Optimization method for working state of photovoltaic panel

Abstract

The invention discloses a method for optimizing the working state of a photovoltaic panel, and the method comprises the steps: determining state related data based on the correlation degree between a historical working demand instruction and historical operation data, and dividing the state related data into direct state data and indirect state data; acquiring a real-time working state instruction, real-time direct state data and real-time indirect state data to obtain a plurality of first prediction state data difference quantities and second prediction state data difference quantities; obtaining a third prediction state data difference quantity of the photovoltaic panel according to an image detection result of the photovoltaic panel; determining a working state coefficient according to the first prediction state data difference quantity, the second prediction state data difference quantity and the third prediction state data difference quantity, and configuring a first optimization scheme for the photovoltaic panel according to the working state coefficient; and obtaining a virtual working state coefficient according to the first optimization scheme, and judging whether to perform optimization again, thereby realizing performance optimization of the photovoltaic panel and the photovoltaic power generation system.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic panels, and in particular to a method for optimizing the working state of a photovoltaic panel. Background Art

[0002] Photovoltaic panels are key components for improving the efficiency and reliability of photovoltaic power generation systems. Current photovoltaic power stations are composed of many photovoltaic panels. However, due to the large amount of photovoltaic panel data, the accuracy of judging the working status of photovoltaic panels is reduced, resulting in reduced power generation, which in turn affects the efficiency and life of the entire photovoltaic power generation system. Therefore, how to accurately judge the working status of photovoltaic panels and formulate reasonable optimization plans to improve the performance of photovoltaic power generation systems is a technical problem that urgently needs to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a method for optimizing the working state of a photovoltaic panel. By comprehensively analyzing the direct state data, indirect state data and shading information of the photovoltaic panel, the working state coefficient of the photovoltaic panel can be accurately judged, and a reasonable optimization plan can be formulated to achieve performance optimization of the photovoltaic panel and the photovoltaic power generation system.

[0004] In some embodiments of the present application, a method for optimizing the working state of a photovoltaic panel is provided, comprising:

[0005] Obtain historical work demand instructions and corresponding historical operation data of the photovoltaic panel, determine state-related data of each historical work demand instruction based on the degree of correlation between the historical work demand instructions and the historical operation data, and divide the state-related data into direct state data and indirect state data;

[0006] Acquire the real-time working state instruction, real-time direct state data and real-time indirect state data of the photovoltaic panel, and compare them with the preset state data interval to obtain a plurality of first predicted state data difference amounts and second predicted state data difference amounts;

[0007] Determine the panel shading condition of the photovoltaic panel according to the image detection result of the photovoltaic panel, and compare it with the standard panel condition of the current real-time work demand instruction to obtain the third predicted state data difference of the photovoltaic panel;

[0008] Determine the working state coefficient of the photovoltaic panel according to the first predicted state data difference amount, the second predicted state data difference amount and the third predicted state data difference amount, judge whether optimization is needed according to the working state coefficient, and if necessary, configure the first optimization scheme for the photovoltaic panel;

[0009] The first optimization scheme is simulated, and the virtual working state coefficient is determined according to the simulation result. It is determined whether to perform optimization again according to the virtual working state coefficient. If necessary, a second optimization scheme is generated.

[0010] In some embodiments of the present application, based on the degree of association between the historical work demand instructions and the historical operation data, the state-related data of each historical work demand instruction is determined and divided into direct state data and indirect state data, including:

[0011] Obtaining historical work demand instructions of the photovoltaic panel and corresponding historical operation logs, wherein the historical operation logs include historical operation data within a historical period and historical evaluation values ​​of work status evaluation indicators;

[0012] A time reference line is established based on the historical period of each historical operation log, and a collection node is set according to a preset time interval. The historical operation data and historical evaluation values ​​in the corresponding historical period are obtained according to the collection node, and mapped to the corresponding time reference line to obtain a change relationship curve of each historical operation log;

[0013] The change relationship between the historical operation data and the historical evaluation value at each acquisition node in the change relationship curve is obtained to obtain the relative fluctuation degree of the historical evaluation value to the change of the historical operation data, and the number of acquisition nodes with a relative fluctuation degree greater than a preset relative fluctuation degree threshold and the relative fluctuation degree difference with a relative fluctuation degree greater than the preset relative fluctuation degree threshold are used to generate the correlation coefficient between the corresponding historical operation data and the working status evaluation index;

[0014] The historical operation data with a correlation coefficient greater than a preset correlation coefficient is set as the pending operation data of the historical work demand instruction corresponding to the historical operation log, the same pending operation data of different historical operation logs of the same historical work demand instruction is analyzed to obtain the appearance ratio of each pending operation data, and the pending operation data with an appearance ratio greater than a preset ratio threshold is set as the state-related data corresponding to the historical work demand instruction;

[0015] The state-related data of each historical work demand instruction is divided according to its data source to obtain direct state data and indirect state data.

