Multi-strategy hybrid photovoltaic maximum power point tracking control method

Abstract

The invention discloses a multi-strategy hybrid photovoltaic maximum power point tracking control method, which belongs to the technical field of photovoltaic power generation intelligent regulation and control, and comprises the following steps of: acquiring real-time data of a photovoltaic array, searching an initial maximum power point of the photovoltaic array by utilizing a heuristic optimization algorithm, and in the searching process, determining the maximum power point of the photovoltaic array; predicting the output power of each candidate solution by using a photovoltaic array output prediction model and taking the output power as fitness; adjusting the duty ratio of the photovoltaic array control circuit based on the obtained initial maximum power point, performing disturbance control by using a variable step size disturbance observation method, and outputting the optimal duty ratio of the photovoltaic array control circuit; and the output power of the photovoltaic array is monitored in real time, if the output power exceeds preset fluctuation, the initial maximum power point is searched again, and otherwise, disturbance control is carried out on the photovoltaic system again in a preset period. According to the method, the multi-peak dilemma caused by local shading can be effectively avoided, accurate tracking of the global maximum power point is realized, and quick response and stability of output power can be taken into account.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of photovoltaic power generation intelligent regulation and control, and particularly relates to a multi-strategy hybrid photovoltaic maximum power point tracking control method. BACKGROUND

[0002] With the continuous growth of global energy demand and the increasing severity of environmental problems, solar photovoltaic power generation as a clean and renewable energy utilization method has received widespread attention. The core goal of a photovoltaic power generation system is to efficiently convert solar energy into electrical energy, and the Maximum Power Point Tracking (MPPT) technology plays a crucial role in the operation of the photovoltaic system. The MPPT technology aims to control the photovoltaic system to operate near the maximum power point under different light intensity and temperature conditions, thereby improving the energy conversion efficiency of the system.

[0003] In existing MPPT technologies, traditional methods such as variable step size perturbation and observation method, incremental conductance method, etc. can achieve relatively stable and fast maximum power point tracking under uniform light conditions. However, in actual application environments, photovoltaic arrays are inevitably affected by local shading, such as cloud cover or component surface contamination, which causes uneven light, making each component in the photovoltaic array work under different temperature and irradiation conditions, thereby causing the output power-voltage characteristic curve of the photovoltaic system to exhibit a multi-peak characteristic, with multiple local maximum power points. In this case, the traditional MPPT method is easily trapped in a local optimum point and cannot correctly identify and track the global maximum power point, resulting in a significant decrease in power generation efficiency.

[0004] To overcome the limitations of traditional methods, researchers have applied heuristic optimization algorithms (such as particle swarm optimization algorithm, genetic algorithm, cuckoo algorithm, etc.) to MPPT control methods. This type of method can find the global maximum power point in a complex multi-peak photovoltaic output characteristic curve, with relatively high accuracy. However, its disadvantage is that the tracking speed is not ideal, and during the search process, there is often a large power fluctuation, thereby affecting the stability and output quality of the photovoltaic system. Therefore, some studies have proposed a two-step composite calculation strategy, which uses a heuristic optimization algorithm in the initial global optimization stage and switches to a traditional method in the later local tracking stage. This calculation strategy improves the tracking speed to some extent, but still has the problem of excessive power fluctuation in the initial global optimization stage, resulting in the system operation efficiency and stability still being difficult to balance. SUMMARY

[0005] The present application aims at the deficiencies in the prior art, and provides a multi-strategy hybrid photovoltaic maximum power point tracking control method, which can effectively avoid the multi-peak dilemma caused by local shading, realize accurate tracking of the global maximum power point, and balance the rapid response and the stability of the output power in the photovoltaic maximum power point tracking control process, so as to further improve the operation performance of the photovoltaic system in the actual complex environment.

