A power distribution network outage time prediction method and system considering distributed resource access

Abstract

The application discloses a power distribution network outage time prediction method and system considering distributed resource access. When a certain element fails, the power supply of the upstream area of the fault point is restored after fault isolation, all power sources in the outage area and the tie switch connected with the outage line in the downstream area of the fault point are searched, the transfer supply area division and the transfer load analysis are carried out; if there is no tie switch and the fault line segment in the outage area, the power supply is restored after the fault is repaired, and the outage time of the area is calculated; if there is a tie switch in the transfer supply area and a standby power source can provide power support, the power supply of the area is transferred, and the outage time of the area is calculated; if there is no standby power source, the distributed resource is considered as a power source to provide power, the outage time prediction method of a new power distribution system is proposed for different areas involved in the outage, and the accuracy of the outage time prediction is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid distribution, and in particular relates to a method and system for predicting power outage time in a distribution network considering access of distributed resources. Background Art

[0002] The distribution network, located at the end of the power grid and directly connected to users, is responsible for distributing electricity from the transmission network to users. With the implementation of the country's "dual carbon" strategy and the development and construction of new power systems, an increasing number of distributed resources, such as distributed power sources, energy storage devices, and electric vehicles, are being connected to the distribution network. This has had a certain impact on the structure and operation of the distribution system. The new distribution system has shown new characteristics such as diversified power supply and interactive power consumption.

[0003] With the integration of distributed resources, users have more diverse ways to obtain electricity. The duration and impact of power outages in new distribution networks have also changed compared to traditional distribution networks. Therefore, the analysis methods for power outage duration should also change accordingly. Traditional distribution networks, however, analyze their operation and outage conditions solely based on the availability of power from the grid side. They fail to consider the power supply provided by distributed resources, resulting in a lack of accuracy in determining the duration of power outages. Summary of the Invention

[0004] To solve the above problems, the purpose of the present invention is to provide a distribution network outage time prediction method and system taking into account the access of distributed resources. Based on the supplement of distributed resources to the traditional centralized power supply mode, the present invention considers the situation where distributed photovoltaic, decentralized wind power, energy storage devices, electric vehicles and other distributed resources provide electricity as the power supply side, and proposes various situations in which their coordinated scheduling jointly assists the distribution network to provide electricity to users; for different areas involved in the power outage, a power outage time prediction method under a new distribution system is proposed to further improve the accuracy of the power outage time prediction.

[0005] To achieve the above objectives, the technical solution of the present invention is: a method for predicting power outage time in a distribution network considering the access of distributed resources, comprising the following steps:

[0006] When a component fails, the system isolates the fault and restores power to the upstream area. In the downstream area, the system searches for all power sources within the blackout area and the tie switches connected to the blackout line, divides the power supply area, and analyzes the load to be transferred.

[0007] If there is no tie switch or fault line segment in the power outage area, wait for the fault to be repaired before power is restored, and calculate the power outage time in the area;

[0008] If there is a tie switch in the transfer area and a backup power source is available to provide power support, power will be transferred to that area and the power outage time for that area will be calculated;

[0009] If there is no backup power supply:

[0010] If there is a tie switch in the transfer area and it is connected to wind, solar and storage distributed resources, the total power generation of wind, solar and storage is predicted based on the historical output curve of the wind, solar and storage distributed resources during the fault power outage time based on the time convolution network prediction model, and the total load demand is predicted based on the historical time series curve of the power outage load;

[0011] If the total power generation of the wind, solar, and storage distributed resources is greater than or equal to the total load demand, the power supply to the area will be restored through the wind, solar, and storage distributed resources, and the power outage time in the area will be calculated;

[0012] If the total power generation of wind, solar and storage distributed resources is less than the total load demand, and there are important users in the transfer area, then according to the preset load shedding plan, wind, solar and storage distributed resources will be used to transfer power to important users first, and the power outage time of this type of load will be calculated.