[0016] In some embodiments of the present application, obtaining a plurality of first predicted state data difference amounts and second predicted state data difference amounts includes:

[0017] Obtaining real-time work demand instructions of photovoltaic panels and real-time direct status data and real-time indirect status data within a preset period;

[0018] Based on the preset monitoring accuracy, multiple preset acquisition nodes within a preset time period are set, and the real-time direct state data and the real-time indirect state data at each preset acquisition node are compared with the standard direct state data interval and the standard indirect state data interval at the corresponding preset acquisition node, respectively, to obtain the first predicted state data difference amount and the second state data difference amount at the multiple preset acquisition nodes within the preset time period;

[0019] The plurality of first predicted state data difference quantities and the plurality of second state data difference quantities are sorted respectively according to the time sequence of the preset acquisition nodes in the preset time period to obtain the first predicted state data difference quantity sequence and the second predicted state data difference quantity sequence within the preset time period.

[0020] In some embodiments of the present application, obtaining the third predicted state data difference of the photovoltaic panel includes:

[0021] The photovoltaic panel is divided into a plurality of detection sub-areas, and the actual image of the detection sub-area of ​​each preset acquisition node within a preset time period is obtained according to the image detection result, the actual image is preprocessed and the actual occlusion information is extracted, and the focus point of the corresponding detection sub-area is set according to the actual occlusion information;

[0022] Compare the actual occlusion information with the preset standard occlusion information of the corresponding detection sub-region, and generate an occlusion evaluation value for each detection sub-region according to the comparison result;

[0023] The calculation formula of the occlusion evaluation value is:

[0024]

[0025] Where P is the occlusion evaluation value, z is the occlusion evaluation conversion coefficient, u is the number of focus points in the detection sub-area, q1s is the grayscale value of the sth focus point in the actual occlusion information, q2s is the grayscale value of the sth focus point in the preset standard occlusion information, and k is (q1s-q2s) 2 The variance of

[0026] The third predicted state data difference amount of the photovoltaic panel within a preset time period is generated according to the occlusion evaluation values ​​of all detection sub-areas, and the third predicted state data difference amount is sorted according to the time sequence of the preset acquisition nodes to generate a third predicted state data difference amount sequence within the preset time period.

[0027] In some embodiments of the present application, determining the working state coefficient of the photovoltaic panel includes:

[0028] Divide the standard direct state data interval, the standard indirect state data interval, and the first preset state data difference interval, the second preset state data difference interval, and the third preset state data difference interval of the preset standard shading information according to the operation state stability factor and the working performance factor of the photovoltaic panel, obtain a plurality of first preset state data difference sub-intervals, second preset state data difference sub-intervals, and third preset state data difference sub-intervals, and configure corresponding first preset state coefficients, second preset state coefficients, and third preset state coefficients;

[0029] Determine a plurality of first preset state coefficients according to a relationship between a first predicted state data difference amount and a first preset state data difference sub-interval in a first predicted state data difference amount sequence;

[0030] Determine a plurality of second preset state coefficients according to a relationship between a second predicted state data difference amount and a second preset state data difference sub-interval in a second predicted state data difference amount sequence;

[0031] Determine a plurality of third preset state coefficients according to a relationship between a third predicted state data difference amount and a third preset state data difference sub-interval in a third predicted state data difference amount sequence;

[0032] Generate a working state coefficient of the photovoltaic panel according to a plurality of first preset state coefficients, second preset state coefficients, third preset state coefficients and a weight coefficient;

[0033] The calculation formula of the working state coefficient is:

[0034]

[0035] Among them, T is the working state coefficient, g1 is the weight coefficient corresponding to the direct state data, w is the number of preset acquisition nodes, a1v is the first preset state coefficient at the vth preset acquisition node, a2v is the second preset state coefficient at the vth preset acquisition node, and a3v is the third preset state coefficient at the vth preset acquisition node.

[0036] In some embodiments of the present application, a first optimization scheme is configured for a photovoltaic panel, including:

[0037] Determine a corresponding first optimization solution reference library, a second optimization solution reference library, and a third optimization solution reference library according to the real-time work demand instruction, wherein the first optimization solution reference library, the second optimization solution reference library, and the third optimization solution reference library respectively include a plurality of first preset state data difference quantity sequences, second preset state data difference quantity sequences, and third preset state data difference quantity sequences, and each of the first preset state data difference quantity sequence, the second preset state data difference quantity sequence, and the third preset state data difference quantity sequence is associated with a specific preset optimization solution;

[0038] Perform similarity analysis on the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence with the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence, respectively, and construct a first optimization solution set, a second optimization solution set and a third optimization solution set from the preset optimization solutions of the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence whose similarity is greater than a preset similarity threshold, respectively;

[0039] Obtain historical evaluation values ​​of the photovoltaic panel working state evaluation index after the preset optimization schemes in the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set, and set the preset optimization scheme with the largest historical evaluation value as the preferred optimization scheme corresponding to the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set;

[0040] The preferred optimization schemes of the first optimization scheme set, the second optimization scheme set and the third optimization scheme set are comprehensively analyzed, and the first optimization scheme is determined based on the principle of optimal performance of photovoltaic panels.

