period before the current time and the net load of the sub-network i at the current time is divided by the result of the maximum value, and the result is normalized, denoted as the supply-demand difference degree between the power resource j and the sub-network i at each time;

[0044] The supply-demand matching degree between the power resource j and the sub-network i at the current time is negatively correlated with the supply-demand difference degree.

[0045] It should be noted that there are many commonly used normalization methods, and in the present embodiment, the maximum and minimum value normalization method is used to normalize the difference between the active power of the power resource j and the net load of the sub-network i at each time divided by the maximum value, and the result is mapped to the range of [0, 1], and in actual application, as other implementation manners, the implementer can also use other normalization methods according to the specific circumstances, and the present embodiment does not make special restrictions.

[0046] It should be noted that, except for special instructions, the maximum and minimum value normalization method is used in the present embodiment for any content involving normalization.

[0047] It should be understood that the negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases, which can be a subtraction relationship or a division relationship, etc., determined by actual application.

[0048] Preferably, as an implementation form, in the embodiment, the method for determining the supply-demand matching degree between the power resource j and the subnetwork i is as follows: in a preset period before the current time, the difference between the supply-demand difference degree at each time is calculated, denoted as a supply-demand matching value, and the average of the supply-demand matching values at all times is taken as the supply-demand matching degree between the power resource j and the subnetwork i at the current time.

[0049] According to the supply-demand matching degree, it can be understood that the supply-demand matching degree is used to represent the supply-demand balance characteristics between the power resource output and the subnetwork net load, and reflects the capacity adaptation degree of the power resource as a power supply. The calculation of the supply-demand matching degree is mainly affected by the difference degree of the active power of the power resource and the net load of the subnetwork. The greater the difference degree, the lower the supply-demand matching degree, which reflects that the resource output cannot effectively cover the load gap, resulting in a decrease in the power supply reliability of the resource in the fault self-healing dispatching. On the contrary, the smaller the difference degree, the higher the supply-demand matching degree, which reflects that the resource output and the load demand are highly consistent, and the most accurate power supply support can be achieved with the smallest capacity margin, thereby significantly improving the efficiency and success rate of fault recovery.

[0050] Secondly, the embodiment determines the voltage coordination degree between the power resource j and the subnetwork i by analyzing the correlation between the fluctuation degree of the active power of the power resource j and the fluctuation degree of the voltage of the nodes in the subnetwork i. Specifically:

[0051] In the embodiment, first, based on the fluctuation degree of the voltage of the nodes in the subnetwork i, a representative node is selected from all the nodes in the subnetwork i. Specifically, in the embodiment, the representative node is the node with the largest voltage fluctuation degree in the subnetwork.

[0052] Further, the correlation coefficient between the fluctuation degree of the active power of the power resource j and the fluctuation degree of the voltage of the representative node in the subnetwork i is taken as the voltage coordination degree between the power resource j and the subnetwork i.

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

[0054] For the node a in the subnet i, the voltage sequence is composed of the voltage at the node a at all time points in the preset period before the current time point, the moving standard deviation sequence of the voltage sequence is calculated as the quantification result of the voltage fluctuation degree of the node a in the subnet i in the preset period before the current time point; similarly, for the power resource j, the power sequence is composed of the active power of the power resource at all time points in the preset period before the current time point, the moving standard deviation sequence of the power sequence is calculated as the quantification result of the active power fluctuation degree of the power resource j at the current time point; for each node in the subnet i, the mean of all elements in the moving standard deviation sequence of the voltage sequence of each node is calculated, and the node corresponding to the maximum mean is taken as the representative node; wherein the moving standard deviation sequence is obtained by using the moving standard deviation algorithm, in the embodiment, the sliding step in the moving standard deviation sequence algorithm is 1s, and the sliding window size is 100s, in the actual application process, the implementer can also set it by himself according to the specific circumstances, and the embodiment does not make special limitation, wherein the process of obtaining the moving standard deviation sequence by using the moving standard deviation algorithm is a known technology, and will not be described in detail.

[0055] In addition, it is supplemented that in the embodiment, the Pearson correlation coefficient between the active power fluctuation degree of the power resource j and the voltage fluctuation degree of the representative node in the subnet i, that is, the Pearson correlation coefficient between the moving standard deviation sequence of the active power sequence of the power resource j and the moving standard deviation sequence of the voltage sequence of the representative node in the subnet i, is taken as the correlation coefficient between the active power fluctuation degree of the power resource j and the voltage fluctuation degree of the representative node in the subnet i, in the actual application process, as other implementation manners, the implementer can also use other correlation coefficient calculation methods such as Spearman correlation coefficient according to the specific circumstances, and the embodiment does not make special limitation.

