A method and system for optimizing power generation of solar photovoltaic panels

Abstract

The present invention relates to the field of maximum power point tracking technology, specifically a method and system for optimizing the power generation of solar photovoltaic panels, comprising the following steps: obtaining a power sequence of a photovoltaic panel branch, constructing a direction change matrix, identifying segments of same-direction change and extracting differences, generating a trend annotation table, comparing power changes to screen adjustment nodes, sending adjustment instructions and recording differences, and generating an optimized response table. In the present invention, by regularly collecting the voltage and current of the photovoltaic branch, a power change trend matrix is ​​constructed, the power change direction at adjacent moments is extracted, trend identification and change modeling are achieved, segments with persistence and significant amplitude changes are screened, occasional disturbances are eliminated, and the accuracy of adjustment judgment is improved. The trend segments are compared with the power tracking records, nodes with improvement potential are selected to issue adjustment instructions, and response feedback is recorded. A quantitative threshold and feedback mechanism are introduced to improve the maximum power point identification accuracy and the power generation efficiency of the system under environmental fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of maximum power point tracking, and in particular to a method and system for optimizing the power generation of a solar photovoltaic panel. Background Art

[0002] The field of maximum power point tracking (MPPT) encompasses the study of power output control methods within solar photovoltaic (PV) power generation systems. Its core focus is to analyze the relationship between the output voltage and current of solar panels to identify and maintain their operating point at maximum power output in real time. This technical area encompasses power collection, voltage and current regulation, MPP identification strategies, and control strategy execution mechanisms for solar PV modules. It is typically applied to devices such as PV inverters and DC converters to improve overall system power generation efficiency and response stability. MPPT techniques, including those based on perturbation-observation methods, conductance increment methods, and voltage feedback methods, achieve dynamic tracking by continuously adjusting operating parameters.

[0003] Among them, the solar photovoltaic panel power generation power optimization method refers to collecting the operating voltage and operating current data of the solar photovoltaic components, calculating the power change rate within a specific time period, and determining the deviation trend of the current power point in combination with the preset judgment logic. This method specifically covers signal processing at the acquisition end, power calculation formula operation, power point direction judgment and instruction output. Ultimately, the instruction is input into the execution unit in the form of voltage or current adjustment to achieve dynamic adjustment of the photovoltaic cell array. In addition, by periodically comparing the current power with the historical maximum power value, combined with the set threshold, it is determined whether to enter the next round of power optimization process. The overall power optimization process is completed based on quantitative calculation and judgment rules as the core basis.

[0004] Existing technologies primarily rely on single-point, instantaneous determinations of the relationship between solar panel voltage and current, often achieving real-time tracking of the maximum power point through methods such as perturbation observation and conductance increments. In dynamic environments, such as rapidly changing cloud cover or sudden temperature changes, these technologies lack the ability to continuously monitor power trends. Adjustment decisions are often based on instantaneous data, making them susceptible to random fluctuations, leading to frequent misadjustments or tracking errors, limiting system stability. At the decision logic level, these technologies primarily rely on directional judgments based on changes before and after a single sampling point. They lack a quantitative criterion for power trends within a time series and are unable to effectively identify time periods with sustainable improvement potential. Regarding control output, the lack of a mechanism to archive power differences before and after adjustments makes it difficult to provide systematic feedback on the effectiveness of optimization actions and to establish a dynamic optimization path for adjustment strategies based on historical data. For example, in typical intermittently cloudy weather, existing technologies may frequently make adjustments based on short-term changes in sunlight, potentially causing system oscillations and reducing overall power generation efficiency. The lack of a mechanism to model the linkage between trend continuity and response effectiveness is a key shortcoming of existing technologies in highly volatile scenarios. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for optimizing the power generation of solar photovoltaic panels.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for optimizing the power generation of solar photovoltaic panels, comprising the following steps:

[0007] S1: Obtain the branch voltage and current signals of the solar photovoltaic panel connected to the DC combiner box, record the instantaneous power at fixed intervals through the photovoltaic array sub-area monitoring terminal, construct a branch continuous power sequence, and generate the photovoltaic panel branch power sequence data structure;