[0041] In some embodiments of the present application, the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence are respectively subjected to similarity analysis with a plurality of first preset state data difference amount sequences, second preset state data difference amount sequences and third preset state data difference amount sequences to obtain a difference amount similarity coefficient and a volatility similarity coefficient;

[0042] Generate similarity degree based on difference similarity coefficient and volatility similarity coefficient;

[0043] The calculation formula of the similarity is:

[0044]

[0045] Where Di,y is the similarity between the ith predicted state data difference sequence and the ith preset state data difference sequence, r1 is the weight coefficient of the difference similarity coefficient, △fv,i is the first predicted state data difference at the vth preset acquisition node in the ith predicted state data difference sequence, △f0v,i,y is the first preset state data difference at the vth preset acquisition node in the yth preset state data difference sequence, r2 is the volatility similarity coefficient, σ 2 ,i is the variance of the i-th prediction state data difference sequence, σ0 2i,y is the variance of the yth i-th preset state data difference sequence, i=1,2,3, y=1,2,…,ni,n is the number of preset state data difference sequences of the i-th predicted state data difference sequence.

[0046] In some embodiments of the present application, the first optimization scheme is simulated, and the virtual working state coefficient is determined according to the simulation result, including:

[0047] An operation simulation model of the photovoltaic panel is constructed based on the real-time operation condition of the photovoltaic panel and the surrounding environment parameters, and the operation simulation model is controlled according to the first optimization scheme to obtain a simulation result of the first optimization scheme;

[0048] The simulation result includes a number of first simulation direct state data, second simulation indirect state data and third simulation occlusion information, and calculates the corresponding first simulation state data difference amount, second simulation state data difference amount and third simulation state data difference amount;

[0049] The virtual working state coefficient is calculated according to the first simulation state data difference amount, the second simulation state data difference amount and the third simulation state data difference amount.

[0050] In some embodiments of the present application, whether to perform re-optimization is determined according to the virtual working state coefficient, and if necessary, a second optimization scheme is generated, including:

[0051] Generating an optimization efficiency of the first optimization solution according to a difference between the virtual working state coefficient and the working state coefficient and a corresponding application duration;

[0052] If the virtual working state coefficient is less than the preset working state coefficient threshold or the optimization efficiency is less than the preset optimization efficiency threshold, the first optimization scheme is optimized again and a second optimization scheme is generated;

[0053] If the virtual working state coefficient is greater than the preset working state coefficient threshold and the optimization efficiency is greater than the preset optimization efficiency threshold, the first optimization scheme is not optimized again, and the photovoltaic panel is regulated according to the first optimization scheme.

[0054] Compared with the prior art, the method for optimizing the working state of a photovoltaic panel in the embodiment of the present application has the following beneficial effects:

[0055] By comprehensively analyzing the direct status data, indirect status data and shading information of the photovoltaic panels, the working status coefficient of the photovoltaic panels can be accurately judged, and a reasonable optimization plan can be formulated to achieve performance optimization of the photovoltaic panels and photovoltaic power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1It is a flow chart of a method for optimizing the working state of a photovoltaic panel in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0057] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0058] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0059] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0060] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0061] like Figure 1 As shown, a method for optimizing the working state of a photovoltaic panel in a preferred embodiment of the present application includes:

[0062] Step S101: obtaining historical work demand instructions and corresponding historical operation data of the photovoltaic panel, and determining state-related data of each historical work demand instruction based on the degree of association between the historical work demand instructions and the historical operation data, and dividing the state-related data into direct state data and indirect state data;

[0063] Step S102: obtaining the real-time working state instruction, real-time direct state data and real-time indirect state data of the photovoltaic panel, and comparing them with the preset state data interval to obtain a plurality of first predicted state data difference amounts and second predicted state data difference amounts;

[0064] Step S103: determining the panel shielding condition of the photovoltaic panel according to the image detection result of the photovoltaic panel, and comparing it with the standard panel condition of the current real-time work demand instruction to obtain the third predicted state data difference amount of the photovoltaic panel;

[0065] Step S104: determining the working state coefficient of the photovoltaic panel according to the first prediction state data difference amount, the second prediction state data difference amount and the third prediction state data difference amount, judging whether optimization is needed according to the working state coefficient, and if so, configuring a first optimization scheme for the photovoltaic panel;

[0066] Step S105: simulate the first optimization scheme, determine the virtual working state coefficient according to the simulation result, and judge whether to optimize again according to the virtual working state coefficient. If necessary, generate a second optimization scheme.

