Bearing capacity analysis method and device of double-chain-ring power distribution network and electronic equipment

Abstract

The invention discloses a bearing capacity analysis method and device for a double-chain-ring power distribution network and electronic equipment, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining distributed photovoltaic power data, distributed wind power data, an independent energy storage operation state and the load capacity of a distribution network load corresponding to the double-chain-ring power distribution network; performing feature extraction on the distributed photovoltaic power data, the distributed wind power data, the operation state of the independent energy storage and the load capacity to obtain various feature data; and inputting the various feature data into a neural network model, and calculating the bearing capacity of the double-chain-loop power distribution network based on the neural network model. Therefore, on the premise of ensuring the safety and reliability of the power distribution network, the access of the distributed new energy, the energy storage and the load is optimized, and the multi-element interaction capability of the source, the network, the load and the energy storage is improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a method, device and electronic equipment for analyzing the carrying capacity of a double-chain ring distribution network. Background Art

[0002] In the existing double-chain distribution network, due to the small installed capacity of distributed renewable energy and strong uncertainty, the access of distributed energy and independent energy storage often ignores the analysis of the carrying capacity of the regional power grid, resulting in uneven distribution of power grid currents, and frequent light and heavy loads of main transformers and lines. Therefore, how to accurately quantify the output forecast of distributed power sources, and cooperate with independent energy storage on the grid side to smooth the output fluctuation of renewable energy, increase the inertia response of renewable energy units, quickly respond to load demands, and improve the carrying capacity of local power grids has become a problem that must be solved in the power system. Summary of the invention

[0003] In view of this, the present application provides a carrying capacity analysis method, device and electronic equipment for a dual-chain distribution network to solve the problems of uneven power flow distribution in the power grid caused by the access of distributed energy and independent energy storage, and frequent light and heavy loads of main transformers and lines.

[0004] On the one hand, an embodiment of the present application provides a method for analyzing the carrying capacity of a dual-chain ring distribution network, the method comprising:

[0005] Obtain the distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load of the distribution network corresponding to the dual-chain ring distribution network;

[0006] Feature extraction is performed on distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage, and load to obtain a variety of feature data;

[0007] A variety of characteristic data are input into the neural network model, and the carrying capacity of the double-chain ring distribution network is calculated based on the neural network model; wherein, the calculation process of the carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network.

[0008] On the one hand, an embodiment of the present application provides a device for analyzing the carrying capacity of a dual-chain ring distribution network, the device comprising:

[0009] An acquisition module is used to obtain distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load of distribution network corresponding to the dual-chain ring distribution network;

[0010] The feature extraction module is used to extract features of distributed photovoltaic power data, distributed wind power data, the operating status of independent energy storage, and load, and obtain a variety of feature data;

[0011] The calculation module is used to input a variety of characteristic data into the neural network model, and calculate the carrying capacity of the double-chain ring distribution network based on the neural network model; wherein the calculation process of the carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network.

[0012] In a possible embodiment, a double-chain-ring distribution network includes: a first substation bus, a second substation bus and N switch stations, where N is an integer greater than or equal to 1; the switch station includes a first switch station bus and a second switch station bus, and the first switch station bus and the second switch station bus are connected by a connecting switch; the first substation bus, N first switch station busbars, and the second side substation bus are connected in a chain shape, and each interface is provided with a connecting switch; and the first substation bus, N second switch station busbars, and the second substation bus are connected in a chain shape, and each interface is provided with a connecting switch; distributed photovoltaic and independent energy storage are respectively connected to the first switch station bus through the connecting switch, and distributed wind power and distribution network loads are respectively connected to the second switch station bus through the connecting switch.

[0013] In a possible embodiment, the first substation bus includes a first bus section and a second bus section, and the second substation bus includes a third bus section and a fourth bus section; wherein the first bus section and the second bus section, and the third bus section and the fourth bus section are connected based on a connecting switch, respectively; then, the first bus section, N first switch station busbars, and the third bus section are connected in a chain shape, and each interface is provided with a connecting switch; and the second bus section, N second switch station busbars, and the fourth bus section are connected in a chain shape, and each interface is provided with a connecting switch.