[0013] Preferably, the method of training the initialized temporal convolutional network prediction model using the data of the training set based on the improved particle swarm algorithm further includes:

[0014] Initialize the model parameters, including the inertia weights wini and wfin of the improved particle swarm algorithm, learning coefficients c1ini, c1fin, c2ini, c2fin, number of particles, number of iterations, and number of neurons;

[0015] The temporal convolutional network prediction model is trained using an improved particle swarm algorithm, and Pbest and Gbest are determined according to the initial fitness values;

[0016] Iteratively update the Pbest of each particle and the Gbest of the group. If the current iteration fitness value of the particle is less than Pbest, it is assigned to Pbestt, otherwise Pbest remains unchanged. From the perspective of the entire particle group, if there is a particle with a fitness value less than Gbest in this iteration, it is assigned to Gbest, otherwise Gbest remains unchanged.

[0017] Update the speed and position of each particle;

[0018] Determine whether the maximum number of iterations has been reached. If so, return the optimal parameter combination in the entire iteration process; otherwise, continue to use the IPPSO (particle swarm) algorithm to train the temporal convolutional network prediction model.

[0019] The returned parameters are used to build the temporal convolutional network prediction model.

[0020] Preferably, using the improved particle swarm optimization algorithm to train the temporal convolutional network prediction model further includes:

[0021] Exponential and cosine functions are introduced to perform nonlinear improvements on w to fine-tune the range of w, so that w reaches the optimal attenuation range [0.4, 0.9] and maintains dynamic changes during the entire iteration;

[0022] For the maximum number of iterations Tmax, the improvement of w for the tth time is as follows:

[0023]

[0024] Where wini and wfin are the initial and final values ​​of w, and n is the control coefficient for adjusting the smoothness of the exponential function, n=8.

[0025] Preferably, using the improved particle swarm optimization algorithm to train the temporal convolutional network prediction model further comprises:

[0026] Adaptively adjust c1 and c2 in the improved particle swarm algorithm so that they change linearly according to the number of iterations. The improved formula of the learning factor is as follows:

[0027]

[0028] Where: c1ini, c1fin, c2ini, c2fin are the initial and final values ​​of c1 and c2.

[0029] Preferably, obtaining historical power generation data of distributed resources within the distribution network further includes:

[0030] Collect historical operating output data and meteorological data of distributed resources to obtain the time series of distributed power generation;

[0031] Analyze the operating characteristics of distributed resources connected to the distribution network, including distributed photovoltaics, distributed wind power, energy storage devices, and electric vehicles;

[0032] Fit the time-output curves of various distributed resources.

[0033] Preferably, the method further includes: integrating the time-output curve of distributed resources with the primary topology data of the distribution network to construct a new distribution network topology model. The primary topology data of the distribution network refers to the equipment inventory and topology information of the current distribution network, including the topological connection relationship between transformers, lines, and switch facilities, basic equipment information including equipment number, name, and voltage level, equipment outage parameters including fault outage rate and average fault repair time, and load point data including load capacity, number of users, and importance level.

[0034] Preferably, enumerating the temporal convolutional network prediction model trained on multiple fault inputs to predict the power outage time of the distribution network further includes:

[0035] If there are important users in the transfer area, a load shedding plan will be formulated. For important users, electric vehicles will be used as the power supply side, combined with wind, solar and storage to transfer power, and the power outage time of this type of load will be calculated.

[0036] Based on the same concept, the present invention also provides a distribution network outage time prediction system considering distributed resource access, comprising:

[0037] The fault acquisition module is used to isolate the fault and restore power to the upstream area after a component fails. It also searches for all power sources in the outage area and the tie switches connected to the outage line in the downstream area of ​​the fault point, and divides the power supply area and analyzes the transferred load.

