Photovoltaic system evaluation method, apparatus, and electronic device

Abstract

The present disclosure relates to a photovoltaic system evaluation method, device and electronic equipment, the method comprising: obtaining evaluation data of MPPT tracking effect, determining a first target sequence value and a second target sequence value based on the evaluation data, performing state calibration operation according to the first target sequence value and the second target sequence value, the first target sequence value being determined based on at least one of a current sequence and a voltage sequence, the second target sequence value being determined based on at least one of light intensity and ambient temperature, obtaining a first state sequence and a second state sequence, and selecting at least one special branch from a plurality of branches based on the first state sequence and the second state sequence; and evaluating the MPPT tracking effect of the at least one special branch to obtain a branch evaluation result. The present application can autonomously and conveniently evaluate the photovoltaic system by performing the state calibration operation.

Description

Technical Field

[0001] This disclosure relates to the field of photovoltaic power generation technology, and more specifically, to a photovoltaic system evaluation method, apparatus, and electronic equipment. Background Technology

[0002] In today's society, people are paying increasing attention to environmental protection and resource conservation. The deteriorating ecological environment and dwindling material resources are driving the vigorous development of renewable and clean energy. Photovoltaic power generation, with its wide distribution, large reserves, and clean and renewable characteristics, has experienced rapid development in recent years. In existing photovoltaic power generation systems, maximum power point tracking (MPPT) control strategies are needed to improve the output power of solar panels. How to better evaluate the tracking efficiency of devices with MPPT functionality is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The purpose of this disclosure is to provide photovoltaic system evaluation methods, apparatus, and electronic equipment that enable autonomous and convenient evaluation of photovoltaic systems.

[0004] To achieve the above objectives, the first aspect of this disclosure provides a photovoltaic system evaluation method, the method comprising:

[0005] Obtain evaluation data for MPPT tracking performance. The evaluation data includes at least one of the following: current sequence, voltage sequence, light intensity sequence, and ambient temperature sequence for multiple branches.

[0006] The first target sequence value and the second target sequence value are determined based on the evaluation data. The first target sequence value is determined based on at least one of the current sequence and the voltage sequence, and the second target value is determined based on at least one of the light intensity and the ambient temperature.

[0007] Perform a state calibration operation based on the first target sequence value and the second target sequence value to obtain the first state sequence and the second state sequence;

[0008] Select at least one special branch from multiple branches based on the first state sequence and the second state sequence;

[0009] The MPPT tracing performance of at least one specific branch is evaluated to obtain the branch evaluation results.

[0010] Optionally, the first target sequence value and the second target sequence value include multiple data points, and the data points of the first target sequence value correspond to the data points of the second target sequence value.

[0011] Based on the first target sequence value and the second target sequence value, a state calibration operation is performed to obtain a first state sequence and a second state sequence, including:

[0012] Based on the first target sequence value and the second target sequence value, a state calibration operation is performed to obtain a first state sequence and a second state sequence, including:

[0013] Obtain the data points that are adjacent to and continuous with each data point in the first target sequence value and the second target sequence value;

[0014] The state of each data point is determined by adjacent and consecutive data points to achieve state calibration, resulting in a first state sequence and a second state sequence.

[0015] Optionally, the state of the branch can be an ascending state, a descending state, a stable state, a minimum state, or a maximum state.

[0016] Optionally, the method further includes:

[0017] Obtain the status of the photovoltaic system;

[0018] When the photovoltaic system is in a specified state, determine that the data in the first target sequence value and the second target sequence value are valid.

[0019] Optionally, the state of the photovoltaic system includes at least one of the following: initialization state, light detection state, grid detection state, normal grid connection state, power limiting grid connection state, self-derating grid connection state, and shutdown state, with the specified state including the normal grid connection state.

[0020] Optionally, the method further includes:

[0021] Based on the branch assessment results, determine whether the MPPT tracing of at least one special branch is faulty.

[0022] Optionally, the MPPT tracing performance of at least one specific branch is evaluated, including:

[0023] The MPPT tracking performance of at least one special branch is evaluated using at least one of the following: state matching algorithm, power curve similarity algorithm, and power generation index.

[0024] A second aspect of this disclosure provides a photovoltaic system evaluation apparatus, the apparatus comprising:

[0025] The data acquisition module is used to acquire evaluation data of MPPT tracking effect. The evaluation data includes at least one of the following: current sequence, voltage sequence, light intensity sequence and ambient temperature sequence corresponding to multiple branches.