[0014] In a possible embodiment, the calculation module is used to: calculate based on the activation function, after adding the product of the feature vectors corresponding to multiple feature data and the first layer weight matrix of the neural network model plus the first bias vector, to obtain the first layer calculation result; calculate based on the activation function, after adding the product of the first layer calculation result and the second layer weight matrix of the neural network model plus the second bias vector, to obtain the second layer calculation result; calculate based on the activation function, after adding the product of the second layer calculation result and the third layer weight matrix of the neural network model plus the third bias vector, to obtain the carrying capacity of the dual-chain distribution network.

[0015] In a possible embodiment, the model formula of the neural network model is:

[0016] C y=σ(W3·σ(W2·σ(W1·F+b1)+b2)+b3);

[0017] Among them, C y is the carrying capacity predicted by the neural network model, σ is the activation function, F is the feature vector corresponding to the multiple feature data, W1, W2, W3 are the first layer weight matrix, the second layer weight matrix, and the third layer weight matrix of the neural network model, b1, b2, b3 are the first bias vector, the second bias vector, and the third bias vector of the neural network model, respectively.

[0018] In a possible embodiment, the device further includes:

[0019] The training module is used to train the preset network model based on the preset loss function to obtain a neural network model. The preset loss function is:

[0020]

[0021] Among them, C y,i is the carrying capacity predicted by the neural network model for the i-th training sample among n training samples, where n is an integer greater than or equal to 1, and C actual,i is the actual carrying capacity corresponding to the i-th training sample. The calculation process of the actual carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate.

[0022] In a possible embodiment, the calculation formula of the actual bearing capacity is:

[0023]

[0024] Among them, V d is the bus voltage deviation rate, V dmax is the maximum allowable value of bus voltage deviation rate; P loss is the power loss of the line, P rated is the rated power of the distribution network system, P lmax is the maximum permissible ratio of line power loss to the rated power of the distribution network system; f d is the frequency deviation rate of the distribution network system, f dmax is the maximum permissible value of the frequency deviation rate of the distribution network system; L f is the load factor, L fmax is the maximum allowable value of the load rate.

[0025] On the one hand, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any of the above-mentioned dual-chain ring distribution network carrying capacity analysis methods.

[0026] On the one hand, the present application provides a computer-readable storage medium, which includes a program code. When the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any of the above-mentioned dual-chain ring distribution network carrying capacity analysis methods.

[0027] The beneficial effects of this application are as follows:

[0028] The embodiment of the present application provides a method, device and electronic device for analyzing the carrying capacity of a double-chain ring distribution network. Due to the inherent characteristics of distributed energy, independent energy storage and area load, they all have different characteristic curves at different times of each year, month and day. By statistically analyzing the characteristic data of distributed photovoltaic, distributed wind power, independent energy storage and user load in the double-chain ring distribution network area, iterative calculation is performed using a neural network model based on the characteristic data to obtain the optimal solution for the multi-element interactive carrying capacity, thereby optimizing the access of distributed new energy, energy storage and load under the premise of ensuring the safety and reliability of the distribution network, improving the multi-element interactive capabilities of source, network, load and storage, and optimizing the new energy absorption capacity, voltage qualification rate, grid loss and other grid technical indicators of the area distribution network to the greatest extent, solving the problems of low carrying capacity and poor multi-element interactive capabilities of the traditional distribution network.