[0056] Wherein the calculation process of the Pearson correlation coefficient is a known technology, and will not be described in detail.

[0057] In particular, if the Pearson correlation coefficient is negative, or all elements in any one of the moving standard deviation sequence of the voltage sequence and the moving standard deviation sequence of the active power sequence are all 0, the voltage coordination degree is set to 0.

[0058] According to the voltage coordination degree, it can be understood that the voltage coordination degree is used to characterize the synchronization of the voltage fluctuation characteristics of the sub-network node and the active power of the power resource, and reflects the inherent coupling strength of the power resource and the sub-network in the electrical characteristics. Its calculation is mainly affected by the correlation coefficient between the fluctuation degree of the active power of the power resource and the fluctuation degree of the voltage of the representative node of the sub-network. The larger the correlation coefficient is, the higher the voltage coordination degree is, which reflects that the power resource and the sub-network are closely related, the output change of the power resource can be intuitively perceived by the power system, and the effectiveness of the participation in the regulation is stronger. On the contrary, the smaller the correlation coefficient is, the lower the voltage coordination degree is, which reflects that the coupling between the power resource and the sub-network is loose, the supporting effect of the power resource on the voltage of the sub-network is weak, and it is difficult to play a key role in the stable control in the self-healing process.

[0059] Further, the embodiment calculates the reactive power regulation coefficient between the power resource j and the sub-network i by comparing the mutual correlation between the deviation of the node voltage in the sub-network i compared with the rated voltage and the reactive power of the power resource j. Specifically:

[0060] The deviation between the real-time voltage of the representative node in the sub-network i and the rated voltage is calculated, which is denoted as the real-time voltage deviation of the sub-network i.

[0061] The mutual correlation coefficient between the voltage deviation of the representative node in the sub-network i at the historical time and the reactive power of the power resource j at the historical time is denoted as the reactive power regulation coefficient between the power resource j and the sub-network i, that is, the normalized value of the mutual correlation coefficient between the voltage deviation of the representative node in the sub-network i and the reactive power of the power resource j at all times within a preset time period before the current time is denoted as the reactive power regulation coefficient between the power resource j and the sub-network i at the current time.

[0062] The calculation process of the mutual correlation coefficient is a known technology, and will not be described in detail.

[0063] In particular, if the voltage deviation is all 0 or the reactive power is all 0 in the preset time period during the mutual correlation analysis, the reactive power regulation coefficient is set to 0.5.

[0064] According to the reactive power regulation coefficient, it can be understood that the reactive power regulation coefficient is used to characterize the compensation ability of the reactive power change of the power resource to the voltage deviation of the sub-network, and reflects the functional contribution of the power resource to maintain the local voltage stability. Its calculation is mainly affected by the mutual correlation between the voltage deviation of the sub-network and the reactive power of the power resource. The larger the mutual correlation coefficient is, the higher the reactive power regulation coefficient is, which reflects that the power resource can sensitively capture the voltage fluctuation and timely output reactive power support, which is helpful to quickly flatten the voltage out-of-limit in the fault self-healing. On the contrary, the smaller the mutual correlation is, the lower the reactive power regulation coefficient is, which reflects that the power resource is slow to react to the voltage fluctuation or lacks reactive power regulation ability, and it is difficult to guarantee the voltage safety in 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] So far, the embodiment quantifies the correlation characteristics of power resources and subnets in supply-demand matching, voltage coordination and reactive power regulation, constructs a closeness score, effectively solves the contradiction between local decision and global optimization of edge computing, and provides a scientific scheduling basis for online fault self-healing by accurately capturing the internal coupling relationship between resources and subnets through offline analysis, thereby ensuring response speed while achieving efficient coordination and accurate matching of global resources, which helps to improve the efficiency of power system fault recovery and the stability of power system operation.

[0072] S3: Based on the electrical distance and the closeness score between each power resource and subnet, an association weight between each power resource and subnet is constructed; based on the association weight, a particle swarm optimization algorithm is used to construct a target function to solve the power supply priority of each power resource to the subnet to control the power resource to transmit power to the subnet to realize fault self-healing.