[0026] The sequence value determination module is used to determine a first target sequence value and a second target sequence value based on evaluation data. The first target sequence value is determined based on at least one of a current sequence and a voltage sequence, and the second target sequence value is determined based on at least one of light intensity and ambient temperature.

[0027] The state calibration module is used to perform a state calibration operation based on the first target sequence value and the second target sequence value to obtain the first state sequence and the second state sequence.

[0028] A filtering module is used to select at least one special branch from multiple branches based on a first state sequence and a second state sequence;

[0029] The evaluation module is used to evaluate the MPPT tracing performance of at least one specific branch and obtain the branch evaluation results.

[0030] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0031] A memory on which computer programs are stored;

[0032] A processor for executing the computer program in the memory to implement the steps of the photovoltaic system evaluation method provided in the first aspect.

[0033] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the photovoltaic system evaluation method provided in the first aspect of the present disclosure.

[0034] In the above technical solution, evaluation data of MPPT tracking effect is first obtained. This evaluation data may include at least one of the current sequence, voltage sequence, light intensity sequence, and ambient temperature sequence corresponding to multiple branches. Based on this, a first target sequence value and a second target sequence value are determined based on the evaluation data. The first target sequence value is determined based on at least one of the current sequence and voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and ambient temperature. A state calibration operation is performed based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence. At least one special branch is selected from multiple branches based on the first state sequence and the second state sequence. The MPPT tracking effect of the at least one special branch is evaluated to obtain the branch evaluation result. In this way, the evaluation of the photovoltaic system can be realized autonomously and conveniently.

[0035] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0037] Figure 1This is a flowchart illustrating a photovoltaic system evaluation method according to an exemplary embodiment.

[0038] Figure 2 This is a process example diagram illustrating a photovoltaic system evaluation method according to an exemplary embodiment.

[0039] Figure 3 This is a flowchart illustrating a photovoltaic system evaluation method according to another exemplary embodiment.

[0040] Figure 4 This is a block diagram illustrating a photovoltaic system evaluation device according to an exemplary embodiment.

[0041] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0042] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0043] It should be understood that the various steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect. The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the description below.

[0044] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules, or units, and are not used to limit the order of functions performed by these devices, modules, or units or their interdependencies. It should also be noted that the modifications of "a" and "a plurality of" mentioned in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0045] Solar photovoltaic (PV) power generation is one of the fastest-growing and most promising renewable energy industries due to its significant energy, environmental, and economic benefits. The amount of electricity generated by a PV system directly affects the economic returns of the entire PV power plant. Many factors influence the power generation efficiency of a PV power plant. In addition to direct factors such as light intensity, temperature, module efficiency, and equipment losses, whether each branch circuit is operating at its maximum power point (IV characteristic) also directly affects the power output.

[0046] The MPPT (Maximum Power Point Tracking) function ensures that the output power of the device remains near the maximum power point by dynamically adjusting the voltage and current, thereby maximizing photovoltaic power generation efficiency. To guarantee the power generation efficiency of photovoltaic power plants, it is necessary to conduct tracking efficiency assessments on devices with MPPT functionality. However, currently, there is a lack of methods for evaluating the tracking performance of devices with MPPT functionality.

[0047] In view of this, the present disclosure provides a photovoltaic system evaluation method, apparatus and electronic equipment to solve the above-mentioned technical problems.

[0048] Figure 1 This is a flowchart illustrating a photovoltaic system evaluation method according to an exemplary embodiment, such as... Figure 1 As shown, it includes the following steps.

[0049] In step S110, evaluation data of MPPT tracking effect is obtained.

[0050] In this embodiment of the application, the evaluation data may include at least one of the following: current sequence, voltage sequence, light intensity sequence, and ambient temperature sequence corresponding to multiple branches.

[0051] As an optional approach, after collecting basic production data from the photovoltaic power plant via a data interface, this embodiment of the application can collect evaluation data on the MPPT tracking effect at specified time intervals. These specified time intervals can be 5 min, 10 min, or 30 min, etc. In other words, the evaluation data can include data such as the voltage sequence, current sequence, irradiance sequence, and ambient temperature sequence of each branch of the equipment within a specified time window.