[0029] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1 A flowchart of a method for analyzing the carrying capacity of a double-chain ring distribution network in an embodiment of the present application;

[0032] Figure 2 This is a structural example diagram of a double-chain ring distribution network in an embodiment of the present application;

[0033] Figure 3 It is a structural schematic diagram of a bearing capacity analysis device of a double-chain ring distribution network in an embodiment of the present application;

[0034] Figure 4 A schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0036] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0037] The following is a brief introduction to the design concept of the embodiment of the present application:

[0038] In the existing double-chain distribution network, since the installed capacity of distributed renewable energy is small and there is strong uncertainty, the access of distributed energy and independent energy storage often ignores the analysis of the carrying capacity of the regional power grid, resulting in uneven distribution of power grid currents, and frequent light and heavy loads of main transformers and lines. Therefore, how to accurately quantify the output forecast of distributed power sources, and cooperate with independent energy storage on the grid side to smooth the output fluctuation of renewable energy, increase the inertia response of renewable energy units, quickly respond to load demand, and improve the carrying capacity of local power grids has become a problem that must be solved in the power system.

[0039] In view of this, the embodiment of the present application provides a method, device and electronic device for analyzing the carrying capacity of a dual-chain ring distribution network. Since distributed energy, independent energy storage and area loads have their inherent characteristics, they have different characteristic curves at different times of the year, month and day. By statistically analyzing the characteristic data of distributed photovoltaic, distributed wind power, independent energy storage and user loads in the dual-chain ring distribution network area, iterative calculations are performed using a neural network model based on the characteristic data to obtain the optimal solution for the multi-element interactive carrying capacity, thereby optimizing the access of distributed new energy, energy storage and loads while ensuring the safety and reliability of the distribution network, improving the multi-element interactive capabilities of source, network, load and storage, and optimizing the new energy absorption capacity, voltage qualification rate, grid loss and other grid technical indicators of the area distribution network to the greatest extent, solving the problems of low carrying capacity and poor multi-element interactive capabilities of the traditional distribution network.

[0040] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0041] like Figure 1 As shown, it is a flowchart of a method for analyzing the carrying capacity of a double-chain ring distribution network provided in an embodiment of the present application, including the following steps:

[0042] S101, obtaining distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load of distribution network corresponding to the dual-chain ring distribution network;

[0043] Among them, the double-chain distribution network includes the first substation bus, the second substation bus and N switch stations, where N is an integer greater than or equal to 1; the switch station includes the first switch station bus and the second switch station bus, and the first switch station bus and the second switch station bus are connected by a connecting switch; the first substation bus, N first switch station busbars, and the second side substation bus are connected in a chain shape, and each interface is provided with a connecting switch; and the first substation bus, N second switch station busbars, and the second substation bus are connected in a chain shape, and each interface is provided with a connecting switch; distributed photovoltaic and independent energy storage are respectively connected to the first switch station bus through the connecting switch, and distributed wind power and distribution network loads are respectively connected to the second switch station bus through the connecting switch.

[0044] Optionally, the first substation bus includes a first bus section and a second bus section, and the second substation bus includes a third bus section and a fourth bus section; wherein the first bus section and the second bus section, and the third bus section and the fourth bus section are respectively connected based on a connecting switch; then, the first bus section, the N first switch station busbars, and the third bus section are connected in a chain shape, and each interface is provided with a connecting switch; and the second bus section, the N second switch station busbars, and the fourth bus section are connected in a chain shape, and each interface is provided with a connecting switch.

[0045] For example, refer to Figure 2 , is a structural example diagram of a double-chain ring distribution network. Figure 2 In the figure, it includes 20kV busbar of substation A, 20kV busbar of substation B, 20kV switch station 1, 20kV substation 2 and 20kV substation 3. Among them, 20kV busbar of substation A and 20kV busbar of substation B are set in sections, and tie switches are set between the sectioned buses; 20kV switch station 1 is connected to distributed photovoltaic 1, independent energy storage 1, distributed wind power 1 and distribution network load 1; 20kV switch station 2 is connected to distributed photovoltaic 2, independent energy storage 2, distributed wind power 2 and distribution network load 2; 20kV switch station 3 is connected to distributed photovoltaic 3, independent energy storage 3, distributed wind power 3 and distribution network load 3. In the above, each access point of the switch station is equipped with a tie switch, which can play a role of protection and isolation.