[0038] The prediction module is used to wait for the power supply to be restored after the fault is repaired if there is no connecting switch and fault line segment in the power outage area, and calculate the power outage time of the area; if there is a connecting switch in the transfer area and there is a backup power supply to provide power support, the power supply to the area is transferred and the power outage time of the area is calculated; if there is no backup power supply: if there is a connecting switch in the transfer area and there are wind, solar and storage distributed resources, the total power generation of wind, solar and storage is predicted based on the time convolution network prediction model according to the historical output curve of the wind, solar and storage distributed resources during the fault power outage time, and the total load demand is predicted according to the historical time series curve of the power outage load; if the total power generation of the wind, solar and storage distributed resources is greater than or equal to the total load demand, the power supply to the area is restored through the wind, solar and storage distributed resources, and the power outage time of the area is calculated; if the total power generation of the wind, solar and storage distributed resources is less than the total load demand, and there are important users in the transfer area, then according to the preset load shedding plan, the wind, solar and storage distributed resources are used to transfer power to important users, and the power outage time of this type of load is calculated.

[0039] Based on the same concept, the present invention further provides a computer device, comprising:

[0040] a memory for storing a processing program;

[0041] A processor, wherein when executing the processing program, the processor implements any one of the above-mentioned methods for predicting power outage time in a distribution network considering access of distributed resources.

[0042] Based on the same concept, the present invention also provides a readable storage medium having a processing program stored thereon. When the processing program is executed by a processor, any one of the above-mentioned methods for predicting power outage time in a distribution network considering access to distributed resources is implemented.

[0043] After adopting the above technical solution, compared with the prior art, the present invention has the following advantages:

[0044] The present invention considers the situation where various distributed resources such as distributed photovoltaic, distributed wind power, energy storage devices, electric vehicles, etc. are used as the power supply side to provide electricity, and proposes various situations in which they are coordinated and dispatched to jointly assist the distribution network in providing electricity to users; for different areas affected by power outages, a power outage time prediction method under a new distribution system is proposed to further improve the accuracy of power outage time prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings, wherein:

[0046] Figure 1 This is a general flow chart of a method for predicting power outage time in a distribution network considering access of distributed resources according to an embodiment of the present invention;

[0047] Figure 2 It is the w change curve diagram of the improved particle swarm algorithm of the present invention;

[0048] Figure 3 It is a basic framework flow chart of the IPPSO-TCN prediction model established by the present invention;

[0049] Figure 4 It is a power outage simulation flow chart of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims. It should be noted that the drawings are greatly simplified and not to exact ratios, and are intended solely to facilitate and clearly illustrate the embodiments of the present invention.

[0051] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0052] Example

[0053] like Figure 1 As shown, the contribution made by the inventors of this application to the power distribution network outage time prediction method considering the access of distributed resources is shown, including the following steps:

[0054] Step 1: Fit the timing characteristics of distributed resources. This involves analyzing the operating characteristics of various distributed resources and fitting their time-output curves.

[0055] The operating characteristics of distributed resources connected to the distribution network are analyzed. Distributed resources mainly include distributed photovoltaics, distributed wind power, energy storage devices, electric vehicles, etc.

[0056] The operating characteristics of distributed photovoltaic systems are as follows: they are primarily influenced by light intensity and temperature, exhibiting diurnal and seasonal characteristics. Output is higher during the daytime and midday, and essentially zero at night; output is higher in the summer and lower in the winter.

[0057] The operating characteristics of distributed wind power are as follows: primarily influenced by wind speed. When the wind speed exceeds the wind turbine's set cut-in wind speed, the wind turbine begins operating; when the wind speed exceeds the set rated wind speed, the wind turbine operates at rated power; when the wind speed exceeds the set cut-out wind speed, the wind turbine stops operating.

[0058] The operating characteristics of energy storage devices are as follows: Different energy storage technologies have different characteristics such as charging and discharging power, energy density, and discharge duration, but they play the same role in the power system, that is, by adjusting the charging and discharging power to absorb or release electrical energy to smooth out fluctuations in renewable energy output and ensure the safe and stable operation of the power grid.

[0059] The operating characteristics of electric vehicles are primarily determined by the charging behavior of the owner. For example, for private cars, charging locations are primarily concentrated at home and work, and charging times are primarily concentrated at night and during work.