[0052] It should be noted that the evaluation time in this embodiment can be data collected at different times corresponding to different branches. In other words, the data collected at the same time can include data from multiple branches. For example, the number of branches can be 18.

[0053] In step S120, the first target sequence value and the second target sequence value are determined based on the evaluation data.

[0054] In some implementations, after obtaining evaluation data on the MPPT tracking effect, embodiments of this application can determine a first target sequence value and a second target sequence value based on the evaluation data. The first target sequence value can be determined based on at least one of a current sequence and a voltage sequence, and can be composed of data from multiple branches collected at different times. Similarly, the second target sequence value can be determined based on at least one of light intensity and ambient temperature. Likewise, the second target sequence value can be composed of data collected at different times.

[0055] In some specific implementations, the first target sequence value can be a branch power generation value sequence. In this case, the branch power generation value sequence can be obtained by multiplying the voltage value in the current sequence by the voltage value in the voltage sequence. For example, if the current in branch 1 at the first moment is I1 and the voltage is U1, then its corresponding branch power generation value can be I1*U1. Similarly, if the current in branch 2 at the first moment is I2 and the voltage is U2, then its corresponding branch power generation value can be I2*U2.

[0056] As an optional approach, the second target sequence value may include multiple external environmental parameters, such as light intensity and temperature. In a specific implementation, the second target sequence value may consist of light intensity, which can also be referred to as irradiance. As described above, the second target sequence value may include multiple external environmental parameters collected at different times. For example, the second target sequence value may include external environmental parameter 1 collected at a first time and external environmental parameter 2 collected at a second time, wherein external environmental parameter 1 and external environmental parameter 2 may be at least one of light intensity and ambient temperature.

[0057] It should be noted that the data in the first target sequence value and the second target sequence value can correspond to each other. In other words, the data in the first target sequence value and the second target sequence value at the same time can correspond to each other. In addition, the data of multiple branches in the first target sequence value can correspond to one data point in the second target sequence value. For example, if the first target sequence value includes data from 18 branches at the first time, the corresponding data in the second target sequence value could be a light intensity value.

[0058] In step S130, a state calibration operation is performed based on the first target sequence value and the second target sequence value to obtain the first state sequence and the second state sequence.

[0059] As an optional approach, after obtaining the first target sequence value and the second target sequence value, this embodiment of the application can perform a state calibration operation to obtain a first state sequence and a second state sequence. The first state sequence can be a sequence obtained by calibrating data points in the first target sequence value. Similarly, the second state sequence can be a sequence obtained by calibrating data in the second target sequence value.

[0060] As a specific implementation method, when performing the state calibration operation, this embodiment can be based on the preceding and following data for each data point, that is, the state calibration operation can be implemented according to the changing trend of the data points. As an example, this embodiment can use a three-point state calibration algorithm for the state calibration operation. As another example, this embodiment can also use a two-point or five-point state calibration algorithm for the state calibration operation. The specific number of state calibration algorithms used for the state calibration operation is not explicitly limited here and can be selected according to the actual situation.

[0061] In this embodiment of the application, the state of the branch may include rising state, falling state, stable state, minimum state or maximum state, etc.

[0062] In step S140, at least one special branch is selected from multiple branches based on the first state sequence and the second state sequence.

[0063] In some implementations, after performing the state labeling operation, embodiments of this application can select at least one special branch from multiple branches based on a first state sequence and a second state sequence. Specifically, embodiments of this application can filter multiple branches based on a majority rule to obtain at least one special branch.

[0064] As an example, there are 18 branches. Within a preset time period, 15 branches have the same state, while 3 branches have different states. In this case, the embodiments of this application can treat these 3 branches as special branches.

[0065] In step S150, the MPPT tracking performance of at least one special branch is evaluated to obtain the branch evaluation result.

[0066] Alternatively, after obtaining at least one special branch, embodiments of this application can evaluate the MPPT tracking performance of the at least one special branch to obtain branch evaluation results for each special branch. Specifically, when evaluating the MPPT tracking performance of at least one special branch, embodiments of this application can utilize at least one of a state matching algorithm, a power curve similarity algorithm, and a power generation index to evaluate the MPPT tracking performance of the at least one special branch.