[0046] Furthermore, the operation data of the dual-chain ring distribution network is obtained, including distributed photovoltaic power data, distributed wind power data, the operation status of independent energy storage, and the load of the distribution network. Among them, since distributed photovoltaic, distributed wind power, independent energy storage and distribution network load each have their own inherent characteristics, they have different characteristic curves at different times of the year, month and day. Therefore, after obtaining the operation data of the dual-chain ring network, according to the characteristic curve of the operation data, the coordination strategy between distributed photovoltaic, distributed wind power, independent energy storage and distribution network load can be assisted in analyzing, for example, wind-solar complementary strategy, multi-element interactive carrying capacity analysis. The following is an example of multi-element interactive carrying capacity analysis.

[0047] S102, extracting features of distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage, and load respectively to obtain a variety of feature data;

[0048] In the embodiments of the present application, distributed photovoltaic power data refers to the output curve of distributed photovoltaic over time, and the statistical time may include year, month, day, hour, etc.; distributed wind power data refers to the output curve of distributed wind power over time, and the statistical time may include year, month, day, hour, etc.; the operating status of independent energy storage refers to the charging and discharging status of independent energy storage over time, and the statistical time may include year, month, day, hour, etc.; load refers to the load change curve over time, and the statistical time may include year, month, day, hour, etc.

[0049] Furthermore, feature extraction is performed on distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage, and load, including:

[0050] Based on the spatiotemporal correlation features and the Bayesian-Long Short-Term Memory (B-LSTM) neural network model, the distributed photovoltaic power data is feature extracted to obtain the distributed photovoltaic power characteristic data curve. Among them, the B-LSTM model is Bayesianized by adding a Dropout layer to enhance the generalization ability of the model.

[0051] Based on the combination model of variational mode decomposition (VMD) and convolutional neural network (CNN) and long short-term memory network (LSTM), the distributed wind power data is feature extracted to obtain the distributed wind power characteristic data curve. Among them, VMD is used to decompose the wind power sequence, CNN is used for feature extraction, and LSTM is used for time series prediction.

[0052] Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) model, the operating status of the independent energy storage is extracted to obtain the independent energy storage charge and discharge characteristic data curve.

[0053] Based on the variational mode decomposition (VMD) and convolutional neural network (CNN) and long short-term memory network (LSTM) models, the attention mechanism is introduced to extract features of distribution network load data and obtain the load characteristic data curve.

[0054] S103, inputting a variety of characteristic data into a neural network model, and calculating the carrying capacity of the double-chain ring distribution network based on the neural network model.

[0055] In the embodiment of the present application, the neural network model is a three-layer neural network, each layer of the neural network has a corresponding weight matrix and a bias vector, and the carrying capacity of the double-chain ring distribution network is calculated based on the neural network model, including:

[0056] After adding the product of the feature vectors corresponding to the multiple feature data and the first layer weight matrix of the neural network model to the first bias vector, the first layer calculation result is obtained based on the activation function calculation;

[0057] After adding the product of the first layer calculation result and the second layer weight matrix of the neural network model to the second bias vector, the second layer calculation result is obtained based on the activation function calculation;

[0058] After adding the product of the second layer calculation result and the third layer weight matrix of the neural network model to the third bias vector, the carrying capacity of the double-chain ring distribution network is obtained based on the activation function calculation.

[0059] Optionally, the model formula of the neural network model is:

[0060] C y =σ(W3·σ(W2·σ(W1·F+b1)+b2)+b3) (1)

[0061] In formula (1), C y is the carrying capacity predicted by the neural network model, σ is the activation function, F is the feature vector corresponding to the various feature data, W1, W2, W3 are the first layer weight matrix, the second layer weight matrix, and the third layer weight matrix of the neural network model, respectively, and b1, b2, b3 are the first bias vector, the second bias vector, and the third bias vector of the neural network model, respectively. Among them, the dimensions of W1, W2, W3 and b1, b2, b3 correspond to the input and output of the neural network level in which they are located; F = [F1, F2, F3, F4], F1 is the output power of distributed photovoltaics, F2 is the output power of distributed wind power, F3 is the operating status of independent energy storage, and F4 is the load of the distribution network.