[0060] Based on the above analysis of operating characteristics, the time-output curves of various distributed resources are fitted according to the actual conditions in different regions.

[0061] Step 2: Build a graph model. Integrate the time-output curves of distributed resources with the primary topology data of the distribution network to build a new distribution network topology model.

[0062] Collect historical and forecasted output data for various distributed resources, as well as primary distribution network topology data. This data primarily refers to the equipment inventory and topology information for the current distribution network, including topological connectivity between transformers, lines, switches, and other facilities; basic equipment information such as equipment number, name, and voltage level; equipment outage parameters such as outage rate and mean time to repair; and load point data such as load capacity, number of users, and importance level.

[0063] For distributed resources already connected to the distribution network, the distribution network topology model is updated based on their actual access situation. For distributed resources planned to be connected to the distribution network, a new distribution system network topology model is constructed based on the existing distribution system network topology and real-time load conditions, taking into account the access location, access capacity, and access method of the distributed resources.

[0064] Step 3: Determine the power generation of distributed resources. Build an improved particle swarm optimization algorithm-temporal convolutional network (IPPSO-TCN) model to predict the power generation of distributed generation and analyze the total capacity of distributed resources during the fault outage time.

[0065] First, historical operating output data and meteorological data from distributed generation systems are collected to generate a time series of distributed generation output. This data is preprocessed, including removing outliers, filling in missing values, and normalizing the data. The preprocessed data is then divided into training and test sets.

[0066] Then, the IPPSO-TCN model is established. This model consists of two parts: improving the particle swarm algorithm and running the improved particle swarm algorithm to optimize the parameters of the temporal convolutional network model to establish the IPPSO-TCN model.

[0067] The particle swarm optimization (PSO) compares the birds in a flock to particles in a fixed space. Each particle updates its own optimal value, Pbest, through self-searching. Particles communicate with each other and jointly search for the global optimal value, Gbest. Through continuous iteration, the optimal solution within the search area is found. In a d-dimensional search space, n particles form a swarm x = [x1,…,xn], the position of the i-th particle xi = [xi1,…,xid], the velocity vi = [vi1,…,vid], the optimal value pi = [pi1,…,pid], and the global optimal value of the particle swarm pg = [pg1,…,pgd]. The formulas for updating the velocity and position of the k-th particle are as follows:

[0068]

[0069]

[0070] Where: w is the inertia weight, which indicates the degree to which the particle maintains its velocity at the previous moment; c1 and c2 are learning factors; r1 and r2 are random numbers between [0,1].

[0071] The method to improve the PSO algorithm is as follows:

[0072] (1) Improvement of inertia weight

[0073] Exponential and cosine functions are introduced to improve w nonlinearly. According to the algorithm mechanism of the exponential function, when the exponent is negative with e as the base, the image shows signs of rapid decay followed by slow decay. As a result, after the 8th iteration, the change range of w approaches 0, which is not conducive to the dynamic change of weights. Therefore, on this basis, the cosine function is added to fine-tune the range of w, so that w reaches the optimal decay range [0.4, 0.9] and maintains dynamic changes during the entire iteration. The change curve is as follows Figure 2 shown.

[0074] For the maximum number of iterations Tmax, the improvement of w for the tth time is as follows:

[0075]

[0076] Where: wini and wfin are the initial and final values ​​of w; n is the control coefficient for adjusting the smoothness of the exponential function, n = 8.

[0077] (2) Improvement of learning factors

[0078] In the original PSO algorithm, c1 and c2 are fixed values ​​set based on experience and do not match the particle's trajectory. This paper makes adaptive time-varying adjustments to c1 and c2 so that they change linearly according to the number of iterations. The improved formula for the learning factor is as follows:

[0079]

[0080] Where: c1ini, c1fin, c2ini, c2fin are the initial and final values ​​of c1 and c2.

[0081] Finally, the established IPPSO-TCN model is used to predict the power generation of distributed power sources, and the total power generation of distributed power sources during the fault outage time is obtained.