[0067] In some embodiments of the present application, based on the degree of association between the historical work demand instructions and the historical operation data, the state-related data of each historical work demand instruction is determined and divided into direct state data and indirect state data, including:

[0068] Obtaining historical work demand instructions of the photovoltaic panel and corresponding historical operation logs, wherein the historical operation logs include historical operation data within a historical period and historical evaluation values ​​of work status evaluation indicators;

[0069] A time reference line is established based on the historical period of each historical operation log, and a collection node is set according to a preset time interval. The historical operation data and historical evaluation values ​​in the corresponding historical period are obtained according to the collection node, and mapped to the corresponding time reference line to obtain a change relationship curve of each historical operation log;

[0070] The change relationship between the historical operation data and the historical evaluation value at each acquisition node in the change relationship curve is obtained to obtain the relative fluctuation degree of the historical evaluation value to the change of the historical operation data, and the number of acquisition nodes with a relative fluctuation degree greater than a preset relative fluctuation degree threshold and the relative fluctuation degree difference with a relative fluctuation degree greater than the preset relative fluctuation degree threshold are used to generate the correlation coefficient between the corresponding historical operation data and the working status evaluation index;

[0071] The historical operation data with a correlation coefficient greater than a preset correlation coefficient is set as the pending operation data of the historical work demand instruction corresponding to the historical operation log, the same pending operation data of different historical operation logs of the same historical work demand instruction is analyzed to obtain the appearance ratio of each pending operation data, and the pending operation data with an appearance ratio greater than a preset ratio threshold is set as the state-related data corresponding to the historical work demand instruction;

[0072] The state-related data of each historical work demand instruction is divided according to its data source to obtain direct state data and indirect state data.

[0073] In this embodiment, the historical work demand instructions refer to the work instructions received by each photovoltaic panel, including maximum power point tracking, voltage instructions, response scheduling instructions, etc.

[0074] In this embodiment, the relative fluctuation degree refers to the fluctuation degree of the historical evaluation value with respect to the change of the historical operation data. The greater the degree of change of the historical evaluation value with the change of the historical operation data, the greater the relative fluctuation degree. The preset relative fluctuation degree threshold refers to the minimum fluctuation degree that can indicate that there is a correlation between the historical operation data and the historical evaluation value, and is set in advance.

[0075] In this embodiment, the occurrence ratio = the number of historical operation logs in which the same pending operation data appears in the same historical work requirement instruction / the total number of historical operation logs corresponding to the historical work requirement instruction. The preset ratio threshold refers to 1 / 3 of the total number of historical operation logs for each historical work requirement instruction.

[0076] In this embodiment, direct status data refers to data directly obtained from the photovoltaic panel's own parameters, and indirect status data refers to data indirectly obtained from the photovoltaic panel's own parameters.

[0077] In this embodiment, by analyzing the historical operation data of different historical work demand instructions, the state-related data with the historical work demand instructions is determined and divided into direct state data and indirect state data, that is, data that can directly and indirectly represent the state of the photovoltaic panel, thereby laying the foundation for the subsequent determination of the working state coefficient of the photovoltaic panel and the optimization plan, improving the accuracy of the judgment of the working state of the photovoltaic panel, and realizing the optimization of the working state of the photovoltaic panel.

[0078] In some embodiments of the present application, obtaining a plurality of first predicted state data difference amounts and second predicted state data difference amounts includes:

[0079] Obtaining real-time work demand instructions of photovoltaic panels and real-time direct status data and real-time indirect status data within a preset period;

[0080] Based on the preset monitoring accuracy, multiple preset acquisition nodes within a preset time period are set, and the real-time direct state data and the real-time indirect state data at each preset acquisition node are compared with the standard direct state data interval and the standard indirect state data interval at the corresponding preset acquisition node, respectively, to obtain the first predicted state data difference amount and the second state data difference amount at the multiple preset acquisition nodes within the preset time period;

[0081] The plurality of first predicted state data difference quantities and the plurality of second state data difference quantities are sorted respectively according to the time sequence of the preset acquisition nodes in the preset time period to obtain the first predicted state data difference quantity sequence and the second predicted state data difference quantity sequence within the preset time period.

[0082] In this embodiment, the standard direct status data interval and the standard indirect status data interval are set according to the historical evaluation values ​​of the working status evaluation indicators corresponding to the direct status data and the indirect status data in the historical operation log of the real-time work demand instructions. According to the rising trend of the historical evaluation values ​​and the direct status data and indirect status data that meet the preset evaluation value threshold, multiple standard direct status data intervals and standard indirect status data intervals are set. According to the real-time work demand instructions and the operating conditions and operating time of the photovoltaic panels, the standard direct status data intervals and the standard indirect status data intervals are configured at the corresponding preset collection nodes.

[0083] In this embodiment, the real-time direct state data and the real-time indirect state data are compared with the standard direct state data interval and the standard indirect state data interval at the corresponding preset acquisition node, respectively, to obtain a first predicted state data difference sequence and a second predicted state data difference sequence, thereby laying a foundation for the subsequent determination of the first optimization scheme for the photovoltaic panel, improving the rationality and effectiveness of the optimization scheme, and achieving performance optimization of the photovoltaic panel and the photovoltaic power generation system.