[0062] Optionally, the activation function in formula (1) is a ReLU function or a sigmoid function, which is used to increase the nonlinear characteristics of the network so that the network can learn and perform more complex tasks.

[0063] It should be noted that before inputting various feature data into the neural network model, it also includes:

[0064] Based on the preset loss function, the preset network model is trained. After the training is completed, the neural network model is obtained. The preset loss function is:

[0065]

[0066] In formula (2), C y,i is the carrying capacity predicted by the neural network model for the i-th training sample among n training samples, where n is an integer greater than or equal to 1, and C actual,i is the actual carrying capacity corresponding to the i-th training sample. The calculation process of the carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain distribution network. The calculation of the actual carrying capacity can be completed with the help of power system simulation software.

[0067] Optionally, the actual bearing capacity can be calculated as:

[0068]

[0069] In formula (3), V d is the bus voltage deviation rate, V dmax is the maximum allowable value of bus voltage deviation rate; P loss is the power loss of the line, P rated is the rated power of the distribution network system, P lmax is the maximum permissible ratio of line power loss to the rated power of the distribution network system; f d is the frequency deviation rate of the distribution network system, f dmax is the maximum permissible value of the frequency deviation rate of the distribution network system; L f is the load factor, L fmax is the maximum allowable value of the load factor, where:

[0070]

[0071] P loss =I 2 ×R (5)

[0072]

[0073] In formula (4), V actual is the actual voltage, V e is the rated voltage.

[0074] In formula (5), I is the line current and R is the line equivalent resistance.

[0075] In formula (6), P actual is the actual output power of the distribution network system.

[0076] When distributed photovoltaic, distributed wind power or distribution network load is suddenly connected, the grid frequency will also be disturbed. If the connected load is too large, or the output of distributed photovoltaic or distributed wind power suddenly increases, it will affect the grid frequency, resulting in grid frequency deviation. If the grid frequency fluctuates too much, there will be a risk of grid decoupling. Therefore, the above actual carrying capacity calculation process also takes into account the system frequency characteristics.

[0077] Based on the above formulas (2) to (7), the training process of the preset network model can be constrained to make the model converge and obtain the neural network model after training. During the training process, the calculation of the actual carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network, so that the final prediction result of the neural network model also takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate, so that the calculation result of the carrying capacity can ensure the safe operation of the distribution network system.

[0078] In a possible embodiment, after obtaining the carrying capacity of the dual-chain ring distribution network through the trained neural network model, the carrying capacity is further corrected, including:

[0079] The bearing capacity calculation results are imported into the Electrical Transient Analysis Program (ETAP) model, and the simulation verification is performed through the ETAP model. If the verification result passes, it is directly output. If the verification result fails, the neural network model parameters are adjusted and the iterative calculation is continued until the ETAP model verification passes.

[0080] Among them, the ETAP model is a power system simulation model, and its simulation parameters are all taken from the actual operation data of the above-mentioned double-chain ring distribution network, so the calculation results are reliable.

[0081] After determining the carrying capacity of the dual-chain ring distribution network, based on this carrying capacity, the coordinated strategy adjustment between distributed photovoltaic, distributed wind power, independent energy storage and distribution network load in the dual-chain ring distribution network can be carried out. This will help to achieve balanced and efficient use of electricity, avoid energy waste, and achieve a flexible balance between power generation, power consumption and energy storage under the premise of power distribution safety.