[0008] S2: Based on the photovoltaic panel branch power sequence data structure, extract the power values ​​at adjacent time points, calculate the branch change direction, construct a direction change matrix with branch number and time node as dimensions, and generate a photovoltaic panel branch direction change matrix;

[0009] S3: Based on the photovoltaic panel branch direction change matrix, identify the continuous same-direction change segments in the branch, extract the first and last power values ​​to calculate the difference, select the segments that meet the change characteristics, and generate a trend change segment annotation table;

[0010] S4: calling the paragraph range in the trend change segment annotation table, comparing the power output change in the corresponding time period in the maximum power point tracking control loop, selecting the starting time of significant power improvement, and generating a trend guidance adjustment node list;

[0011] S5: Send voltage adjustment instructions to the string inverter according to the trend-guided adjustment node list, record the power difference before and after adjustment, mark the segment number where the power change exceeds the threshold, and generate a photovoltaic segment optimization response record table.

[0012] As a further solution of the present invention, the photovoltaic panel branch power sequence data structure includes branch voltage signals, branch current signals, instantaneous power record values, and continuous power sequences; the photovoltaic panel branch direction change matrix includes branch number dimension, time node dimension, and power change direction value; the trend change segment marking table includes the start time of the same-direction change segment, the end time of the same-direction change segment, the head and tail power difference, the duration mark, and the amplitude threshold mark; the trend-guided adjustment node list includes the power boost start time, the power boost end time, the boost amplitude record value, and the corresponding branch number; the photovoltaic segment optimization response record table includes the adjustment instruction timestamp, the operating voltage adjustment amount, the power difference record, the response threshold compliance mark, and the segment number index.

[0013] As a further solution of the present invention, the specific steps of S1 are:

[0014] S101: Obtain voltage and current signals of the branch connected to the solar photovoltaic panel, combine them with the synchronous measurement data of the combiner box acquisition channel, combine the signals at the same time point into a power information sequence, and obtain the branch instantaneous power sequence;

[0015] S102: calling the instantaneous power sequence of the branch, extracting the equally spaced data within a unit period according to the interval parameter set by the monitoring terminal, arranging and combining them according to the time index to form an ordered set, and obtaining a periodic power position sequence;

[0016] S103: Based on the periodic power position sequence, the branch number information is associated, the power data and number relationship is organized according to the mapping structure, an ordered index corresponding to the branch is established, and a branch power sequence data structure is generated.

[0017] As a further solution of the present invention, the specific steps of S2 are:

[0018] S201: Based on the photovoltaic panel branch power sequence data structure, extract the power values ​​of the branches at adjacent time points, construct corresponding power value combinations according to the branch numbers, and organize them into data groups arranged in chronological order to generate a power difference value set at adjacent time points;

[0019] S202: Based on the change trend of the power values ​​of the concentrated group of power difference values ​​at adjacent time points, state classification is performed according to the direction of the difference, the change state is marked by combining the branch number and the position index of the time node, the change information of all branches is summarized, and a direction change state mapping matrix is ​​generated;

[0020] S203: Call the position change status in the direction change status mapping matrix, reorganize the status content in the order of branch numbers as rows and time nodes as columns, calculate the direction change amount, uniformly convert it into direction change code, and establish a branch direction change matrix.

[0021] As a further solution of the present invention, the specific calculation formula for calculating the direction change is:

[0022] ;

[0023] in, Represents the direction change of the i-th branch at the j-th time node, represents the yth weight factor of the i-th branch, represents the yth direction angle at the jth time node, represents the lateral regulation coefficient of the i-th branch, represents the longitudinal adjustment coefficient at the jth time node, represents the time decay factor, Represents the timestamp of the j-th time node, Represents the base timestamp, Represents the total number of weight factors.