[0084] In some embodiments of the present application, obtaining the third predicted state data difference of the photovoltaic panel includes:

[0085] The photovoltaic panel is divided into a plurality of detection sub-areas, and the actual image of the detection sub-area of ​​each preset acquisition node within a preset time period is obtained according to the image detection result, the actual image is preprocessed and the actual occlusion information is extracted, and the focus point of the corresponding detection sub-area is set according to the actual occlusion information;

[0086] Compare the actual occlusion information with the preset standard occlusion information of the corresponding detection sub-region, and generate an occlusion evaluation value for each detection sub-region according to the comparison result;

[0087] The calculation formula of the occlusion evaluation value is:

[0088]

[0089] Where P is the occlusion evaluation value, z is the occlusion evaluation conversion coefficient, u is the number of focus points in the detection sub-area, q1s is the grayscale value of the sth focus point in the actual occlusion information, q2s is the grayscale value of the sth focus point in the preset standard occlusion information, and k is (q1s-q2s) 2 The variance of

[0090] The third predicted state data difference amount of the photovoltaic panel within a preset time period is generated according to the occlusion evaluation values ​​of all detection sub-areas, and the third predicted state data difference amount is sorted according to the time sequence of the preset acquisition nodes to generate a third predicted state data difference amount sequence within the preset time period.

[0091] In this embodiment, the occlusion evaluation value refers to the degree of occlusion difference between the actual occlusion information and the preset standard occlusion information of the corresponding detection sub-area. The larger the occlusion evaluation value, the smaller the occlusion difference, that is, the smaller the difference between the actual occlusion information and the preset standard occlusion information, the greater the power generation efficiency of the photovoltaic panel.

[0092] In some embodiments of the present application, determining the working state coefficient of the photovoltaic panel includes:

[0093] Divide the standard direct state data interval, the standard indirect state data interval, and the first preset state data difference interval, the second preset state data difference interval, and the third preset state data difference interval of the preset standard shading information according to the operation state stability factor and the working performance factor of the photovoltaic panel, obtain a plurality of first preset state data difference sub-intervals, second preset state data difference sub-intervals, and third preset state data difference sub-intervals, and configure corresponding first preset state coefficients, second preset state coefficients, and third preset state coefficients;

[0094] Determine a plurality of first preset state coefficients according to a relationship between a first predicted state data difference amount and a first preset state data difference sub-interval in a first predicted state data difference amount sequence;

[0095] Determine a plurality of second preset state coefficients according to a relationship between a second predicted state data difference amount and a second preset state data difference sub-interval in a second predicted state data difference amount sequence;

[0096] Determine a plurality of third preset state coefficients according to a relationship between a third predicted state data difference amount and a third preset state data difference sub-interval in a third predicted state data difference amount sequence;

[0097] Generate a working state coefficient of the photovoltaic panel according to a plurality of first preset state coefficients, second preset state coefficients, third preset state coefficients and a weight coefficient;

[0098] The calculation formula of the working state coefficient is:

[0099]

[0100] Among them, T is the working state coefficient, g1 is the weight coefficient corresponding to the direct state data, w is the number of preset acquisition nodes, a1v is the first preset state coefficient at the vth preset acquisition node, a2v is the second preset state coefficient at the vth preset acquisition node, and a3v is the third preset state coefficient at the vth preset acquisition node.

[0101] In this embodiment, the first preset state coefficient refers to the first preset state coefficient of multiple real-time direct state data at each preset acquisition node obtained by averaging, the second preset state coefficient refers to the second preset state coefficient of multiple real-time indirect state data at each preset acquisition node obtained by averaging, and the third preset state coefficient refers to the third preset state coefficient of multiple real-time occlusion information at each preset acquisition node obtained by averaging.

[0102] In this embodiment, by comprehensively analyzing the direct state data, indirect state data and shading information of the photovoltaic panel, the working state coefficient of the photovoltaic panel is accurately determined, laying the foundation for the subsequent generation of the first optimization plan and improving the performance of the photovoltaic power generation system.

[0103] In some embodiments of the present application, a first optimization scheme is configured for a photovoltaic panel, including:

[0104] Determine a corresponding first optimization solution reference library, a second optimization solution reference library, and a third optimization solution reference library according to the real-time work demand instruction, wherein the first optimization solution reference library, the second optimization solution reference library, and the third optimization solution reference library respectively include a plurality of first preset state data difference quantity sequences, second preset state data difference quantity sequences, and third preset state data difference quantity sequences, and each of the first preset state data difference quantity sequence, the second preset state data difference quantity sequence, and the third preset state data difference quantity sequence is associated with a specific preset optimization solution;

[0105] Perform similarity analysis on the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence with the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence, respectively, and construct a first optimization solution set, a second optimization solution set and a third optimization solution set from the preset optimization solutions of the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence whose similarity is greater than a preset similarity threshold, respectively;

[0106] Obtain historical evaluation values ​​of the photovoltaic panel working state evaluation index after the preset optimization schemes in the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set, and set the preset optimization scheme with the largest historical evaluation value as the preferred optimization scheme corresponding to the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set;

[0107] The preferred optimization schemes of the first optimization scheme set, the second optimization scheme set and the third optimization scheme set are comprehensively analyzed, and the first optimization scheme is determined based on the principle of optimal performance of photovoltaic panels.