[0067] The state matching algorithm is used to match the first state sequence with the second state sequence, and a 0-1 calculation method can be used during the matching process. The state matching algorithm can calculate the score of each branch; a higher score indicates a better match and thus a better MPPT tracking effect. Since the same MPPT unit in a real device often includes two terminals, meaning it can simultaneously connect to two string branches, a low score on one terminal and a high score on the other cannot be used to determine the MPPT unit's tracking performance. Therefore, when filtering units with poor MPPT tracking performance, this embodiment of the disclosure provides a step to obtain the average score of branches under a unified MPPT unit, thereby determining the MPPT unit's performance.

[0068] As an example, at the first moment, the first state sequence includes the states of two branches: branch 1 is in an increasing state, and branch 2 is in a stable state. Simultaneously, at the first moment, the second state sequence includes the state of light intensity, which is also in an increasing state. By matching the first and second state sequences, we can see that the state of branch 1 is the same as the state of light intensity, so the value of branch 1 is 1. However, the state of branch 2 is different from the state of light intensity, so the value of branch 2 is 0.

[0069] Optionally, a power curve similarity algorithm can be used to obtain branch evaluation results by calculating the similarity between the theoretical power generation and the actual branch power curve. The theoretical power generation can be obtained through external factors such as ambient temperature, irradiance, and equipment parameters. If the tracking effect is good, the actual power generation curve of the branch will approximate the theoretical power generation curve. Furthermore, the horizontal axis of the theoretical power generation and actual branch power curves can be time, and the vertical axis can be power.

[0070] Optionally, the power generation index can be used to evaluate the branches by analyzing the power generation of each branch. The higher the power generation, the better the MPPT tracking effect, and vice versa.

[0071] It should be noted that the embodiments of this application can also evaluate the MPPT tracking effect of at least one special branch using parameters such as actual power, loss rate, and photovoltaic system efficiency PR (Performance Ratio) value. The specific algorithm used to evaluate the MPPT tracking effect of at least one special branch is not explicitly limited in the embodiments of this application and can be selected according to the actual situation.

[0072] This application requires simple data for evaluating photovoltaic systems and can achieve the evaluation without the aid of third-party equipment, thus reducing the cost of system evaluation to a certain extent. Furthermore, since this application does not require off-grid testing, it does not affect production and offers significant economic benefits. Moreover, the evaluation method of this application is rapid, simple, and highly accurate, not only quickly determining the MPPT tracking performance of each photovoltaic device but also providing support for other fault detection.

[0073] To better illustrate the photovoltaic system evaluation process, the embodiments of this application provide, as follows: Figure 2 The example diagram shown is from Figure 2 The evaluation process can be divided into three modules: the first module is the data acquisition module, which is used to acquire evaluation data of MPPT tracking effect; the second module is the state calibration module, which is used to perform state calibration operations; and the third module is the tracking effect evaluation module, which is used to perform evaluation operations to obtain branch evaluation results.

[0074] This application embodiment first obtains evaluation data on MPPT tracking performance. This evaluation data may include at least one of current sequences, voltage sequences, light intensity sequences, and ambient temperature sequences corresponding to multiple branches. Based on this, a first target sequence value and a second target sequence value are determined according to the evaluation data. The first target sequence value is determined based on at least one of the current sequence and voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and ambient temperature. A state calibration operation is performed based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence. At least one special branch is selected from multiple branches based on the first state sequence and the second state sequence. The MPPT tracking performance of the at least one special branch is evaluated to obtain the branch evaluation result. In this way, the evaluation of the photovoltaic system can be achieved autonomously and conveniently.

[0075] Figure 3 This is a flowchart illustrating a photovoltaic system evaluation method according to another exemplary embodiment, such as... Figure 3 As shown, it includes the following steps.

[0076] In step S210, evaluation data on the MPPT tracking effect is obtained.

[0077] In step S220, the first target sequence value and the second target sequence value are determined based on the evaluation data.

[0078] The specific implementation methods of steps S210 to S220 have been described in detail in the above embodiments and will not be repeated here.

[0079] In step S230, the data points that are adjacent to and continuous with each data point in the first target sequence value and the second target sequence value are obtained.

[0080] In this embodiment of the application, the first target sequence value and the second target sequence value may include multiple data points, wherein the data points of the first target sequence value may correspond to the data points of the second target sequence value, and the correspondence may be mutual at time points.