[0073] The above closeness score is based on historical operation data and measures the correlation closeness between any subnet and resource from a global perspective. However, in actual scheduling and distribution, power transmission is accompanied by power loss, and the extension of transmission distance will reduce controllability and stability, therefore, the spatial distribution characteristics between subnets and power resources are also key factors to determine the scheduling efficiency. In view of this, the embodiment introduces spatial distribution characteristics for weighting correction on the basis of the closeness score, that is: by comprehensively considering the electrical distance and the closeness score between each power resource and subnet, the association weight between each power resource and subnet is constructed; based on the association weight, a particle swarm optimization algorithm is used to construct a target function to solve the power supply priority of each power resource to the subnet to control the power resource to transmit power to the subnet to realize fault self-healing, and the specific process is as follows:

[0074] Firstly, for power resource j and subnet i, the embodiment constructs the association weight between power resource j and subnet i at the current time by comprehensively considering the electrical distance and the closeness score between power resource j and subnet i, specifically:

[0075] In the 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 closeness score.

[0076] Wherein, the calculation process of the electrical distance is a known technology, and will not be described in detail.

[0077] Preferably, as an implementation manner, in the embodiment, the association weight between power resource j and subnet i at the current time is The expression is: ; in the formula, represents an electrical distance value between the power resource j and the subnet i, wherein the electrical distance value is a dimensionless value without unit after removing the electrical distance; represents a closeness score between the power resource j and the subnet i at the current time; exp() represents an exponential function with a natural constant as a base number; norm[] represents a normalization function.

[0078] Preferably, the association weight extraction process schematic diagram provided by the embodiment is as shown in Figure 2

[0079] According to the association weight, it can be understood that the association weight is used to represent the final priority of the power resource after comprehensively considering the operation coupling and the physical constraint, and reflects the actual competitiveness of the power resource in the fault recovery scheme; the calculation is jointly affected by the closeness score and the electrical distance, if the electrical distance between the current power resource and the subnet is smaller and the closeness score is larger, the association weight is larger, which reflects that the current power resource has high operation coupling degree, small transmission loss and strong controllability, and should be preferentially dispatched to realize efficient self-healing, otherwise, the electrical distance between the current power resource and the subnet is larger and the closeness score is smaller, the corresponding association weight is smaller, which reflects that the current power resource has poor economic efficiency and weak control effect, and should be degraded or excluded in the optimized dispatching.

[0080] Based on the above analysis, the association weight between each power resource and any subnet is obtained by traversing all power resources and all subnets, and further, based on the association weight, the particle swarm optimization algorithm is used to construct the objective function to solve the power supply priority of each power resource to the subnet, so as to control the power resource to deliver power to the subnet and realize fault self-healing, specifically:

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

[0082] The constraint condition of the objective function is that the total amount of electric energy supplied by any power resource to all subnets is less than or equal to the rated power supply amount (which can be obtained by the power system) of itself, and the total amount of electric energy obtained by any subnet is less than or equal to the own net load.

[0083] Finally, the electric energy amount and the priority order of the supply of each power resource to each subnet are output. ​

[0084] According to the objective function, it can be understood that the objective function reflects the overall advantages and disadvantages of the power distribution strategy in meeting the load demand and utilizing the associated resources; its calculation is comprehensively affected by the power supply amount of each power resource to each subnetwork and the corresponding associated weight; the higher the associated weight and the more reasonable the power supply amount, the larger the objective function value, which reflects that the dispatching scheme fully utilizes high-priority resources, realizes efficient, stable and low-cost distribution of electric energy, and vice versa; the lower the associated weight or the improper power supply amount, the smaller the objective function value, which reflects that the dispatching scheme has resource mismatch, which may lead to poor recovery effect or decline of system stability.

[0085] Further, after obtaining the fault location in the power system, the circuit breaker is controlled to disconnect and isolate the relevant position, and then according to each subnetwork area affected after being disconnected, the corresponding objective function and constraint condition are constructed according to the particle swarm optimization algorithm process, and the secondary dispatching of the electric energy resource in the power system is performed, so that the affected subnetwork area recovers the electric energy supply as soon as possible and reduces the fault influence.

[0086] The particle swarm optimization algorithm is a known technology, and the process of obtaining the optimal solution is a known technology, which will not be described in detail.

[0087] So far, the embodiment introduces the electrical distance to modify the closeness score in space, constructs the associated weight representing the actual dispatching value of the power resource, and constructs the particle swarm optimization model based on the core to solve the optimal power supply scheme, which effectively overcomes the limitation of simply relying on the operation coupling degree and ignoring the transmission loss; under the premise of ensuring to meet the load demand and resource constraints, the method realizes the secondary optimization distribution of the electric energy in the fault area, significantly improves the economy and response speed of the dispatching decision, guarantees the efficiency and stability of the fault self-healing process, and finally improves the efficiency of the power system fault self-healing and the stability of the power system operation.