[0006] The present application provides the following technical solutions: In a first aspect, a multi-strategy hybrid photovoltaic maximum power point tracking control method is provided, comprising: acquiring real-time data of a photovoltaic array; searching for an initial maximum power point of the photovoltaic array by using a heuristic optimization algorithm, wherein in the searching process, the output power of each candidate solution is predicted by using a pre-constructed photovoltaic array output prediction model based on the real-time data of the photovoltaic array, and the output power is used as the fitness; adjusting the duty cycle of a photovoltaic array control circuit based on the obtained initial maximum power point, and performing perturbation control by using a variable step size perturbation and observation method to output the optimal duty cycle of the photovoltaic array control circuit; monitoring the output power of the photovoltaic array in real time, if the output power exceeds a preset fluctuation, re-searching for the initial maximum power point by using the heuristic optimization algorithm, otherwise, re-performing the perturbation control of the photovoltaic system by using the variable step size perturbation and observation method within a preset period.

[0007] Optionally, the real-time data of the photovoltaic array includes working voltage, output power, current light irradiance and environmental temperature, wherein the working voltage and the output power of the photovoltaic array are acquired, and specifically: under the current light irradiance and the environmental temperature, collecting the open-circuit voltage of the photovoltaic array ; adjusting the duty cycle of the photovoltaic array control circuit to make the working voltage of the photovoltaic array , and collecting the output power of the photovoltaic array correspondingly, wherein the working voltage of the photovoltaic array is , and the calculation formula of the working voltage is: ; wherein, n is the number of photovoltaic components in series in the photovoltaic array. Optionally, the heuristic optimization algorithm includes a particle swarm optimization algorithm, a cuttlefish optimization algorithm or a grey wolf optimization algorithm, and the target of the heuristic optimization algorithm is the maximum output power.

[0008] Optionally, the duty cycle of the photovoltaic array control circuit is adjusted based on the obtained initial maximum power point, and a variable step size perturbation and observation method is used for perturbation control to output an optimal duty cycle of the photovoltaic array control circuit, specifically: Step a: adjusting the duty cycle of the photovoltaic array control circuit using the initial maximum power point; Step b: controlling the photovoltaic system to work using the current perturbed duty cycle, and collecting the change in output power before and after the perturbation Step c: determining whether the change in output power before and after the perturbation reaches a termination condition, if yes, the duty cycle corresponding to the current perturbation operation is the optimal duty cycle, otherwise, the tracking step size is adjusted according to the change in operating voltage before and after the perturbation and the change in output power , and the duty cycle of the next perturbation is re-adjusted according to the adjusted tracking step size, and returns to step b; The termination condition is: Among them, is the output power of the photovoltaic system after the current perturbation operation, is the output power of the photovoltaic system before the perturbation, is a preset termination threshold.

[0009] Optionally, in step c, the tracking step size is adjusted according to the change in operating voltage before and after the perturbation and the change in output power , and the duty cycle of the next perturbation is re-adjusted according to the adjusted tracking step size, specifically: Among them, is the tracking step size, is a variable step size function, is the duty cycle of the next perturbation, is the duty cycle of the current perturbation, is a direction coefficient.

[0010] Optionally, the output power of the photovoltaic array is monitored in real time, and if it exceeds a preset fluctuation, the initial maximum power point is searched again using a heuristic optimization algorithm, otherwise, the photovoltaic system is perturbed and controlled using the variable step size perturbation and observation method within a preset period, specifically: The output power of the photovoltaic array is obtained ​​​​​If the first restart condition is satisfied, the initial maximum power point is searched again by using the heuristic optimization algorithm; If the first restart condition is not satisfied, the second restart condition is judged. The first restart condition is: ; wherein, is the maximum output power corresponding to the optimal duty cycle of the perturbation control output, is a preset condition fluctuation threshold value; The second restart condition is: ; wherein, is the current time, is the time corresponding to the maximum output power, is a preset time threshold value.

[0011] In a second aspect, a computer device is provided, comprising a processor and a memory; wherein the processor implements the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method according to any one of the first aspect when executing the computer program stored in the memory.

[0012] In a third aspect, a computer readable storage medium is provided for storing a computer program; the computer program is executed by a processor to implement the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method according to any one of the first aspect.