[0081] Alternatively, the first target sequence value may include the power values ​​of multiple branches at different time points, and the second target sequence value may include the values ​​of light intensity or ambient temperature at different time points. In other words, a light intensity value at the same time may correspond to the power values ​​of multiple branches, or an ambient temperature at the same time may correspond to the power values ​​of multiple branches.

[0082] In this embodiment of the application, each data point may have adjacent and continuous data points. When performing the state calibration operation, this embodiment of the application can determine the state of the target data point based on the adjacent and continuous data points to realize the state calibration operation. The adjacent and continuous data points may be adjacent at time points.

[0083] In step S240, the state of each data point is determined by adjacent and consecutive data points to realize the state calibration operation and obtain the first state sequence and the second state sequence.

[0084] As an optional approach, after obtaining the adjacent and continuous data points in the first target sequence value and the second target sequence value, the embodiments of this application can determine the state of each data point from the adjacent and continuous data points to realize the state calibration operation and obtain the first state sequence and the second state sequence. Here, the adjacent and continuous data points can be the states of data points connected and continuous at a given time point.

[0085] As an example, the first target sequence value includes data from the branches at three time points. The value of branch 1 at the first time point is 10, the value of branch 1 at the second time point is 11, and the value of branch 1 at the third time point is 12. It can be seen that the value of branch 1 increases at these three time points, so its state can be an ascending state.

[0086] As another example, the value of branch 2 is 10 at the first moment, 9 at the second moment, and 8 at the third moment. It can be seen that the value of branch 2 decreases at these three moments, so its state can be a decreasing state.

[0087] As another example, the value of branch 3 is 10 at the first time, the value of branch 3 is 10 at the second time, and the value of branch 3 is 10 at the third time. It can be seen that the value of branch 3 is stable at these three times, so its state can be a stable state.

[0088] As another example, the value of branch 4 is 10 at the first time, 11 at the second time, and 9 at the third time. It can be seen that the value of branch 4 increases first and then decreases at these three times, so its state can be a maximum point state.

[0089] As another example, the value of branch 5 is 10 at the first moment, 8 at the second moment, and 11 at the third moment. It can be seen that the value of branch 5 decreases first and then increases at these three moments, so its state can be a minimum state.

[0090] The process of calibrating the state of the second target sequence value is similar to that of the first target sequence value. However, the data points in the second target value are light intensity or ambient temperature, while the data points in the first target sequence value can be the power values ​​of each branch. The specific process of calibrating the state of the data points in the second target sequence value will not be elaborated here.

[0091] Alternatively, embodiments of this application may also obtain the state of the photovoltaic system, and if the state of the photovoltaic system is a specified state, determine that the data in the first target sequence value and the second target sequence value are valid.

[0092] The state of the photovoltaic system may include at least one of the following: initialization state, light detection state, grid detection state, normal grid connection state, power limiting grid connection state, self-derating grid connection state, and shutdown state. The specified state includes the normal grid connection state.

[0093] In other words, after obtaining the status of the photovoltaic system, this application embodiment can determine whether its status is a normal grid-connected state. If the photovoltaic system is determined to be in a normal grid-connected state, the data obtained in that state is valid; otherwise, it is invalid. At this point, the data obtained in that state can be used as data in the first target sequence value and the second target sequence value.

[0094] It should be noted that, when evaluating the validity of the state, the embodiments of this application can also determine whether the operating voltage of the inverter has reached a specified voltage. When the specified voltage is reached, the data collected at that voltage is determined to be valid; otherwise, the data collected at that voltage is determined to be invalid.

[0095] In step S250, at least one special branch is selected from multiple branches based on the first state sequence and the second state sequence.

[0096] As described above, when performing state calibration operations based on the first target sequence value and the second target sequence value to obtain the first state sequence and the second state sequence, the embodiments of this application can also determine the state of the photovoltaic system and retain the state as specified state data to obtain the first state sequence and the second state sequence.

[0097] Based on this, embodiments of this application can select at least one special branch from multiple branches based on a first state sequence and a second state sequence. In this process, this application can statistically analyze the probability percentage of each state in all branches at the same time point and assign each probability percentage to the corresponding branch state, thereby obtaining the percentage score of each branch state at that moment. This percentage score represents the consistency between the current branch and other branches; a higher score indicates better consistency, and vice versa. Optionally, this application can statistically sum the scores of each branch within a set time window and select the special branch according to the majority rule.

