Power distribution network risk assessment method and system considering load-photovoltaic data resolution
Patent Information
- Application Number
- CN202611082873.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-21
- Publication Date
- 2026-09-25
AI Technical Summary
然而，为了降低通信、存储和数据处理成本，电网公司总是将高分辨的负荷和光伏数据下采样到低分辨率进行存储，这意味着从电网公司只能获得和使用低分辨率的数据
本发明利用生成对抗网络对低分辨率数据进行超分辨处理，有效还原了因下采样而丢失的负荷与光伏出力的高频波动特征，解决了传统方法因数据平滑导致的风险评估偏差问题；通过引入节点电压越限和线路潮流越限的双重成本函数，能够从经济性和安全性两个维度全面反映配电网在高比例可再生能源接入下的运行风险。
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Figure CN122819922A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of distribution network risk assessment technology, specifically relating to a distribution network risk assessment method and system that takes into account load-photovoltaic data resolution. Background Technology
[0002] Distribution network risk assessment aims to quantitatively evaluate the risk level of the distribution system, providing a basis for decision-making in further system operation and planning. However, with the vigorous development of renewable energy, more and more photovoltaic systems are being connected to the distribution system, further increasing the uncertainty of the distribution system. This makes it difficult to accurately assess the risk level of the distribution system using traditional risk assessment methods to guide further system dispatch.
[0003] To address this issue, a series of methods have been proposed in the prior art for accurately assessing the risk level of power distribution systems. These methods typically rely on the assumption that high-quality load and photovoltaic (PV) data is available and usable from the grid company. However, to reduce communication, storage, and data processing costs, grid companies invariably downsample high-resolution load and PV data to low resolution for storage. This means that only low-resolution data can be obtained and used from the grid company. Furthermore, due to the smoothing effect of the downsampling process, the volatility of load and PV data is lost, resulting in existing risk assessment methods still failing to accurately assess the system's risk level.
[0004] Therefore, this invention provides a distribution network risk assessment method that takes into account the resolution of load-photovoltaic data. By fully considering the impact of the resolution of load and photovoltaic data on the risk assessment of the distribution network system, it helps to accurately assess the system risk and provides guidance and decision-making basis for the further operation and planning of the distribution network system, thereby improving the reliability of the system. Summary of the Invention
[0005] The purpose of this invention is to overcome the existing deficiencies and provide a distribution network risk assessment method and system that takes into account load-photovoltaic data resolution.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: The primary objective of this invention is to provide a distribution network risk assessment method that takes into account load-photovoltaic data resolution, comprising the following steps: (1) Obtain the predicted low-resolution load-photovoltaic data of the distribution network, and use the trained generative adversarial network model to perform super-resolution processing on the low-resolution data to generate the predicted high-resolution load-photovoltaic data. (2) Input the predicted high-resolution load-photovoltaic data into the risk assessment module, and calculate the system severity caused by node voltage overrun and line power flow overrun by solving the objective function that includes node voltage overrun cost and line power flow overrun cost; (3) Based on the calculated system severity and the set voltage over-limit and power flow over-limit indicators, the risk level of the distribution network system is obtained.
[0007] Furthermore, the training process of the generative adversarial network model specifically includes: Generative model training: Obtain existing low-resolution load-photovoltaic data, and use a one-dimensional convolutional layer with batch normalization and linear rectified activation function to capture the high-frequency features of the input low-resolution data; alleviate the gradient descent problem during training by inserting residual blocks; capture the correlation between low-resolution data and corresponding high-resolution data by using upsampling and downsampling blocks; finally, use a one-dimensional convolutional layer to generate high-resolution data. Adversarial model training: Four one-dimensional convolutional layers with batch normalization and modified linear rectified activation functions are used to capture and compare the high-frequency features of the original and generated high-resolution data. The kernel size of the convolutional layers increases sequentially from 4 to 32. Finally, a fully connected layer with a sigmoid activation function is used to determine whether the generated high-resolution data is close to the original high-resolution data. Parameter update: The model parameters are updated using a weighted sum of mean squared error, adversarial loss, and feature loss as the loss function of the generative model, and the corresponding adversarial model loss function.