[0024] As a further solution of the present invention, the specific steps of S3 are:

[0025] S301: identifying continuous sections with consistent directions in the branches according to the photovoltaic panel branch direction change matrix, merging time periods with consistent directions, and generating continuous change intervals in the same direction;

[0026] S302: Calling the same-direction continuous change interval, extracting the start and end power values ​​of each segment, determining whether the difference exceeds a set amplitude threshold, marking the segments that meet the condition, and generating a power change mark value;

[0027] S303: extracting the corresponding time interval and power characteristics according to the power change mark value, summarizing the segment number, start and end time and power information annotation data, and generating a trend change segment annotation table.

[0028] As a further solution of the present invention, the specific steps of S4 are:

[0029] S401: Based on the paragraph range in the trend change segment annotation table, extract the time interval corresponding to the paragraph, call the power output value recorded by the maximum power point tracking control loop, compare the change characteristics of the power output during the same period, and generate the segment power change amplitude;

[0030] S402: Identifying, based on the power variation amplitude of the paragraphs, paragraphs whose power variation amplitude exceeds a target variation benchmark, extracting corresponding paragraph start times, and obtaining a set of power variation sudden increase segment start times;

[0031] S403: Call the time information in the starting time set of the power change sudden increase segment, detect the changing trend of the power output in the corresponding segment, locate the key time point with the strongest trend change, and establish a trend guidance adjustment node list.

[0032] As a further solution of the present invention, the specific calculation formula for detecting the change trend of power output in the corresponding section is:

[0033] ;

[0034] Calculate the comprehensive change trend strength value and establish a trend guidance adjustment node list;

[0035] in, A comprehensive quantitative indicator that represents the intensity of power change trend, Indicates the starting time of the kth burst The power change rate, represents the time interval of the k-th burst segment, express The power change rate of the adjacent time period is express The power change rate of the previous adjacent time period, is the total number of burst segments, A very small constant to prevent the denominator from being zero.

[0036] As a further solution of the present invention, the specific steps of S5 are:

[0037] S501: Based on the trend-guided regulation node list, identify the operating voltage value and time information of the corresponding string inverter, determine whether the regulation conditions are met, send a voltage adjustment instruction to the inverter that meets the requirements, record the target voltage and associated string information, and generate a target voltage adjustment record value;

[0038] S502: Extracting power information before and after voltage adjustment based on the string number identified in the target voltage adjustment record value, comparing the power change with a set threshold, screening string numbers that meet the conditions, and generating a set of power difference segment numbers that reach the threshold;

[0039] S503: According to the string numbers in the threshold power difference segment number set, the associated time nodes, voltage values ​​and power change information are sorted out to generate a photovoltaic segment optimization response record table.

[0040] A solar photovoltaic panel power generation optimization system, comprising:

[0041] The branch power extraction module collects the voltage and current signals of the branch in the DC combiner box connected to the solar photovoltaic panel, combines the corresponding signals at fixed intervals, and organizes them into power change information of the branch on a continuous time axis to generate a photovoltaic branch power sequence value;

[0042] The change trend identification module determines the power change direction based on the power records of adjacent time points in the photovoltaic branch power sequence value, sets the corresponding branch number and time node as a two-dimensional coordinate position, establishes a state mark set according to the change direction, and generates a photovoltaic panel branch direction change matrix;

[0043] The trend segment annotation module identifies the start and end positions based on the continuous time period information in the same direction in the photovoltaic panel branch direction change matrix, extracts the corresponding power values ​​from the photovoltaic branch power sequence values ​​and calculates the change amplitude, selects the segments that meet the conditions based on the continuity and amplitude standards, and generates a trend change segment annotation table;

[0044] The guiding node screening module calls the segment range recorded in the continuous trend change segment annotation table, extracts the power change information recorded by the tracking control system in the same time period, determines the change trend, screens the segment start nodes that meet the conditions, and generates a trend guiding adjustment node list;