[0098] In step S260, the MPPT tracking performance of at least one special branch is evaluated to obtain the branch evaluation result.

[0099] As described above, the embodiments of this application can evaluate the MPPT tracking performance of at least one special branch using various algorithms to obtain branch evaluation results. In other words, this application can evaluate the MPPT tracking performance of at least one special branch from multiple dimensions to obtain branch evaluation results.

[0100] Specifically, in this application embodiment, a comprehensive threshold and a single-dimensional threshold can be set for the scores calculated from different dimensions. If the score of a branch in a certain dimension is lower than the single-dimensional threshold, it indicates that the MPPT tracing of that branch is faulty. Optionally, if the overall score of the abnormal branch is lower than the comprehensive threshold, it indicates that the MPPT tracing of that branch is faulty.

[0101] It should be noted that a single branch cannot represent the entire MPPT unit; an overall evaluation of all branches under the MPPT unit is required.

[0102] This application embodiment first obtains evaluation data on MPPT tracking performance. This evaluation data may include at least one of current sequences, voltage sequences, light intensity sequences, and ambient temperature sequences corresponding to multiple branches. Based on this, a first target sequence value and a second target sequence value are determined according to the evaluation data. The first target sequence value is determined based on at least one of the current sequence and voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and ambient temperature. A state calibration operation is performed based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence. At least one special branch is selected from multiple branches based on the first state sequence and the second state sequence. The MPPT tracking performance of the at least one special branch is evaluated to obtain the branch evaluation result. In this way, the evaluation of the photovoltaic system can be achieved autonomously and conveniently.

[0103] In addition, this application uses the majority decision criterion to quantitatively describe the state of each branch under the same equipment and screen special branches. Through state matching degree algorithm, power similarity algorithm and power generation indicators, it comprehensively evaluates the actual MPPT tracking effect of each branch, realizes the whole closed loop of the evaluation process, and achieves the goal of independently and conveniently evaluating the MPPT effect of photovoltaic power generation equipment in actual engineering scenarios without affecting production operation or using additional equipment.

[0104] Figure 4 This is a block diagram illustrating a photovoltaic system evaluation apparatus according to an exemplary embodiment, such as... Figure 4 As shown, the photovoltaic system evaluation device 300 may include a data acquisition module 310, a sequence value determination module 320, a state calibration module 330, a screening module 340, and an evaluation module 350.

[0105] The data acquisition module 310 is used to acquire evaluation data of MPPT tracking effect, the evaluation data including at least one of current sequence, voltage sequence, light intensity sequence and ambient temperature sequence corresponding to multiple branches;

[0106] The sequence value determination module 320 is used to determine a first target sequence value and a second target sequence value based on the evaluation data. The first target sequence value is determined based on at least one of the current sequence and the voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and the ambient temperature.

[0107] The state calibration module 330 is used to perform a state calibration operation based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence.

[0108] The filtering module 340 is used to select at least one special branch from the plurality of branches based on the first state sequence and the second state sequence;

[0109] The evaluation module 350 is used to evaluate the MPPT tracking performance of the at least one special branch and obtain the branch evaluation result.

[0110] In some implementations, the first target sequence value and the second target sequence value include multiple data points, and the data points of the first target sequence value correspond to the data points of the second target sequence value. The state calibration module 330 may include:

[0111] The acquisition submodule is used to acquire data points that are adjacent to and continuous with each of the data points in the first target sequence value and the second target sequence value;

[0112] A determination submodule is used to determine the state of each data point from the adjacent and continuous data points in order to implement the state calibration operation and obtain the first state sequence and the second state sequence.

[0113] In some implementations, the state of the branch includes an ascending state, a descending state, a stable state, a minimum state, or a maximum state.

[0114] In some embodiments, the photovoltaic system evaluation device 300 may further include:

[0115] The status acquisition module is used to acquire the status of the photovoltaic system;

[0116] The validity determination module is used to determine the validity of data in the first target sequence value and the second target sequence value when the state of the photovoltaic system is a specified state.

[0117] In some implementations, the state of the photovoltaic system includes at least one of the following: initialization state, light detection state, grid detection state, normal grid connection state, power-limited grid connection state, self-derating grid connection state, and shutdown state, wherein the specified state includes the normal grid connection state.