[0008] Furthermore, the loss functions of the generative model and the adversarial model are respectively expressed as:
[0009]
[0010] in, These are generative models and adversarial models, respectively. These are the parameters for the generative model and the adversarial model, respectively. , , , In the adversarial model, the first The output of a one-dimensional convolutional layer It is the total number of one-dimensional convolutional layers in the adversarial model. It is a weighting factor that is always constant. They represent the obtained Low-resolution and high-resolution data, This indicates that the data belongs to either load or photovoltaic data.
[0011] Furthermore, in step (2), the objective function of the risk assessment module is expressed as:
[0012] in, These are the costs of node voltage exceeding limits and the costs of line power flow exceeding limits. These are the power generation cost coefficients, Indicates busbar Active power injection from the generator. Represents a node The square of the voltage amplitude, They represent the busbars respectively. The upper and lower limits of the square of the voltage amplitude. Indicates a branch The positive trend.
[0013] Furthermore, the constraints of the risk assessment module are as follows:
[0014]
[0015]
[0016]
[0017]
[0018]
[0019]
[0020] in, These respectively represent the sets of generator bus, all system bus, all system branches, and photovoltaic bus. They represent the busbars respectively. Active power injection from generators, photovoltaic systems, and loads. They represent the busbars respectively. Reactive power injection from generators, photovoltaic systems, and loads. Representing branch roads The trend of meritorious and ineffective actions Representing branch roads Resistance and reactance, Indicates a branch The square of the current amplitude, Represents a node The square of the voltage amplitude, They represent the busbars respectively. The upper and lower limits of the generator's active power. They represent the busbars respectively. The upper and lower limits of the generator's reactive power. They represent the busbars respectively. The upper and lower limits of the square of the voltage amplitude. Indicates a branch The upper limit of the merit trend, Indicates busbar Place and The allowable ratio between them.
[0021] Furthermore, in step (2), the system severity includes time. The severity and timing of voltage exceeding limits on the system The severity of the impact of flow overflow on the system is expressed as follows:
[0022]
[0023] in, express Real-time system operation status express The severity of the impact of voltage exceeding the limit on the system at any given moment. express The severity of the impact of exceeding the time limit on the system.
[0024] Furthermore, in step (3), the risk level of the distribution network system is assessed using an economically based risk indicator, calculated as follows:
[0025] in, Indicates the risk level of the system, when This formula is used to assess the risk level of system voltage exceeding limits. This formula is used to assess the risk level of system power flow exceeding limits. express The probability of the system's operational status at any given time.
[0026] Another object of the present invention is to provide a distribution network risk assessment system that takes into account load-photovoltaic data resolution, comprising: The data acquisition module is used to acquire existing low-resolution load-photovoltaic data and corresponding high-resolution data; The model training module is used to construct and train a generative adversarial network model using existing low-resolution load-photovoltaic data and corresponding high-resolution data as the training set. The generative adversarial network model includes a generative model and an adversarial model. The generative model is used to map low-resolution data to high-resolution data, and the adversarial model is used to determine the degree of similarity between the generated data and the original high-resolution data. Super-resolution generation module: Used to input the predicted low-resolution load-photovoltaic data into the trained generative adversarial network model and output the predicted high-resolution load-photovoltaic data; Risk assessment module: Used to receive the predicted high-resolution load-photovoltaic data, calculate the system severity caused by node voltage overruns and line power flow overruns in the distribution network system, and output the risk level of the distribution network system by combining the set voltage overrun index and power flow overrun index.
[0027] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distribution network risk assessment method considering load-photovoltaic data resolution provided by the first object of the present invention.
[0028] Another object of the present invention is to provide a server comprising at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the processor to cause the at least one processor to perform the distribution network risk assessment method considering load-photovoltaic data resolution provided by the first object of the present invention.
[0029] In combination with the above technical solutions, the beneficial effects of the present invention compared with the prior art are as follows: This invention utilizes generative adversarial networks to perform super-resolution processing on low-resolution data, effectively restoring the high-frequency fluctuation characteristics of load and photovoltaic output lost due to downsampling, and solving the risk assessment bias problem caused by data smoothing in traditional methods. By introducing dual cost functions for node voltage overruns and line power flow overruns, it can comprehensively reflect the operational risks of the distribution network under high-proportion renewable energy access from both economic and safety dimensions.