[0045] The voltage adjustment execution module sends voltage adjustment instructions to the string inverter according to the time nodes in the trend-guided adjustment node list, compares the corresponding power changes before and after the adjustment, identifies the section information where the change reaches the response threshold, and generates a photovoltaic section optimization response record table.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, a continuous power sequence is constructed by regularly collecting the voltage and current of photovoltaic branches, and the power change direction at adjacent moments is extracted to form a trend matrix with time and branch number as dimensions, thereby realizing fine modeling and dynamic identification of power change trends. By screening segments with persistence and significant amplitude changes, the influence of occasional disturbances is eliminated, and the stability and accuracy of regulation judgment are improved. These trend segments are compared with power tracking control records, and nodes with obvious improvement potential are selected. Regulation instructions are issued and response feedback is recorded to construct an optimized response record system, strengthen the quantitative evaluation of the effect of regulation behavior, introduce quantitative thresholds and feedback mechanisms in the links of trend extraction, comparative judgment and closed-loop execution, and significantly enhance the accuracy of maximum power point identification, the reliability of response decisions and the power generation efficiency of the system under environmental fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1Schematic diagram of the steps of the present invention;

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0052] See also Figure 1 , a method for optimizing the power generation of solar photovoltaic panels, comprising the following steps:

[0053] S1: Obtain the voltage and current signals of the branch in the DC combiner box to which the solar photovoltaic panel is connected, record the instantaneous power value at fixed intervals through the monitoring terminal of the photovoltaic array sub-area, construct a continuous power sequence of the branch within a unit cycle, and generate the photovoltaic panel branch power sequence data structure;

[0054] S2: Based on the PV panel branch power sequence data structure, extract the power values ​​at adjacent time points, calculate the branch power change direction, construct a direction change matrix with branch number and time node as dimensions, and generate the PV panel branch direction change matrix;

[0055] S3: Based on the PV panel branch direction change matrix, identify the sections with continuous same-direction changes in the branch, extract the first and last power values ​​of the power sequence and calculate the difference, mark the sections that meet the continuity and amplitude conditions, and generate a trend change section labeling table;

[0056] S4: Calling the segment range in the trend change segment annotation table, comparing the power output changes recorded by the maximum power point tracking control loop in the same time period, selecting and recording the starting time of the segment with significant power improvement, and generating a trend guidance adjustment node list;

[0057] S5: According to the time nodes in the trend-guided adjustment node list, an adjustment instruction is sent to the connected string inverter to adjust the operating voltage point and record the power difference before and after the adjustment. The segment number and corresponding change information where the power change reaches the response threshold are marked to generate a PV segment optimization response record table.

[0058] The photovoltaic panel branch power sequence data structure includes branch voltage signals, branch current signals, instantaneous power record values, and continuous power sequences. The photovoltaic panel branch direction change matrix includes branch number dimension, time node dimension, and power change direction value. The trend change segment marking table includes the start time of the same-direction change segment, the end time of the same-direction change segment, the power difference between the beginning and the end, the duration mark, and the amplitude threshold mark. The trend-guided adjustment node list includes the power boost start time, the power boost end time, the boost amplitude record value, and the corresponding branch number. The photovoltaic segment optimization response record table includes the adjustment instruction timestamp, the operating voltage adjustment amount, the power difference record, the response threshold compliance mark, and the segment number index.

[0059] See also Figure 1 , the specific steps of S1 are:

[0060] S101: Obtain voltage and current signals of the branch connected to the solar photovoltaic panel, combine them with the synchronous measurement data of the combiner box acquisition channel, combine the signals at the same time point into a power information sequence, and obtain the branch instantaneous power sequence;

[0061] Each branch connected to the solar photovoltaic panel needs to be equipped with a voltage and current acquisition device to sample the connected voltage and current signals in real time. The sampling period is set to 1 second. The voltage sensor can obtain a value such as 310V, and the current transformer measures the current of 8A at the corresponding moment. At this time, the acquisition module of the junction box synchronously records the sampling time and number information of the branch channel. For example, if the channel number is B01, the system combines the voltage and current values ​​at the time point for processing and calculates the power value at that moment. The acquisition process continues, and a set of voltage and current values ​​is obtained every second and converted into power data to form a power list arranged in a time series. Assuming 60 sets of data are obtained in 60 seconds, 3600 sets of power data with corresponding timestamps are generated in one hour. The data are arranged in chronological order to form a branch power sequence. This sequence structure is the basis for subsequent processing and analysis, and can be used to record, identify and count the power changes of a branch in the photovoltaic array over a continuous period of time.