[0082] Based on the above-mentioned carrying capacity analysis method of the double-chain distribution network, since distributed energy, independent energy storage and area load have their inherent characteristics, they have different characteristic curves at different times of the year, month and day. By statistically analyzing the characteristic data of distributed photovoltaic, distributed wind power, independent energy storage and user load in the double-chain distribution network area, iterative calculation is performed using a neural network model based on the characteristic data to obtain the optimal solution for the multi-element interactive carrying capacity, thereby optimizing the access of distributed new energy, energy storage and loads while ensuring the safety and reliability of the distribution network, improving the multi-element interactive capabilities of source, network, load and storage, and maximizing the optimization of the new energy absorption capacity, voltage qualification rate, grid loss and other grid technical indicators of the area distribution network, solving the problems of low carrying capacity and poor multi-element interactive capabilities of the traditional distribution network.

[0083] Based on the same inventive concept, the embodiment of the present application also provides a bearing capacity analysis device for a double-chain ring distribution network, such as Figure 3 As shown, the device comprises:

[0084] The acquisition module 301 is used to acquire the distributed photovoltaic power data, distributed wind power data, the operating status of the independent energy storage and the load of the distribution network corresponding to the double-chain ring distribution network;

[0085] The feature extraction module 302 is used to extract features of distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load respectively to obtain multiple feature data;

[0086] The calculation module 303 is used to input a variety of characteristic data into the neural network model, and calculate the carrying capacity of the double-chain ring distribution network based on the neural network model; wherein the calculation process of the carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network.

[0087] In a possible embodiment, a double-chain-ring distribution network includes: a first substation bus, a second substation bus and N switch stations, where N is an integer greater than or equal to 1; the switch station includes a first switch station bus and a second switch station bus, and the first switch station bus and the second switch station bus are connected by a connecting switch; the first substation bus, N first switch station busbars, and the second side substation bus are connected in a chain shape, and each interface is provided with a connecting switch; and the first substation bus, N second switch station busbars, and the second substation bus are connected in a chain shape, and each interface is provided with a connecting switch; distributed photovoltaic and independent energy storage are respectively connected to the first switch station bus through the connecting switch, and distributed wind power and distribution network loads are respectively connected to the second switch station bus through the connecting switch.

[0088] In a possible embodiment, the first substation bus includes a first bus section and a second bus section, and the second substation bus includes a third bus section and a fourth bus section; wherein the first bus section and the second bus section, and the third bus section and the fourth bus section are connected based on a connecting switch, respectively; then, the first bus section, N first switch station busbars, and the third bus section are connected in a chain shape, and each interface is provided with a connecting switch; and the second bus section, N second switch station busbars, and the fourth bus section are connected in a chain shape, and each interface is provided with a connecting switch.

[0089] In a possible embodiment, the calculation module 303 is used to: calculate the first layer calculation result based on the activation function after adding the product of the feature vector corresponding to multiple feature data and the first layer weight matrix of the neural network model plus the first bias vector; calculate the second layer calculation result based on the activation function after adding the product of the first layer calculation result and the second layer weight matrix of the neural network model plus the second bias vector; calculate the carrying capacity of the dual-chain distribution network based on the activation function after adding the product of the second layer calculation result and the third layer weight matrix of the neural network model plus the third bias vector.

[0090] In a possible embodiment, the model formula of the neural network model is:

[0091] C y =σ(W3·σ(W2·σ(W1·F+b1)+b2)+b3);

[0092] Among them, C y is the carrying capacity predicted by the neural network model, σ is the activation function, F is the feature vector corresponding to the multiple feature data, W1, W2, W3 are the first layer weight matrix, the second layer weight matrix, and the third layer weight matrix of the neural network model, b1, b2, b3 are the first bias vector, the second bias vector, and the third bias vector of the neural network model, respectively.

[0093] In a possible embodiment, the device further includes:

[0094] The training module is used to train the preset network model based on the preset loss function to obtain a neural network model. The preset loss function is:

[0095]

[0096] Among them, C y,i is the carrying capacity predicted by the neural network model for the i-th training sample among n training samples, where n is an integer greater than or equal to 1, and C actual,iis the actual carrying capacity corresponding to the i-th training sample. The calculation process of the actual carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate.