[0030] This invention can effectively consider the impact of load-photovoltaic data resolution on distribution network risk assessment, thereby enabling accurate assessment of the risk level of the distribution network system, providing guidance and decision-making basis for the operation and planning of the distribution network system, and thus improving the reliability of the system. Attached Figure Description
[0031] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a distribution network risk assessment method that takes into account load-photovoltaic data resolution, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the super-resolution generative adversarial network model provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the structural principle of the distribution network risk assessment system that takes into account the load-photovoltaic data resolution provided in the embodiment of the present invention. Detailed Implementation
[0032] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0033] Example 1:
[0034] like Figure 1 The illustration shows an embodiment of the distribution network risk assessment method considering load-photovoltaic data resolution provided by the present invention, which specifically includes the following steps: S1: Obtain the predicted low-resolution load-photovoltaic data of the distribution network, and use the trained generative adversarial network model to perform super-resolution processing on the low-resolution data to generate the predicted high-resolution load-photovoltaic data. S2: Input the predicted high-resolution load-photovoltaic data into the risk assessment module, and calculate the system severity caused by node voltage overrun and line power flow overrun by solving the objective function that includes node voltage overrun cost and line power flow overrun cost; S3: Based on the calculated system severity and the set voltage and power flow exceedance indices, the risk level of the distribution network system is obtained.
[0035] Specifically, in this embodiment of the invention, a generative adversarial network (GAN) model is trained using existing low-resolution load-PV data and corresponding high-resolution data as a training set. Then, the predicted low-resolution load-PV data is input into the trained GAN model to obtain the predicted high-resolution load-PV data. Next, the predicted high-resolution load-PV data is input into a risk assessment module to calculate the system severity caused by node voltage exceedances and line power flow exceedances in the distribution network system. Finally, based on the set voltage exceedance and power flow exceedance indicators, the system risk level is obtained for assessing the distribution network risk.
[0036] (1) Based on the existing low-resolution load-photovoltaic data and the corresponding high-resolution data, the generative adversarial network model is trained. The specific steps are as follows: The super-resolution generative adversarial network model used in this invention is as follows: Figure 2 As shown.
[0037] The generative model first uses a one-dimensional convolutional layer with batch normalization and linear rectified activation functions to capture high-frequency features of the input low-resolution data. Then, a residual block is inserted to alleviate gradient descent during training. Subsequently, two upsampling and downsampling blocks are used to capture the correlation between the low-resolution data and the corresponding high-resolution data. Finally, a one-dimensional convolutional layer is used to generate high-resolution data.
[0038] For the adversarial model, four one-dimensional convolutional layers with batch normalization and modified linear rectified activation functions are used to capture and compare high-frequency features of the original and generated high-resolution data. The kernel sizes of these four convolutional layers increase sequentially from 4 to 32. Finally, a fully connected layer with a sigmoid activation function is used to determine whether the generated high-resolution data is close to the original high-resolution data.
[0039] To update the parameters of the super-resolution generative adversarial network model, this invention employs the following loss function design: [Define...] These are generative models and adversarial models, respectively. These are the parameters for the generative model and the adversarial model, respectively.
[0040] The generative model uses a weighted sum of mean squared error, adversarial loss, and feature loss as the loss function, as shown in equation (1). (1) in, , , These represent the mean squared error loss, adversarial loss, and feature loss, respectively. , , , This represents the output of the th one-dimensional convolutional layer in the adversarial model, specifically numbered as follows: Figure 2 The figure shows the number of one-dimensional convolutional layers in the adversarial model. In this invention, the index represents the one-dimensional convolutional layer. , It is a weighting factor that is always constant. They represent the obtained Low-resolution and high-resolution data, Used to distinguish whether the data belongs to load or photovoltaic, when , ,when , .
[0041] The loss function used in the adversarial model is shown in equation (2). (2) (2) Input the predicted low-resolution load-photovoltaic data into the trained generative adversarial network model to obtain the predicted high-resolution load-photovoltaic data. The specific steps are as follows: This step can be described by equation (3). (3) in, It is a pre-trained generative model. It is a predicted low-resolution load-photovoltaic data. It is the predicted high-resolution load-photovoltaic data. Used to distinguish whether the data belongs to load or photovoltaic, when , ,when , .
[0042] (3) Input the predicted high-resolution load-photovoltaic data into the risk assessment module to calculate the system severity caused by node voltage overruns and line power flow overruns in the distribution network system. The specific steps are as follows: The objective function of the risk assessment module is: (4) in, These are the costs of node voltage exceeding limits and the costs of line power flow exceeding limits. Units and same, Indicates obtaining and A function that finds the maximum value between [a certain range].