[0013] Compared with the prior art, the present application has the following advantages: (1) The present application uses the output characteristic model of the photovoltaic array under different light and temperature distribution conditions as the fitness calculation function of the heuristic optimization algorithm, which avoids directly applying each candidate solution to the photovoltaic array in the calculation process of the heuristic optimization algorithm, thereby causing a sharp fluctuation of the system output power and improving the operation stability of the system. In addition, the present application takes advantage of the strong global search capability of the heuristic optimization algorithm to effectively avoid the photovoltaic array from being trapped in the local maximum power point caused by uneven light, accurately identify the global maximum power point, and improve the system power generation efficiency. On the basis of the calculation result of the heuristic optimization algorithm, the variable step perturbation and observation method is further used to quickly and accurately converge to the maximum power point of the photovoltaic array, thereby improving the system rapid response capability and reducing the energy loss of the system in the maximum power point tracking process.

[0014] (2) The application sets two-stage MPPT calculation restart conditions, which significantly enhances the adaptability and robustness of the system under different working conditions while optimizing resource utilization and ensuring tracking timeliness. The power fluctuation restart condition ensures that the complex heuristic optimization algorithm is only started when the environment changes dramatically, effectively saving computing resources and avoiding photovoltaic array output oscillation; the time threshold restart condition, as a regular calibration, fine-tunes the maximum power point in a relatively stable environment, and the system works in a low-power fine tracking mode, ensuring the real-time tracking. This enables the system to intelligently distinguish and efficiently handle two typical working conditions: dramatic changes and smooth and slow changes, thereby exhibiting stronger comprehensive performance and power generation efficiency in all-weather operation. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is the overall flowchart of the multi-strategy hybrid photovoltaic maximum power point tracking control method of the application; Figure 2 is the photovoltaic array output prediction model structure diagram of the application; Figure 3 is the heuristic optimization algorithm MPPT calculation flowchart of the application; Figure 4 is the flowchart of the disturbance control using variable step size perturbation and observation method of the application. DETAILED DESCRIPTION

[0016] The application will be further described below with reference to the drawings. The following examples are only used to more clearly illustrate the technical solutions of the application, and cannot be used to limit the protection scope of the application. It should be noted that the terms "comprise" and any variations thereof in the specification and claims of the application and the above-mentioned drawings are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] Example 1 A multi-strategy hybrid photovoltaic maximum power point tracking control method, comprising: acquiring real-time data of the photovoltaic array, including operating voltage, output power, current illumination irradiance and environmental temperature; searching for the initial maximum power point of the photovoltaic array using a heuristic optimization algorithm, in the search process, based on the real-time data of the photovoltaic array, using a pre-constructed photovoltaic array output prediction model to predict the output power of each candidate solution and as fitness; Adjusting the duty cycle of the photovoltaic array control circuit based on the obtained initial maximum power point, and performing perturbation control using a variable step size perturb and observe method to output an optimal duty cycle of the photovoltaic array control circuit; Real-time monitoring of the output power of the photovoltaic array, if exceeding the preset fluctuation, then reusing the heuristic optimization algorithm to search for the initial maximum power point, otherwise, then reusing the variable step size perturb and observe method to perform perturbation control of the photovoltaic system within a preset period.

[0018] According to the optimal duty cycle, real-time control of the operation of the photovoltaic system, the specific mode can refer to the prior art, for example, converting the optimal duty cycle into a PWM pulse signal, so that the photovoltaic system operates at the maximum power point.

[0019] The operation flow of the above method in specific implementation is shown in Figure 1 The specific implementation operation steps include: Step 1, establishing a photovoltaic array output prediction model.

[0020] S1.1: Test the output characteristics of the photovoltaic array under different light distribution and temperature distribution conditions.

[0021] S1.1.1: Under a certain light and temperature distribution condition, the output characteristic test method of the photovoltaic array is: S1.1.1.1: Adjust the duty cycle of the control circuit to make the photovoltaic array in an open circuit state, and collect the open circuit voltage U OC .