[0108] In this embodiment, the first optimization solution reference library, the second optimization solution reference library, and the third optimization solution reference library are constructed based on historical optimization solutions corresponding to different work requirement instructions and preset state data difference amounts.

[0109] In this embodiment, the first optimization scheme set, the second optimization scheme set and the third optimization scheme set are determined by the working state coefficient of the photovoltaic panel, and the corresponding preferred optimization schemes are screened out to achieve the optimization of the performance of the photovoltaic panel and the photovoltaic power generation system.

[0110] In some embodiments of the present application, the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence are respectively subjected to similarity analysis with a plurality of first preset state data difference amount sequences, second preset state data difference amount sequences and third preset state data difference amount sequences to obtain a difference amount similarity coefficient and a volatility similarity coefficient;

[0111] Generate similarity degree based on difference similarity coefficient and volatility similarity coefficient;

[0112] The calculation formula of the similarity is:

[0113]

[0114] Where Di,y is the similarity between the ith predicted state data difference sequence and the ith preset state data difference sequence, r1 is the weight coefficient of the difference similarity coefficient, △fv,i is the first predicted state data difference at the vth preset acquisition node in the ith predicted state data difference sequence, △f0v,i,y is the first preset state data difference at the vth preset acquisition node in the yth preset state data difference sequence, r2 is the volatility similarity coefficient, σ 2 ,i is the variance of the i-th prediction state data difference sequence, σ0 2 i,y is the variance of the yth i-th preset state data difference sequence, i=1,2,3, y=1,2,…,ni,n is the number of preset state data difference sequences of the i-th predicted state data difference sequence.

[0115] In this embodiment, by calculating the first predicted state data difference sequence, the second predicted state data difference sequence and the third predicted state data difference sequence respectively with multiple first preset state data difference sequences, the second preset state data difference sequences and the third preset state data difference sequences, a reasonable preset optimization scheme is screened out to achieve performance optimization of photovoltaic panels and photovoltaic power generation systems.

[0116] In some embodiments of the present application, the first optimization scheme is simulated, and the virtual working state coefficient is determined according to the simulation result, including:

[0117] An operation simulation model of the photovoltaic panel is constructed based on the real-time operation condition of the photovoltaic panel and the surrounding environment parameters, and the operation simulation model is controlled according to the first optimization scheme to obtain a simulation result of the first optimization scheme;

[0118] The simulation result includes a number of first simulation direct state data, second simulation indirect state data and third simulation occlusion information, and calculates the corresponding first simulation state data difference amount, second simulation state data difference amount and third simulation state data difference amount;

[0119] The virtual working state coefficient is calculated according to the first simulation state data difference amount, the second simulation state data difference amount and the third simulation state data difference amount.

[0120] In some embodiments of the present application, whether to perform re-optimization is determined according to the virtual working state coefficient, and if necessary, a second optimization scheme is generated, including:

[0121] Generating an optimization efficiency of the first optimization solution according to a difference between the virtual working state coefficient and the working state coefficient and a corresponding application duration;

[0122] If the virtual working state coefficient is less than the preset working state coefficient threshold or the optimization efficiency is less than the preset optimization efficiency threshold, the first optimization scheme is optimized again and a second optimization scheme is generated;

[0123] If the virtual working state coefficient is greater than the preset working state coefficient threshold and the optimization efficiency is greater than the preset optimization efficiency threshold, the first optimization scheme is not optimized again, and the photovoltaic panel is regulated according to the first optimization scheme.

[0124] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing the working state of a photovoltaic panel, characterized in that: include: Obtain historical work demand instructions and corresponding historical operation data of the photovoltaic panel, determine state-related data of each historical work demand instruction based on the degree of correlation between the historical work demand instructions and the historical operation data, and divide the state-related data into direct state data and indirect state data; Acquire the real-time working state instruction, real-time direct state data and real-time indirect state data of the photovoltaic panel, and compare them with the preset state data interval to obtain a plurality of first predicted state data difference amounts and second predicted state data difference amounts; Determine the panel shading condition of the photovoltaic panel according to the image detection result of the photovoltaic panel, and compare it with the standard panel condition of the current real-time work demand instruction to obtain the third predicted state data difference of the photovoltaic panel; Determine the working state coefficient of the photovoltaic panel according to the first predicted state data difference amount, the second predicted state data difference amount and the third predicted state data difference amount, judge whether optimization is needed according to the working state coefficient, and if necessary, configure the first optimization scheme for the photovoltaic panel; The first optimization scheme is simulated, and the virtual working state coefficient is determined according to the simulation result. It is determined whether to perform optimization again according to the virtual working state coefficient. If necessary, a second optimization scheme is generated.