[0082] The basic framework of the established IPPSO-TCN prediction model is as follows Figure 3 shown.

[0083] The specific steps for building a prediction model are as follows:

[0084] (1) Obtain historical data, preprocess the data, replace outliers exceeding the threshold with the threshold, fill missing values ​​with mean interpolation, and divide the preprocessed data set into training and test sets;

[0085] (2) Initialize the model parameters, including the inertia weights wini and wfin of the IPPSO algorithm, the learning coefficients c1ini, c1fin, c2ini, c2fin, the number of particles, the number of iterations, the number of neurons, etc.

[0086] (3) Use the IPPSO algorithm to train the prediction model and determine Pbest and Gbest based on the initial fitness value;

[0087] (4) Iteratively update the Pbest of each particle and the Gbest of the swarm. If the current iteration fitness value of a particle is less than Pbest, it is assigned to Pbestt, otherwise Pbest remains unchanged; from the perspective of the entire particle swarm, if there is a particle with a fitness value less than Gbest in this iteration, it is assigned to Gbest, otherwise Gbest remains unchanged;

[0088] (5) Update the velocity and position of each particle;

[0089] (6) Determine whether the maximum number of iterations has been reached. If so, return to the optimal parameter combination in the entire iteration process; otherwise, return to step (3);

[0090] (7) Use the returned parameters to build a model and make predictions about distributed resources.

[0091] Step 4: Power outage simulation: Analyze the probability of distributed resources serving as backup power sources and use the minimum path method to simulate faulty components, power outage scope, and power outage duration.

[0092] Enumerate single component faults, record the process of circuit breaker tripping, fault isolation, load transfer, and power restoration after the fault, determine the power outage scope affected by the fault, and further predict the power outage time of the distribution network.

[0093] The specific process is as follows:

[0094] 1. When a component fails and the circuit breaker trips, causing a power outage on the entire line, the fault is located and isolated, and power is restored to the area upstream of the fault point. The following analysis is performed for the area downstream of the fault point:

[0095] 2. Search all power sources within the blackout area and the tie switches connected to the blackout lines, divide the power transfer area, analyze the power transfer load, and rank the loads according to the analysis results;

[0096] 3. If there is a tie switch in the transfer area and a backup power supply is available to provide power support, power will be transferred to that area and the power outage time will be calculated. If there is no backup power supply, proceed to the next step of analysis.

[0097] 4. If there is a tie switch in the transfer area and it is connected to distributed resources, the IPPSO-TCN model is established to predict the total wind, solar and storage power generation in the area based on the historical output curve of wind, solar and storage during the fault power outage. The total load demand is predicted based on the historical time series curve of the power outage load.

[0098] 5. If the total power generation of wind, solar and storage is greater than or equal to the total load demand, the power supply to the area will be restored through wind, solar and storage, and the power outage time in the area will be calculated. Otherwise, the next step of analysis will be carried out;

[0099] 6. If there are important users in the transfer area, a load shedding plan will be formulated. For important users, electric vehicles will be used as the power supply side, combined with wind, solar and storage to transfer power, and the power outage time of this type of load will be calculated;

[0100] 7. If there is no interconnecting switch or faulty line segment in the power outage area, wait for the fault to be repaired and then restore power supply, and calculate the power outage time in the area.

[0101] The overall flow chart of power outage simulation is as follows: Figure 4 shown.

[0102] Step 5: Predict power outage duration. Simulate power outage events on the new distribution network multiple times and predict the power outage duration for different power outage areas.