[0022] S1.1.1.2: Traverse the adjustment of the duty cycle to make the working voltage of the photovoltaic array ( i ∈[1, n ]), U i The expression is shown in formula (1), and the output power of the photovoltaic array is collected P i ( i ∈[1, n ])。

[0023] (1) In the formula, n is the number of photovoltaic components in series in the photovoltaic array, is the preset first i group working voltage.

[0024] S1.1.2: According to the method of step S1.1.1, test the output characteristics of the photovoltaic array under multiple light distribution and temperature distribution conditions, which should cover the daily operation of the photovoltaic array.

[0025] S1.2: Photovoltaic array operating voltage under a certain light and temperature distribution U i and output power P i i ∈[1, n ] is a set of data, and all the test data of step S1.1.2 are constructed as a model training data set.

[0026] S1.3: A photovoltaic array output prediction model is established using a supervised machine learning algorithm (including but not limited to neural network algorithm, support vector machine algorithm, etc.), and the model structure is as shown in Figure 2 The input parameters of the model are the operating voltage S and output power T a of the photovoltaic array under the condition of current light irradiance U i and ambient temperature P i i ∈[1, n ] and the preset photovoltaic array operating voltage U C , and the output parameters of the model are the output power U C of the photovoltaic array under the operating voltage P C .

[0027] Step 2, collect real-time data of photovoltaic array S2.1: Adjust the duty cycle of the control circuit to make the photovoltaic array in an open circuit state, and collect the open circuit voltage U OC .

[0028] S2.2: Traverse the duty cycle so that the operating voltage of the photovoltaic array is U i i ∈[1, n ], U i as shown in equation (1), and collect the output power P i i ∈[1, n ].

[0029] S2.3: Collect the current light irradiance S and ambient temperature T a .

[0030] ​​​​Step 3, heuristic optimization algorithm MPPT calculation based on photovoltaic array output prediction model.

[0031] Heuristic optimization algorithm MPPT calculation based on photovoltaic array output prediction model, its calculation process is shown in Figure 3 .

[0032] S3.1: Set the parameters of the heuristic optimization algorithm (including but not limited to particle swarm optimization algorithm, cuttlefish optimization algorithm, grey wolf optimization algorithm, etc.), and set the output power of the photovoltaic array as the fitness of the heuristic optimization algorithm.

[0033] S3.2: Randomly generate X candidate solutions as optimization variables of the heuristic optimization algorithm.

[0034] S3.3: Calculate the fitness of each candidate solution using the photovoltaic array output prediction model established in Step 1, and the real-time data of the photovoltaic array collected in Step 2 and the candidate solution as model input data.

[0035] S3.4: Sort the candidate solutions according to the fitness, and determine the optimal candidate solution.

[0036] S3.5: Perform iterative optimization operations on the candidate solutions: S3.5.1: Perturb and update each candidate solution .

[0037] S3.5.2: Calculate the fitness of each candidate solution using the photovoltaic array output prediction model established in Step 1, and the real-time data of the photovoltaic array collected in Step 2 and the candidate solution as model input data.

[0038] S3.5.3: Sort the candidate solutions according to the fitness, and determine the optimal candidate solution.

[0039] S3.5.4: Determine whether the iteration optimization termination condition is met, and the termination condition includes but is not limited to: candidate solution standard deviation is less than a preset threshold, candidate solution maximum voltage difference is less than a preset threshold, preset iteration number, calculation time is exhausted, etc. If the termination condition is not met, return to execute step S3.5.3, if it is met, terminate the iteration optimization operation.

[0040] S3.6: Output the optimal candidate solution .

[0041] S3.7: Adjust the duty cycle of the control circuit so that the operating voltage of the photovoltaic array is .

[0042] Step 4, variable step size perturb and observe MPPT calculation The calculation process of the variable step size perturb and observe MPPT calculation is as shown in Figure 4 .

[0043] S4.1: Obtain the current operating voltage U (0) and output power P (0) of the photovoltaic array, and the current duty cycle D (0) of the control circuit, which is equivalent to the duty cycle corresponding to the operating voltage output in Step 3. .

[0044] S4.2: Calculate and set the control circuit duty cycle D (1), D (1) is expressed as formula (2), and the perturbation number k is set to 1.