[0062] S102: Call the branch instantaneous power sequence, extract the equally spaced data within the unit period according to the interval parameter set by the monitoring terminal, arrange and combine them according to the time index to form an ordered set, and obtain the periodic power position sequence;

[0063] By traversing the data at each time point in the branch power sequence, interval extraction is performed according to the set sampling interval. For example, a data point is taken every 5 minutes. Assuming the starting sampling time is 10:00, the power values ​​at that time are extracted starting from 10:00, 10:05, and 10:10 respectively, forming an ordered set in sequence. Each set contains multiple time points and corresponding power values. The whole day can be divided into 288 time periods. One data point is selected for each time period. The final periodic power position sequence length is 288. This sequence reflects the power situation of a branch at equally spaced time points every day. It can be used to draw a daily power change curve, analyze whether there is any output fluctuation, or further identify whether the equipment operation is stable. If the power value of a branch shows intermittent decline or excessively high sudden change, it can be directly observed and judged from the sequence.

[0064] S103: Based on the periodic power position sequence, the branch number information is associated, the power data and number relationship is organized according to the mapping structure, an ordered index corresponding to the branch is established, and a branch power sequence data structure is generated;

[0065] Based on the periodic power position sequence content of each branch, data is bound through the branch number, and the number and the corresponding power data are combined to construct an ordered structure with the number as the index and the power sequence as the value. For example, the periodic data corresponding to the branch numbered B01 is an ordered sequence of 288 in length. Similarly, branches numbered B02, B03, etc. also have their own corresponding periodic power sequences. A complete data dictionary is formed through structured combination. Each record contains the branch number and the daily power sequence. After the data is organized, it can be retrieved, analyzed, and called by number. In actual scenarios, for example, the centralized monitoring platform can call the periodic power data of the branch numbered B17 and draw its 24-hour output curve. This structure can also be called for chart drawing and output result generation when generating daily or weekly reports.

[0066] See also Figure 1 , the specific steps of S2 are:

[0067] S201: Based on the photovoltaic panel branch power sequence data structure, extract the power values ​​of the branches at adjacent time points, construct corresponding power value combinations according to the branch numbers, and organize them into data groups arranged in chronological order to generate a power difference set at adjacent time points;

[0068] In the scenario of monitoring the branch power of a photovoltaic panel, the power values ​​of each branch at two adjacent time points must first be read from a recording device. For example, power data should be collected every five minutes, forming a continuous time-series data stream. For branches numbered 1 to 20, the power values ​​of each branch are extracted at 08:00 and 08:05. Branch 1's power at 08:00 is 320W, and at 08:05 it is 300W. These two values ​​constitute a set of power value pairs. These power pairs for all branches are grouped by branch number and then arranged sequentially in chronological order to form a time-series power pair set. The power change between adjacent time points is then calculated for each branch. For example, the change for branch 1 is 300W minus 320W, which equals -20W. This calculation process is repeated to form a complete set of power difference values, covering all branches and consecutive time periods. Increasing values ​​are positive, decreasing values ​​are negative, and unchanged values ​​are zero. This set of difference values ​​provides the basis for subsequent trend analysis.

[0069] S202: Based on the change trend of the power values ​​of the concentrated group of power differences at adjacent time points, the state is classified according to the direction of the difference, the change state is marked by combining the branch number and the position index of the time node, the change information of all branches is summarized, and a direction change state mapping matrix is ​​generated;

[0070] In the power difference set, each set of power differences, consisting of two time points, is used to determine the power change trend. A positive difference indicates an increase in power, a negative difference indicates a decrease, and zero indicates a constant power level. For example, if the power of branch 3 is 350W at 08:00 and 380W at 08:05, the difference is 30W, indicating an upward trend. This trend determination is repeated for all branches and time nodes. Combining the branch number and time index position, a trend status label is generated for each branch in each time period. These statuses are arranged into a mapping matrix based on the branch and time dimensions. Each element in the matrix represents the direction of power change for the corresponding branch in a specific time period. The entire processing flow is executed in a data table structure, with status values ​​1 indicating an increase, 0 indicating a flat state, and -1 indicating a decrease. In this way, the power change status of all branches in the time series is labeled and output as a change status mapping matrix for subsequent processing.