2. The method for optimizing the working state of a photovoltaic panel according to claim 1, characterized in that: Based on the degree of association between the historical work demand instructions and the historical operation data, the state-related data of each historical work demand instruction is determined and divided into direct state data and indirect state data, including: Obtaining historical work demand instructions of the photovoltaic panel and corresponding historical operation logs, wherein the historical operation logs include historical operation data within a historical period and historical evaluation values ​​of work status evaluation indicators; A time reference line is established based on the historical period of each historical operation log, and a collection node is set according to a preset time interval. The historical operation data and historical evaluation values ​​in the corresponding historical period are obtained according to the collection node, and mapped to the corresponding time reference line to obtain a change relationship curve of each historical operation log; The change relationship between the historical operation data and the historical evaluation value at each acquisition node in the change relationship curve is obtained to obtain the relative fluctuation degree of the historical evaluation value to the change of the historical operation data, and the number of acquisition nodes with a relative fluctuation degree greater than a preset relative fluctuation degree threshold and the relative fluctuation degree difference with a relative fluctuation degree greater than the preset relative fluctuation degree threshold are used to generate the correlation coefficient between the corresponding historical operation data and the working status evaluation index; The historical operation data with a correlation coefficient greater than a preset correlation coefficient is set as the pending operation data of the historical work demand instruction corresponding to the historical operation log, the same pending operation data of different historical operation logs of the same historical work demand instruction is analyzed to obtain the appearance ratio of each pending operation data, and the pending operation data with an appearance ratio greater than a preset ratio threshold is set as the state-related data corresponding to the historical work demand instruction; The state-related data of each historical work demand instruction is divided according to its data source to obtain direct state data and indirect state data.

3. The method for optimizing the working state of a photovoltaic panel according to claim 2, characterized in that: Obtaining a plurality of first prediction state data difference amounts and second prediction state data difference amounts, including: Obtaining real-time work demand instructions of photovoltaic panels and real-time direct status data and real-time indirect status data within a preset period; Based on the preset monitoring accuracy, multiple preset acquisition nodes within a preset time period are set, and the real-time direct state data and the real-time indirect state data at each preset acquisition node are compared with the standard direct state data interval and the standard indirect state data interval at the corresponding preset acquisition node, respectively, to obtain the first predicted state data difference amount and the second state data difference amount at the multiple preset acquisition nodes within the preset time period; The plurality of first predicted state data difference quantities and the plurality of second state data difference quantities are sorted respectively according to the time sequence of the preset acquisition nodes in the preset time period to obtain the first predicted state data difference quantity sequence and the second predicted state data difference quantity sequence within the preset time period.

4. The method for optimizing the working state of a photovoltaic panel according to claim 3, characterized in that: The third predicted state data difference of the photovoltaic panel is obtained, including: The photovoltaic panel is divided into a plurality of detection sub-areas, and the actual image of the detection sub-area of ​​each preset acquisition node within a preset time period is obtained according to the image detection result, the actual image is preprocessed and the actual occlusion information is extracted, and the focus point of the corresponding detection sub-area is set according to the actual occlusion information; Compare the actual occlusion information with the preset standard occlusion information of the corresponding detection sub-region, and generate an occlusion evaluation value for each detection sub-region according to the comparison result; The calculation formula of the occlusion evaluation value is: Where P is the occlusion evaluation value, z is the occlusion evaluation conversion coefficient, u is the number of focus points in the detection sub-area, q1s is the grayscale value of the sth focus point in the actual occlusion information, q2s is the grayscale value of the sth focus point in the preset standard occlusion information, and k is (q1s-q2s) 2 The variance of The third predicted state data difference amount of the photovoltaic panel within a preset time period is generated according to the occlusion evaluation values ​​of all detection sub-areas, and the third predicted state data difference amount is sorted according to the time sequence of the preset acquisition nodes to generate a third predicted state data difference amount sequence within the preset time period.

5. The method for optimizing the working state of a photovoltaic panel according to claim 4, characterized in that: Determine the working state coefficient of the photovoltaic panel, including: Divide the standard direct state data interval, the standard indirect state data interval, and the first preset state data difference interval, the second preset state data difference interval, and the third preset state data difference interval of the preset standard shading information according to the operation state stability factor and the working performance factor of the photovoltaic panel, obtain a plurality of first preset state data difference sub-intervals, second preset state data difference sub-intervals, and third preset state data difference sub-intervals, and configure corresponding first preset state coefficients, second preset state coefficients, and third preset state coefficients; Determine a plurality of first preset state coefficients according to a relationship between a first predicted state data difference amount and a first preset state data difference sub-interval in a first predicted state data difference amount sequence; Determine a plurality of second preset state coefficients according to a relationship between a second predicted state data difference amount and a second preset state data difference sub-interval in a second predicted state data difference amount sequence; Determine a plurality of third preset state coefficients according to a relationship between a third predicted state data difference amount and a third preset state data difference sub-interval in a third predicted state data difference amount sequence; Generate a working state coefficient of the photovoltaic panel according to a plurality of first preset state coefficients, second preset state coefficients, third preset state coefficients and a weight coefficient; The calculation formula of the working state coefficient is: Among them, T is the working state coefficient, g1 is the weight coefficient corresponding to the direct state data, w is the number of preset acquisition nodes, a1v is the first preset state coefficient at the vth preset acquisition node, a2v is the second preset state coefficient at the vth preset acquisition node, and a3v is the third preset state coefficient at the vth preset acquisition node.