[0097] In a possible embodiment, the calculation formula of the actual bearing capacity is:

[0098]

[0099] Among them, V d is the bus voltage deviation rate, V dmax is the maximum allowable value of bus voltage deviation rate; P loss is the power loss of the line, P rated is the rated power of the distribution network system, P lmax is the maximum permissible ratio of line power loss to the rated power of the distribution network system; f d is the frequency deviation rate of the distribution network system, f dmax is the maximum permissible value of the frequency deviation rate of the distribution network system; L f is the load factor, L fmax is the maximum allowable value of the load rate.

[0100] The technical effect of the above double-chain-ring distribution network load-bearing capacity analysis device is described in the method section and will not be repeated here.

[0101] In some possible implementations, the dual-chain ring distribution network capacity analysis device according to the present application may include at least a processor and a memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the dual-chain ring distribution network capacity analysis method according to various exemplary embodiments of the present application described in this specification. For example, the processor may execute the following steps: Figure 1 Follow the steps shown in .

[0102] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application, and the electronic device can realize the function of the carrying capacity analysis device of the aforementioned double-chain ring distribution network, referring to Figure 4 , the electronic device comprises:

[0103] At least one processor 401, and a memory 402 connected to the at least one processor 401. The specific connection medium between the processor 401 and the memory 402 is not limited in the embodiment of the present application. Figure 4 In the example, the processor 401 and the memory 402 are connected via the bus 400. The bus 400 is Figure 4 The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 400 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 401 can also be called a controller, and there is no limitation on the name.

[0104] In the embodiment of the present application, the memory 402 stores instructions that can be executed by at least one processor 401. The at least one processor 401 can execute the carrying capacity analysis method of the dual-chain ring distribution network discussed above by executing the instructions stored in the memory 402. The processor 401 can implement Figure 4 The functions of each module in the device shown.

[0105] Among them, the processor 401 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 402 and calling the data stored in the memory 402, the various functions of the device and process data, the device can be monitored as a whole.

[0106] In one possible design, the processor 401 may include one or more processing units, and the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0107] The processor 401 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method for analyzing the carrying capacity of a dual-chain ring distribution network disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in a processor.

[0108] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 402 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 402 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0109] By programming the processor 401, the code corresponding to the carrying capacity analysis method of the dual-chain ring distribution network described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The steps of the method for analyzing the carrying capacity of a dual-chain ring distribution network in the embodiment shown are as follows: How to design and program the processor 401 is a technology well known to those skilled in the art and will not be described in detail here.

[0110] Based on the same inventive concept, an embodiment of the present application further provides a storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes the carrying capacity analysis method of the dual-chain ring distribution network discussed above.

[0111] In some possible implementations, various aspects of the dual-chain ring distribution network capacity analysis method provided in the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the dual-chain ring distribution network capacity analysis method according to various exemplary embodiments of the present application described above in this specification.

[0112] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for analyzing the carrying capacity of a double-chain ring distribution network, characterized in that: The method comprises: Obtain the distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load of the distribution network corresponding to the dual-chain ring distribution network; Extracting features of the distributed photovoltaic power data, the distributed wind power data, the operating status of the independent energy storage, and the load respectively to obtain a variety of feature data; The multiple characteristic data are input into a neural network model, and the carrying capacity of the double-chain ring distribution network is calculated based on the neural network model; wherein the carrying capacity calculation process takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network.

2. The method according to claim 1, characterized in that: The double-chain-ring distribution network includes: a first substation bus, a second substation bus and N switch stations, where N is an integer greater than or equal to 1; the switch station includes a first switch station bus and a second switch station bus, and the first switch station bus and the second switch station bus are connected via a tie switch; the first substation bus, N first switch station busbars, and the second side substation bus are connected in a chain shape, and each interface is provided with a tie switch; and the first substation bus, N second switch station busbars, and the second substation bus are connected in a chain shape, and each interface is provided with a tie switch; distributed photovoltaic and independent energy storage are respectively connected to the first switch station bus through the tie switch, and distributed wind power and distribution network loads are respectively connected to the second switch station bus through the tie switch.