[0103] The power outage time in the upstream area of ​​the fault point is:

[0104] t 故障点上游区域 =t 故障定位隔离 +t 故障点上游恢复供电操作

[0105] The power outage time in the backup power supply area is:

[0106] t 备用电源转供区域 =t 故障定位隔离 +t 联络开关切换

[0107] The power outage time in the distributed resource transfer area is:

[0108] t 分布式资源转供区域 =t 故障定位隔离 +t 分布式资源总发电量判断 +t 联络开关切换

[0109] The power outage time in the area without tie switches is:

[0110] t 无联络开关区域 =t 故障定位隔离 +t 故障修复

[0111] The power outage time of the fault section is:

[0112] t 故障段 =t 故障定位隔离 +t 故障修复

[0113] Compared with the prior art, the present invention has the following beneficial effects:

[0114] With the current large-scale integration of distributed resources into the distribution network, traditional methods for analyzing power outage duration are no longer applicable. This paper considers the situation where multiple distributed resources, such as distributed photovoltaics, distributed wind power, energy storage devices, and electric vehicles, act as power sources to provide electricity. It proposes various scenarios in which these resources can coordinate and dispatch to assist the distribution network in providing power to users. Furthermore, a new method for predicting power outage duration under a new distribution system is proposed for different regions affected by power outages, further improving the accuracy of outage duration predictions.

[0115] The application provides a prediction model based on an IPPSO-TCN, which is used for predicting the power generation of distributed power sources.

[0116] Referring to Figure 4 The power outage simulation flowchart shows the actual flow during the operation of the power distribution system.

[0117] A power distribution network power outage time prediction method considering distributed resource access comprises the following steps:

[0118] When a component fails, the power supply to the upstream area of the fault point is restored after fault isolation, and all power sources in the power outage area and tie switches connected to the power outage line in the downstream area of the fault point are searched, and the transfer supply area division and transfer load analysis are performed;

[0119] If there is no tie switch and fault line segment in the power outage area, the power supply is restored after the fault is repaired, and the power outage time of the area is calculated;

[0120] If there is a tie switch in the transfer supply area and a standby power source can provide power support, the power supply is transferred to the area, and the power outage time of the area is calculated;

[0121] If there is no standby power source:

[0122] If there is a tie switch in the transfer supply area and a wind-solar-storage distributed resource is connected, the total power generation of the wind-solar-storage distributed resource is predicted based on a time convolution network prediction model according to the historical output curve of the wind-solar-storage distributed resource in the fault power outage time, and the total load demand is predicted according to the historical time sequence curve of the power outage load;

[0123] If the total power generation of the wind-solar-storage distributed resource is greater than or equal to the total load demand, the power supply to the area is restored by the wind-solar-storage distributed resource, and the power outage time of the area is calculated;

[0124] If the total power generation of the wind-solar-storage distributed resource is less than the total load demand, and the transfer supply area contains important users, the wind-solar-storage distributed resource is used to transfer power to the important users according to a preset load shedding scheme, and the power outage time of the load is calculated.

[0125] Based on the same concept, the application also provides a power distribution network power outage time prediction system considering distributed resource access, comprising:

[0126] The fault acquisition module is used to isolate the fault and restore power to the upstream area after a component fails. It also searches for all power sources in the outage area and the tie switches connected to the outage line in the downstream area of ​​the fault point, and divides the power supply area and analyzes the transferred load.

[0127] The prediction module is used to wait for the fault to be repaired and the power supply to be restored after the power outage area is restored, and calculate the power outage time of the area if there is a connecting switch and a fault line segment in the power outage area; if there is a connecting switch in the transfer area and a backup power supply can provide power support, the power supply to the area is transferred and the power outage time of the area is calculated; if there is no backup power supply: if there is a connecting switch in the transfer area and wind, solar and storage distributed resources are connected, the total power generation of wind, solar and storage is predicted based on the time convolution network prediction model according to the historical output curve of the wind, solar and storage distributed resources during the fault power outage time, and the total load demand is predicted according to the historical time series curve of the power outage load; if the total power generation of the wind, solar and storage distributed resources is greater than or equal to the total load demand, the power supply to the area is restored through the wind, solar and storage distributed resources, and the power outage time of the area is calculated; if the total power generation of the wind, solar and storage distributed resources is less than the total load demand, and there are important users in the transfer area, then according to the preset load shedding plan, the wind, solar and storage distributed resources are used to transfer power to important users, and the power outage time of this type of load is calculated.