[0045] (2) In the formula, Lambda 0 is the preset maximum tracking step size.

[0046] S4.3: Collect the operating voltage U ( k ) and output power P ( k ) of the photovoltaic array after perturbation, and calculate Δ P and ΔU, as shown in formulas (3) and (4).

[0047] (3) (4) S4.4: Determine whether the termination condition is met, and the termination condition is as shown in formula (5). If the termination condition is met, stop calculation and output the duty cycle D ( k ). If the termination condition is not met, calculate the tracking step size Lambda according to formula (6) and execute Step (4.5).

[0048] (5) In the formula, Epsilon is the preset termination threshold.

[0049] (6) In the formula, f is a variable step size function, including but not limited to a proportional function, a partition variable step size function, etc.

[0050] S4.5: Record the perturbation number kAdd 1, calculate and set the control circuit duty cycle according to formula (7) D k ), and return to execute step S4.1.

[0051] (7) In the formula, α is a direction coefficient, and the value is shown in formula (8).

[0052] (8) Step 5, set the control circuit duty cycle, and judge the restart condition.

[0053] S5.1: set the control circuit duty cycle to D ( k ), collect the output power of the photovoltaic array and record it as the maximum output power , and record the current time .

[0054] S5.2: collect the output power of the photovoltaic array in real time , judge whether the MPPT calculation restart condition 1 is reached, and the restart condition 1 is shown in formula (9). If the restart condition 1 is established, it indicates that the change of operating conditions such as irradiance and environmental temperature causes the maximum power point to move greatly, and then return to execute Step 2. If the restart condition 1 is not established, execute step S5.3.

[0055] (9) In the formula, is a preset condition fluctuation threshold.

[0056] S5.3: judge whether the MPPT calculation restart condition 2 is reached, and the restart condition 2 is shown in formula (10). If the restart condition 2 is established, return to execute Step 4, so as to ensure the timeliness of the maximum output power. If the restart condition 2 is not established, return to execute step S5.2.

[0057] (10) In the formula, is the current time; is a preset time threshold.

[0058] The application can effectively avoid the multi-peak dilemma caused by local shading in the photovoltaic maximum power point tracking control process, realize accurate tracking of the global maximum power point, and also consider the rapid response and stability of the output power, so as to further improve the operation performance of the photovoltaic system in the actual complex environment.

[0059] Example 2 ​The application provides a computer device, comprising a processor and a memory; wherein the processor implements the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method when executing the computer program stored in the memory.

[0060] More specific processes of the above method can refer to the corresponding contents disclosed in the foregoing embodiments, and will not be described here.

[0061] Embodiment 3 The application provides a computer readable storage medium for storing a computer program; the computer program is executed by a processor to implement the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method.

[0062] More specific processes of the above method can refer to the corresponding contents disclosed in the foregoing embodiments, and will not be described here.

[0063] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the devices and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0064] Those skilled in the art can clearly understand that the technologies in the embodiments of the application can be realized by means of software and necessary general hardware platforms. Based on such understanding, the technical solutions in the embodiments of the application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the embodiments of the application or some parts of the embodiments.

[0065] The above is only the preferred embodiment of the application, and the protection scope of the application is not limited to the above embodiments. Any technical solution falling within the idea of the application belongs to the protection scope of the application. It should be noted that, for ordinary skilled in the art, some improvements and refinements without departing from the principles of the application should be considered as the protection scope of the application.

Claims

1. A multi-strategy hybrid photovoltaic maximum power point tracking control method, characterized in that, The method comprises the following steps: acquiring real-time data of a photovoltaic array; searching for an initial maximum power point of the photovoltaic array by using a heuristic optimization algorithm, in the searching process, predicting output power of each candidate solution by using a pre-constructed photovoltaic array output prediction model based on the real-time data of the photovoltaic array and taking the output power as fitness; adjusting a duty cycle of a photovoltaic array control circuit based on the obtained initial maximum power point and performing perturbation control by using a variable step size perturb and observe method to output an optimal duty cycle of the photovoltaic array control circuit; monitoring output power of the photovoltaic array in real time, if the output power exceeds a preset fluctuation, searching for the initial maximum power point by using the heuristic optimization algorithm again, otherwise, performing perturbation control of the photovoltaic system by using the variable step size perturb and observe method again in a preset period.