6. The method for optimizing the working state of a photovoltaic panel according to claim 5, characterized in that: The first optimization scheme for configuring photovoltaic panels includes: Determine a corresponding first optimization solution reference library, a second optimization solution reference library, and a third optimization solution reference library according to the real-time work demand instruction, wherein the first optimization solution reference library, the second optimization solution reference library, and the third optimization solution reference library respectively include a plurality of first preset state data difference quantity sequences, second preset state data difference quantity sequences, and third preset state data difference quantity sequences, and each of the first preset state data difference quantity sequence, the second preset state data difference quantity sequence, and the third preset state data difference quantity sequence is associated with a specific preset optimization solution; Perform similarity analysis on the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence with the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence, respectively, and construct a first optimization solution set, a second optimization solution set and a third optimization solution set from the preset optimization solutions of the first preset state data difference amount sequence, the second preset state data difference amount sequence and the third preset state data difference amount sequence whose similarity is greater than a preset similarity threshold, respectively; Obtain historical evaluation values ​​of the photovoltaic panel working state evaluation index after the preset optimization schemes in the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set, and set the preset optimization scheme with the largest historical evaluation value as the preferred optimization scheme corresponding to the first optimization scheme set, the second optimization scheme set, and the third optimization scheme set; The preferred optimization schemes of the first optimization scheme set, the second optimization scheme set and the third optimization scheme set are comprehensively analyzed, and the first optimization scheme is determined based on the principle of optimal performance of photovoltaic panels.

7. The method for optimizing the working state of a photovoltaic panel according to claim 6, characterized in that: Perform similarity analysis on the first predicted state data difference amount sequence, the second predicted state data difference amount sequence and the third predicted state data difference amount sequence with a plurality of first preset state data difference amount sequences, the second preset state data difference amount sequences and the third preset state data difference amount sequences respectively, to obtain a difference amount similarity coefficient and a volatility similarity coefficient; Generate similarity degree based on difference similarity coefficient and volatility similarity coefficient; The calculation formula of the similarity is: Where Di,y is the similarity between the ith predicted state data difference sequence and the ith preset state data difference sequence, r1 is the weight coefficient of the difference similarity coefficient, △fv,i is the first predicted state data difference at the vth preset acquisition node in the ith predicted state data difference sequence, △f0v,i,y is the first preset state data difference at the vth preset acquisition node in the yth preset state data difference sequence, r2 is the volatility similarity coefficient, σ 2 ,i is the variance of the i-th prediction state data difference sequence, σ0 2 i,y is the variance of the yth i-th preset state data difference sequence, i=1,2,3, y=1,2,…,ni,n is the number of preset state data difference sequences of the i-th predicted state data difference sequence.

8. The method for optimizing the working state of a photovoltaic panel according to claim 7, characterized in that: The first optimization scheme is simulated, and the virtual working state coefficient is determined according to the simulation result, including: An operation simulation model of the photovoltaic panel is constructed based on the real-time operation condition of the photovoltaic panel and the surrounding environment parameters, and the operation simulation model is controlled according to the first optimization scheme to obtain a simulation result of the first optimization scheme; The simulation result includes a number of first simulation direct state data, second simulation indirect state data and third simulation occlusion information, and calculates the corresponding first simulation state data difference amount, second simulation state data difference amount and third simulation state data difference amount; The virtual working state coefficient is calculated according to the first simulation state data difference amount, the second simulation state data difference amount and the third simulation state data difference amount.

9. The method for optimizing the working state of a photovoltaic panel according to claim 8, characterized in that: Determine whether to perform re-optimization according to the virtual working state coefficient, and if necessary, generate a second optimization plan, including: Generating an optimization efficiency of the first optimization solution according to a difference between the virtual working state coefficient and the working state coefficient and a corresponding application duration; If the virtual working state coefficient is less than the preset working state coefficient threshold or the optimization efficiency is less than the preset optimization efficiency threshold, the first optimization scheme is optimized again and a second optimization scheme is generated; If the virtual working state coefficient is greater than the preset working state coefficient threshold and the optimization efficiency is greater than the preset optimization efficiency threshold, the first optimization scheme is not optimized again, and the photovoltaic panel is regulated according to the first optimization scheme.

Images