3. The method according to claim 2, characterized in that: The first substation bus includes a first bus section and a second bus section, and the second substation bus includes a third bus section and a fourth bus section; wherein, the first bus section and the second bus section, and the third bus section and the fourth bus section are connected based on a tie switch, respectively; then, the first bus section, N first switch station bus bars, and the third bus section are connected in a chain shape, and each interface is provided with a tie switch; and the second bus section, N second switch station bus bars, and the fourth bus section are connected in a chain shape, and each interface is provided with a tie switch.

4. The method according to claim 1, characterized in that: The calculating the carrying capacity of the double-chain ring distribution network based on the neural network model includes: After adding the first bias vector to the product of the feature vectors corresponding to the multiple feature data and the first layer weight matrix of the neural network model, a first layer calculation result is obtained based on the activation function calculation; After adding the second bias vector to the product of the first layer calculation result and the second layer weight matrix of the neural network model, the second layer calculation result is obtained based on the activation function calculation; After adding the product of the second layer calculation result and the third layer weight matrix of the neural network model to the third bias vector, the carrying capacity of the double-chain ring distribution network is obtained based on the activation function calculation.

5. The method according to claim 4, characterized in that: The model formula of the neural network model is: y =σ(W3·σ(W2·σ(W1·F+b1)+b2)+b3); Among them, C y is the carrying capacity predicted by the neural network model, σ is the activation function, F is the feature vector corresponding to the multiple feature data, W1, W2, W3 are the first layer weight matrix, the second layer weight matrix, and the third layer weight matrix of the neural network model, b1, b2, b3 are the first bias vector, the second bias vector, and the third bias vector of the neural network model, respectively.

6. The method according to claim 1, characterized in that: Before inputting the multiple feature data into the neural network model, the method further includes: Based on the preset loss function, the preset network model is trained to obtain the neural network model, and the preset loss function is: Among them, C y,i is the carrying capacity predicted by the neural network model for the i-th training sample among n training samples, where n is an integer greater than or equal to 1, and C actual,i is the actual carrying capacity corresponding to the i-th training sample, and the calculation process of the actual carrying capacity takes into account the bus voltage deviation rate, the line power loss, the system frequency deviation rate and the load rate.

7. The method according to claim 6, characterized in that The calculation formula of the actual bearing capacity is: Among them, V d is the bus voltage deviation rate, V dmax is the maximum allowable value of bus voltage deviation rate; P loss is the line power loss, P rated is the rated power of the distribution network system, P lmax is the maximum permissible ratio of line power loss to the rated power of the distribution network system; f d is the frequency deviation rate of the distribution network system, f dmax is the maximum permissible value of the frequency deviation rate of the distribution network system; L f is the load factor, L fmax is the maximum allowable value of the load rate.

8. A bearing capacity analysis device for a double-chain ring distribution network, characterized in that: The device comprises: An acquisition module is used to obtain distributed photovoltaic power data, distributed wind power data, operating status of independent energy storage and load of distribution network corresponding to the dual-chain ring distribution network; A feature extraction module is used to extract features of the distributed photovoltaic power data, the distributed wind power data, the operating status of the independent energy storage and the load respectively to obtain a variety of feature data; A calculation module is used to input the multiple characteristic data into a neural network model, and calculate the carrying capacity of the double-chain ring distribution network based on the neural network model; wherein the calculation process of the carrying capacity takes into account the bus voltage deviation rate, line power loss, system frequency deviation rate and load rate of the double-chain ring distribution network.

9. An electronic device, characterized in that: The device comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes any one of the methods in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium comprises a program code, and when the storage medium is run on an electronic device, the program code is used to enable the electronic device to execute any one of the methods described in claims 1 to 7.

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