[0128] Based on the same concept, the present invention further provides a computer device, comprising:

[0129] a memory for storing a processing program;

[0130] A processor, wherein when executing the processing program, the processor implements any one of the above-mentioned methods for predicting power outage time in a distribution network considering access of distributed resources.

[0131] Based on the same concept, the present invention also provides a readable storage medium having a processing program stored thereon. When the processing program is executed by a processor, any one of the above-mentioned methods for predicting power outage time in a distribution network considering access to distributed resources is implemented.

[0132] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program code.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting power outage time in a distribution network considering the access of distributed resources, characterized in that: The following steps are involved: When a component fails, the system isolates the fault and restores power to the upstream area. In the downstream area, the system searches for all power sources within the blackout area and the tie switches connected to the blackout line, divides the power supply area, and analyzes the load to be transferred. If there is no tie switch or fault line segment in the power outage area, wait for the fault to be repaired before power is restored, and calculate the power outage time in the area; If there is a tie switch in the transfer area and a backup power source is available to provide power support, power will be transferred to that area and the power outage time for that area will be calculated; If there is no backup power supply: If there is a tie switch in the transfer area and it is connected to distributed wind, solar and storage resources, the total power generation of wind, solar and storage is predicted based on the historical output curve of the distributed wind, solar and storage resources during the fault power outage time using the improved particle swarm algorithm-temporal convolutional network model. The total load demand is predicted based on the historical time series curve of the power outage load. If the total power generation of the wind, solar, and storage distributed resources is greater than or equal to the total load demand, the power supply to the area will be restored through the wind, solar, and storage distributed resources, and the power outage time in the area will be calculated; If the total power generation of wind, solar, and storage distributed resources is less than the total load demand, and there are important users in the transfer area, the wind, solar, and storage distributed resources will be used to transfer power to important users according to the preset load shedding plan, and the power outage time of this type of load will be calculated; The method further includes using an improved particle swarm algorithm to train the temporal convolutional network prediction model, and further includes: The exponential function and cosine function are introduced to perform nonlinear improvement on the inertia weight w to fine-tune the variation range of the inertia weight w, so that the inertia weight w reaches the optimal attenuation range [0.4, 0.9] and maintains dynamic changes during the entire iteration; For the maximum number of iterations Tmax, the inertia weight w of the tth iteration is improved as follows: Where wini and wfin are the initial and final values ​​of w, and n is the control coefficient for adjusting the smoothness of the exponential function, n=8.

2. The method for predicting power outage time of a distribution network considering distributed resource access according to claim 1, characterized in that: The method of training the initialized temporal convolutional network prediction model using the data of the training set based on the improved particle swarm algorithm further includes: Initialize the model parameters, including the initial value wini and final value wfin of the inertia weight of the improved particle swarm algorithm, the initial value c1ini and final value c1fin of the learning coefficient c1, the initial value c2ini and final value c2fin of the learning coefficient c2, the number of particles, the number of iterations, and the number of neurons; The improved particle swarm algorithm is used to train the temporal convolutional network prediction model, and the individual optimal value Pbest and the global optimal value Gbest are determined according to the initial fitness value; Iteratively update the individual optimal value Pbest of each particle and the global optimal value Gbest of the group. If the current iteration fitness value of the particle is less than the individual optimal value Pbest, it will be assigned the individual optimal value Pbest, otherwise the individual optimal value Pbest remains unchanged. From the perspective of the entire particle group, if there is a particle with a fitness value less than the global optimal value Gbest in this iteration, it will be assigned the global optimal value Gbest, otherwise the global optimal value Gbest remains unchanged. Update the speed and position of each particle; Determine whether the maximum number of iterations has been reached. If so, return the optimal parameter combination in the entire iteration process; otherwise, continue to use the improved particle swarm algorithm to train the temporal convolutional network prediction model; The returned parameters are used to build the temporal convolutional network prediction model.