[0118] In some embodiments, the photovoltaic system evaluation device 300 may further include:

[0119] The fault determination module is used to determine whether the MPPT tracing of the at least one special branch is faulty based on the branch evaluation results.

[0120] In some implementations, the evaluation module 350 may include:

[0121] The evaluation submodule is used to evaluate the MPPT tracking performance of the at least one special branch using at least one of the state matching algorithm, power curve similarity algorithm and power generation index.

[0122] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0123] Figure 5 This is a block diagram illustrating an electronic device 700 according to an exemplary embodiment. Figure 5 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0124] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the photovoltaic system evaluation method described above. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0125] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the photovoltaic system evaluation method described above.

[0126] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the photovoltaic system evaluation method described above. For example, the computer-readable storage medium may be the memory 702 including program instructions described above, which may be executed by the processor 701 of the electronic device 700 to complete the photovoltaic system evaluation method described above.

[0127] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the photovoltaic system evaluation method described above when executed by the programmable device.

[0128] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0129] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0130] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A photovoltaic system evaluation method, characterized in that, The method includes: Obtain evaluation data of MPPT tracking performance, wherein the evaluation data includes at least one of the following: current sequence, voltage sequence, light intensity sequence and ambient temperature sequence corresponding to multiple branches; A first target sequence value and a second target sequence value are determined based on the evaluation data. The first target sequence value is determined based on at least one of the current sequence and the voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and the ambient temperature. A state calibration operation is performed based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence. The state of the branch includes rising state, falling state, stable state, minimum state or maximum state. Based on the first state sequence and the second state sequence, at least one special branch is selected from the plurality of branches according to the majority rule; The MPPT tracing performance of the at least one special branch is evaluated to obtain the branch evaluation result; The first target sequence value and the second target sequence value include multiple data points, and the data points of the first target sequence value correspond to the data points of the second target sequence value. The step of performing a state calibration operation based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence includes: Obtain the data points that are adjacent to and continuous with each of the data points in the first target sequence value and the second target sequence value; The state of each data point is determined by the adjacent and consecutive data points to realize the state calibration operation, thereby obtaining the first state sequence and the second state sequence.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the status of the photovoltaic system; When the photovoltaic system is in a specified state, the data in the first target sequence value and the second target sequence value are determined to be valid.

3. The method according to claim 2, characterized in that, The photovoltaic system's state includes at least one of the following: initialization state, light detection state, grid detection state, normal grid connection state, power-limited grid connection state, self-derating grid connection state, and shutdown state, with the specified state including the normal grid connection state.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Based on the branch evaluation results, determine whether the MPPT tracing of the at least one special branch is faulty.

5. The method according to any one of claims 1 to 3, characterized in that, The evaluation of the MPPT tracing performance of the at least one special branch includes: The MPPT tracking performance of the at least one special branch is evaluated using at least one of the state matching algorithm, power curve similarity algorithm, and power generation index.

6. A photovoltaic system evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire evaluation data of MPPT tracking effect, the evaluation data including at least one of current sequence, voltage sequence, light intensity sequence and ambient temperature sequence corresponding to multiple branches; A sequence value determination module is used to determine a first target sequence value and a second target sequence value based on the evaluation data, wherein the first target sequence value is determined based on at least one of the current sequence and the voltage sequence, and the second target sequence value is determined based on at least one of the light intensity and the ambient temperature; The state calibration module is used to perform a state calibration operation based on the first target sequence value and the second target sequence value to obtain a first state sequence and a second state sequence. The state of the branch includes rising state, falling state, stable state, minimum state or maximum state. The filtering module is used to select at least one special branch from the plurality of branches according to the majority rule based on the first state sequence and the second state sequence; An evaluation module is used to evaluate the MPPT tracing performance of the at least one special branch and obtain the branch evaluation result. The first target sequence value and the second target sequence value include multiple data points, and the data points of the first target sequence value correspond to the data points of the second target sequence value. The state calibration module includes: The acquisition submodule is used to acquire data points that are adjacent to and continuous with each of the data points in the first target sequence value and the second target sequence value; A determination submodule is used to determine the state of each data point from the adjacent and continuous data points in order to implement the state calibration operation and obtain the first state sequence and the second state sequence.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-5.

8. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-5.

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