2. The multi-strategy hybrid photovoltaic maximum power point tracking control method according to claim 1, characterized in that, The real-time data of the photovoltaic array comprises working voltage, output power, current illumination irradiance and environmental temperature; wherein the working voltage and the output power of the photovoltaic array are acquired, and specifically: at the current light irradiance and ambient temperature ; The duty cycle of the photovoltaic array control circuit is traversed to make the working voltage of the photovoltaic array , and the output power of the photovoltaic array is correspondingly collected , wherein the working voltage of the photovoltaic array is The calculation formula is: ； wherein n is the number of photovoltaic modules in series in the photovoltaic array.

3. The multi-policy hybrid photovoltaic maximum power point tracking control method according to claim 1, wherein, The heuristic optimization algorithm comprises a particle swarm optimization algorithm, a cuttlefish optimization algorithm or a grey wolf optimization algorithm; and the target of the heuristic optimization algorithm is maximum output power.

4. The multi-policy hybrid photovoltaic maximum power point tracking control method according to claim 1, characterized in that, The step of adjusting the duty cycle of the photovoltaic array control circuit based on the obtained initial maximum power point and performing perturbation control by using the variable step size perturb and observe method to output the optimal duty cycle of the photovoltaic array control circuit specifically comprises the following steps: Step a: adjusting the duty cycle of the photovoltaic array control circuit by using the initial maximum power point; Step b: control the photovoltaic system to work by using the duty ratio of the current disturbance, and collect the output power variation before and after the disturbance ; Step c: judging the variation of output power before and after the disturbance whether the termination condition is reached, if yes, the duty cycle corresponding to the current disturbance operation is the optimal duty cycle, otherwise, the tracking step is adjusted according to the variation of working voltage and the variation of output power before and after the disturbance and the variation of output power , and the duty cycle of the next disturbance is re-adjusted according to the adjusted tracking step, and returning to step b; The termination condition is: ； wherein, is the output power of the photovoltaic system after performing the current perturbation operation, is the output power of the photovoltaic system before the perturbation, is a pre-set termination threshold.

5. The multi-strategy hybrid photovoltaic maximum power point tracking control method according to claim 4, characterized in that, In step c, the tracking step size is adjusted according to the change of the operating voltage before and after the disturbance and the change of the output power , and the duty cycle of the next disturbance is re-adjusted according to the adjusted tracking step size, specifically: ； ； ； wherein, is a tracking step size, is a variable step function, is a duty cycle of the next perturbation, is a duty cycle of the current perturbation, is a direction coefficient.

6. The multi-policy hybrid photovoltaic maximum power point tracking control method according to claim 1, wherein, The step of monitoring the output power of the photovoltaic array in real time, if the output power exceeds the preset fluctuation, searching for the initial maximum power point by using the heuristic optimization algorithm again, otherwise, performing perturbation control of the photovoltaic system by using the variable step size perturb and observe method again in the preset period specifically comprises the following steps: Obtaining output power of a photovoltaic array , determining whether a first restart condition is satisfied, and if so, searching for an initial maximum power point using a heuristic optimization algorithm; If the first restart condition is not met, it is determined whether a second restart condition is met, if the second restart condition is met, perturbation control of the photovoltaic system is performed by using the variable step size perturb and observe method again, otherwise, the output power of the photovoltaic array is continuously monitored; The first restart condition is: ; wherein, is the maximum output power corresponding to the optimal duty ratio of the disturbance control output, is a preset condition fluctuation threshold; The second restart condition is: wherein, is the current time, is the time corresponding to the maximum output power, is a preset time threshold.

7. A computer device, comprising: The device comprises a processor and a memory; wherein the processor implements the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method according to any one of claims 1-6 when executing a computer program saved in the memory.

8. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to implement the steps of the multi-strategy hybrid photovoltaic maximum power point tracking control method according to any one of claims 1-6.

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