[0088] Based on the same inventive concept as the above method, the embodiments of the present application also provide an Internet of Things-based power system fault self-healing device, which stores a computer program. The computer program is executed by a processor to implement any of the above-mentioned Internet of Things-based power system fault self-healing methods.

[0089] Based on the same inventive concept as the above method, the embodiments of the present application also provide an Internet of Things-based power system fault self-healing system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. The processor executes the computer program to implement the steps of any of the above-mentioned Internet of Things-based power system fault self-healing methods.

[0090] It should be noted that the above-mentioned order of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0091] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0092] The above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for power system fault self-healing based on Internet of Things, characterized in that, The method comprises the following steps: Real-time acquisition of the net load of the sub-network, the voltage of each node, the active power and the reactive power of each power resource in the power system; Comparing the difference between the active power of each power resource and the net load of the sub-network to quantify the supply-demand matching degree between each power resource and the sub-network; analyzing the correlation between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree in the sub-network to determine the voltage coordination degree between each power resource and the sub-network; comparing the deviation of the node voltage in the sub-network compared with the rated voltage with the cross-correlation between the reactive power of each power resource to calculate the reactive power regulation coefficient between each power resource and the sub-network, and fusing the supply-demand matching degree and the voltage coordination degree to determine the closeness score between each power resource and the sub-network; Integrating the electrical distance between each power resource and the sub-network and the closeness score to construct the correlation weight between each power resource and the sub-network; based on the correlation weight, using the particle swarm optimization algorithm to construct the objective function to solve the power supply priority of each power resource to the sub-network to control the power resource to deliver power to the sub-network to realize fault self-healing.

2. The IoT-based power system fault self-healing method of claim 1, wherein, The quantification process of the supply-demand matching degree between each power resource and the sub-network is: Obtaining the rated power of each power resource and the historical maximum load of the sub-network, and selecting the maximum value from the rated power and the historical maximum load, and taking the normalized value of the difference between the active power of each power resource and the net load of the sub-network divided by the maximum value as the supply-demand difference degree between each power resource and the sub-network; The supply-demand matching degree between each power resource and the sub-network is negatively correlated with the supply-demand difference degree. 3.The IoT-based power system fault self-healing method of claim 1, wherein, The determination method of the voltage coordination degree between each power resource and the sub-network is: Based on the fluctuation degree of the node voltage in the sub-network, selecting a representative node from all nodes in the sub-network; Taking the correlation coefficient between the fluctuation degree of the active power of each power resource and the voltage fluctuation degree of the representative node in the sub-network as the voltage coordination degree between each power resource and the sub-network.

4. The IoT-based power system fault self-healing method of claim 3, wherein, The representative node is the node with the largest voltage fluctuation degree in the sub-network.

5. The IoT-based power system fault self-healing method of claim 1, wherein, The calculation process of the reactive power regulation coefficient between each power resource and the sub-network is: Calculating the deviation between the real-time voltage of the representative node in the sub-network and the rated voltage, denoted as real-time voltage deviation; Taking the cross-correlation coefficient between the voltage deviation of the representative node at the historical time and the reactive power of each power resource at the historical time as the reactive power regulation coefficient between each power resource and the sub-network.

6. The IoT-based power system fault self-healing method of claim 1, wherein, The closeness score between each power resource and the sub-network is positively correlated with the supply-demand matching degree, the voltage coordination degree, and the reactive power regulation coefficient, respectively.

7. The IoT-based power system fault self-healing method of claim 1, wherein, The correlation weight between each power resource and the sub-network is negatively correlated with the electrical distance between each power resource and the sub-network, and is positively correlated with the closeness score.

8. The IoT-based power system fault self-healing method of claim 1, wherein, An expression of the objective function is: wherein, represents the objective function with the argument being the electrical energy q; represents the electrical energy supplied by the power resource h to the u-th subnetwork; represents the association weight between the power resource j and the i-th subnetwork; represents the number of all power resources in the power system; represents the number of all subnetworks in the power system.

9. A power system fault self-healing device based on Internet of Things, wherein a computer program is stored in the device, characterized in that, The computer program is executed by the processor to realize the Internet of Things-based power system fault self-healing method according to any one of claims 1-8.

10. An Internet of Things based power system fault self-healing system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the Internet of Things-based power system fault self-healing method according to any one of claims 1-8.

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