3. The method for predicting power outage time in a distribution network considering distributed resource access according to claim 2, characterized in that: Using the improved particle swarm algorithm to train the temporal convolutional network prediction model further includes: The learning coefficients c1 and c2 in the improved particle swarm algorithm are adaptively adjusted to change linearly according to the number of iterations. The improved formula of the learning coefficient is as follows: Where: c1ini, c1fin, c2ini, c2fin are the initial and final values ​​of c1 and c2.

4. The method for predicting power outage time of a distribution network considering access of distributed resources according to claim 1, characterized in that: Obtaining historical power generation data of distributed resources within the distribution network further includes: Collect historical operating output data and meteorological data of distributed resources to obtain the time series of distributed power generation; Analyze the operating characteristics of distributed resources connected to the distribution network, including distributed photovoltaics, distributed wind power, energy storage devices, and electric vehicles; Fit the time-output curves of various distributed resources.

5. The method for predicting power outage time in a distribution network considering access to distributed resources according to claim 4, characterized in that: It also includes integrating the time-output curve of distributed resources with the primary topology data of the distribution network to construct a new distribution network topology model. The primary topology data of the distribution network refers to the equipment inventory and topology information of the existing distribution network, including the topological connection relationship between transformers, lines, and switch facilities, basic equipment information including equipment number, name, and voltage level, equipment outage parameters including fault outage rate and average fault repair time, and load point data including load capacity, number of users, and importance level.

6. The method for predicting power outage time in a distribution network considering distributed resource access according to claim 1, characterized in that: If there are important users in the transfer area, a load shedding plan will be formulated. For important users, electric vehicles will be used as the power supply side, combined with wind, solar and storage to transfer power, and the power outage time of this type of load will be calculated.

7. A power outage time prediction system for a distribution network considering the access of distributed resources, characterized in that: include: The fault acquisition module is used to isolate the fault and restore power to the upstream area after a component fails. It also searches for all power sources in the outage area and the tie switches connected to the outage line in the downstream area of ​​the fault point, and divides the power supply area and analyzes the transferred load. The prediction module is used to wait for the power supply to be restored after the fault is repaired if there is no tie switch and fault line segment in the power outage area, and calculate the power outage time of the area; If there is a connecting switch in the transfer area and a backup power supply can provide power support, the power supply to the area will be transferred and the power outage time of the area will be calculated; if there is no backup power supply: if there is a connecting switch in the transfer area and it is connected to wind, solar and storage distributed resources, the total power generation of wind, solar and storage will be predicted based on the improved particle swarm algorithm-time convolution network model according to the historical output curve of the wind, solar and storage distributed resources during the fault power outage time, and the total load demand will be predicted based on the historical time series curve of the power outage load; if the total power generation of the wind, solar and storage distributed resources is greater than or equal to the total load demand, the power supply to the area will be restored through the wind, solar and storage distributed resources, and the power outage time of the area will be calculated; If the total power generation of the wind, solar, and storage distributed resources is less than the total load demand, and there are important users in the transfer area, then according to the preset load shedding plan, the wind, solar, and storage distributed resources are preferentially used to transfer power to the important users, and the power outage time of this type of load is calculated; it also includes using an improved particle swarm algorithm to train the temporal convolutional network prediction model, further including: introducing an exponential function and a cosine function to perform nonlinear improvement on the inertia weight w to fine-tune the range of change of the inertia weight w, so that the inertia weight w reaches the optimal attenuation range [0.4, 0.9] and maintains dynamic changes during the entire iteration period; the inertia weight w for the maximum number of iterations Tmax and the tth iteration is improved as follows: , where wini and wfin are the initial and final values ​​of w, and n is the control coefficient for adjusting the smoothness of the exponential function, n=8.

8. A computer device, characterized in that: include: a memory for storing a processing program; A processor, wherein when executing the processing program, the processor implements the distribution network outage time prediction method considering distributed resource access as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium stores a processing program, and when the processing program is executed by the processor, the method for predicting power outage time of a distribution network considering distributed resource access according to any one of claims 1 to 6 is implemented.

Images