[0043] The constraints for the risk assessment module are: (5) (6) (7) (8) (9) (10) (11) in, These are the power generation cost coefficients, These respectively represent the sets of generator bus, all system bus, all system branches, and photovoltaic bus. They represent the busbars respectively. Active power injection from generators, photovoltaic systems, and loads. They represent the busbars respectively. Reactive power injection from generators, photovoltaic systems, and loads. Representing branch roads The trend of meritorious and ineffective actions Representing branch roads Resistance and reactance, Indicates a branch The square of the current amplitude, Represents a node The square of the voltage amplitude, They represent the busbars respectively. The upper and lower limits of the generator's active power. They represent the busbars respectively. The upper and lower limits of the generator's reactive power. They represent the busbars respectively. The upper and lower limits of the square of the voltage amplitude. Indicates a branch The upper limit of the trend of merit, Indicates busbar Place and The allowable ratio between them.
[0044] By solving the optimization problem with objective function (4) and constraints (5) to (11), we can... The severity of the system caused by voltage exceeding the limit at a given time and power flow exceeding the limit in a branch is expressed by equations (12) and (13), respectively.
[0045] (12) (13) in, express The operational status of the timekeeping system, including wait, express The severity of the impact of voltage exceeding the limit on the system at any given moment. express The severity of the impact of exceeding the time limit on the system.
[0046] (4) Based on the set voltage over-limit and power flow over-limit indicators, the system risk level is obtained and used to assess the risk of the distribution network. The specific steps are as follows: The purpose of risk indicators is to quantitatively assess the risk level of a system. After obtaining severity functions (12) and (13), an economically based risk indicator (14) is defined to evaluate the risk level of the power distribution system.
[0047] (14) in, Y represents the risk level of the system, where V represents the voltage or branch power flow PF. When, equation (14) assesses the risk level of system voltage exceeding the limit, when At that time, equation (14) assesses the risk level of system power flow exceeding the limit. express The probability of the system's operational status at any given time. The definition is the same as that in equations (12) and (13), and will not be repeated here.
[0048] Example 2:
[0049] like Figure 3 As shown in the figure, the present invention provides a distribution network risk assessment system that takes into account load-photovoltaic data resolution, including: The data acquisition module is used to acquire existing low-resolution load-photovoltaic data and corresponding high-resolution data; The model training module is used to construct and train a generative adversarial network model using existing low-resolution load-photovoltaic data and corresponding high-resolution data as the training set. The generative adversarial network model includes a generative model and an adversarial model. The generative model is used to map low-resolution data to high-resolution data, and the adversarial model is used to determine the degree of similarity between the generated data and the original high-resolution data. Super-resolution generation module: Used to input the predicted low-resolution load-photovoltaic data into the trained generative adversarial network model and output the predicted high-resolution load-photovoltaic data; Risk assessment module: Used to receive the predicted high-resolution load-photovoltaic data, calculate the system severity caused by node voltage overruns and line power flow overruns in the distribution network system, and output the risk level of the distribution network system by combining the set voltage overrun index and power flow overrun index.
[0050] Example 3:
[0051] The present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the distribution network risk assessment method considering load-photovoltaic data resolution provided by the first objective of the present invention.
[0052] Example 4:
[0053] The present invention provides a server including at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the processor to cause the at least one processor to perform the distribution network risk assessment method considering load-photovoltaic data resolution provided by the first objective of the present invention.
[0054] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in the present invention, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.
[0055] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0056] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A distribution network risk assessment method considering load-photovoltaic data resolution, characterized in that, The method includes the following steps: (1) Obtain the predicted low-resolution load-photovoltaic data of the distribution network, and use the trained generative adversarial network model to perform super-resolution processing on the low-resolution data to generate the predicted high-resolution load-photovoltaic data. (2) Input the predicted high-resolution load-photovoltaic data into the risk assessment module, and calculate the system severity caused by node voltage overrun and line power flow overrun by solving the objective function that includes node voltage overrun cost and line power flow overrun cost; (3) Based on the calculated system severity and the set voltage over-limit and power flow over-limit indicators, the risk level of the distribution network system is obtained.
2. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 1, characterized in that, The training process of the generative adversarial network model specifically includes: Generative model training: Obtain existing low-resolution load-photovoltaic data, and use a one-dimensional convolutional layer with batch normalization and linear rectified activation function to capture the high-frequency features of the input low-resolution data; alleviate the gradient descent problem during training by inserting residual blocks; capture the correlation between low-resolution data and corresponding high-resolution data by using upsampling and downsampling blocks; finally, use a one-dimensional convolutional layer to generate high-resolution data. Adversarial model training: Four one-dimensional convolutional layers with batch normalization and modified linear rectified activation functions are used to capture and compare the high-frequency features of the original and generated high-resolution data. Finally, a fully connected layer with a sigmoid activation function is used to determine whether the generated high-resolution data is close to the original high-resolution data. Parameter update: The model parameters are updated using a weighted sum of mean squared error, adversarial loss, and feature loss as the loss function of the generative model, and the corresponding adversarial model loss function.
3. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 2, characterized in that, The loss functions of the generative model and the adversarial model are expressed as follows: in, These are generative models and adversarial models, respectively. These are the parameters for the generative model and the adversarial model, respectively. , , , In the adversarial model, the first The output of a one-dimensional convolutional layer It is the total number of one-dimensional convolutional layers in the adversarial model. It is a weighting factor that is always constant. They represent the obtained Low-resolution and high-resolution data, This indicates that the data belongs to either load or photovoltaic data.
4. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 1, characterized in that, In step (2), the objective function of the risk assessment module is expressed as: in, These are the costs of node voltage exceeding limits and the costs of line power flow exceeding limits. These are the power generation cost coefficients, Indicates busbar Active power injection from the generator. Represents a node The square of the voltage amplitude, They represent the busbars respectively. The upper and lower limits of the square of the voltage amplitude. Indicates a branch The positive trend.
5. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 4, characterized in that, The constraints of the risk assessment module are as follows: in, These respectively represent the sets of generator bus, all system bus, all system branches, and photovoltaic bus. They represent the busbars respectively. Active power injection from generators, photovoltaic systems, and loads. They represent the busbars respectively. Reactive power injection from generators, photovoltaic systems, and loads. Representing branch roads The trend of meritorious and ineffective actions Representing branch roads Resistance and reactance, Indicates a branch The square of the current amplitude, Represents a node The square of the voltage amplitude, They represent the busbars respectively. The upper and lower limits of the generator's active power. They represent the busbars respectively. The upper and lower limits of the generator's reactive power. They represent the busbars respectively. The upper and lower limits of the square of the voltage amplitude. Indicates a branch The upper limit of the trend of merit, Indicates busbar Place and The allowable ratio between them.
6. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 5, characterized in that, In step (2), the system severity includes time. The severity and timing of voltage exceeding limits on the system The severity of the impact of flow overflow on the system is expressed as follows: in, express Real-time system operation status, express The severity of the impact of voltage exceeding the limit on the system at any given moment. express The severity of the impact of exceeding the time limit on the system.
7. The distribution network risk assessment method considering load-photovoltaic data resolution according to claim 6, characterized in that, In step (3), the risk level of the distribution network system is assessed using economic risk indicators, calculated as follows: in, Indicates the risk level of the system, when This formula is used to assess the risk level of system voltage exceeding limits. This formula is used to assess the risk level of system power flow exceeding limits. express The probability of the system's operational status at any given time.
8. A distribution network risk assessment system considering load-photovoltaic data resolution, characterized in that, The system includes: The data acquisition module is used to acquire existing low-resolution load-photovoltaic data and corresponding high-resolution data; The model training module is used to construct and train a generative adversarial network model using existing low-resolution load-photovoltaic data and corresponding high-resolution data as the training set. The generative adversarial network model includes a generative model and an adversarial model. The generative model is used to map low-resolution data to high-resolution data, and the adversarial model is used to determine the degree of similarity between the generated data and the original high-resolution data. Super-resolution generation module: Used to input the predicted low-resolution load-photovoltaic data into the trained generative adversarial network model and output the predicted high-resolution load-photovoltaic data; Risk assessment module: Used to receive the predicted high-resolution load-photovoltaic data, calculate the system severity caused by node voltage overruns and line power flow overruns in the distribution network system, and output the risk level of the distribution network system by combining the set voltage overrun index and power flow overrun index.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distribution network risk assessment method that takes into account the load-photovoltaic data resolution as described in any one of claims 1 to 7.
10. A server, characterized in that: The device includes at least one processor and a memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the processor to cause the at least one processor to perform the distribution network risk assessment method taking into account load-photovoltaic data resolution as described in any one of claims 1 to 7.