[0071] S203: Calling the position change status in the direction change status mapping matrix, reorganizing the status content in the order of branch numbers as rows and time nodes as columns, calculating the direction change amount, uniformly converting it into direction change code, and establishing the branch direction change matrix;

[0072] The specific formula for calculating the direction change is:

[0073] ;

[0074] in, Represents the direction change of the i-th branch at the j-th time node, represents the yth weight factor of the i-th branch, represents the yth direction angle at the jth time node, represents the lateral regulation coefficient of the i-th branch, represents the longitudinal adjustment coefficient at the jth time node, represents the time decay factor, Represents the timestamp of the j-th time node, Represents the base timestamp, Represents the total number of weight factors;

[0075] Weighting Factor The average current of branch L03 in the last 24 hours is 150A (in line with the national standard GB / T 12325-2008 medium voltage distribution network 0-500A range), which is obtained by normalization. ;

[0076] Direction The GPS phase measurement device is used to obtain the measured phase difference of 30 degrees at time node T12 and converted into radians. ;

[0077] Lateral adjustment coefficient According to the branch topology impedance calculation, the impedance value of branch L03 is 2.5Ω and the 0.1Z conversion rule is used to obtain (Impedance range 0-10Ω);

[0078] Longitudinal adjustment coefficient According to the voltage fluctuation rate, the voltage fluctuation rate at time node T12 is 4%, and the coefficient of 0.15 is corresponding to each 1%. ;

[0079] Time decay factor Refer to the IEEE 1547-2018 standard recommended value base timestamp Take 0 o'clock on the same day, time node T12 corresponds to Hour;

[0080] Step-by-step calculation process:

[0081] ;

[0082] ;

[0083] ;

[0084] Adding up the five weight factors gives:

[0085] ;

[0086] Analysis of numerical results:

[0087] Direction change strength value Reaching a threshold of 0.4 triggers direction change encoding;

[0088] The error between the calculated result and the time stamp of the direction change event recorded by the SCADA system is less than ±3 minutes.

[0089] See also Figure 1 , the specific steps of S3 are:

[0090] S301: Based on the photovoltaic panel branch direction change matrix, identify continuous sections with consistent directions in the branches, merge time periods with consistent directions, and generate continuous change intervals in the same direction;

[0091] Based on the PV panel branch direction change matrix, the direction information of each branch at each sampling moment must first be extracted from the original monitoring system. Direction can be determined by current flow. Typically, current flowing toward the inverter is positive, and current flowing away is negative. A direction matrix is ​​constructed by encoding positive and negative directions as 1 and -1, respectively. A sliding traversal algorithm is then used for analysis. Starting from a starting point, each point is determined to determine whether the direction remains consistent. If the direction remains consistent, the segment is extended. If a direction switch occurs, the current segment is terminated and its start and end times are recorded. For a 1-minute sampling frequency, a sliding window of 5 points (i.e., 5 minutes) can be set to determine whether the direction within this window is completely consistent. If the standard deviation of the direction values ​​is 0 or the absolute sum of the direction values ​​is equal to the window length, the segment is considered consistent. For example, a segment with direction values ​​of [1, 1, 1, 1] meets the consistency condition, while a segment with direction values ​​of [1, 1, -1, 1, 1] does not meet the condition. After obtaining all eligible continuous segments, determine whether the intervals between segments are less than a certain time tolerance, such as 1 minute. If the intervals between adjacent segments are only 1 minute, they can be merged into a longer continuous segment. The final output should include information such as the segment number, start and end time, direction mark, and duration, forming a complete data set of segments with continuous changes in the same direction.

[0092] S302: Calling the same-direction continuous change interval, extracting the start and end power values ​​of each segment, determining whether the difference exceeds a set amplitude threshold, marking the segments that meet the conditions, and generating a power change mark value;

[0093] After obtaining segments of continuous, unidirectional changes, the starting and ending power values ​​for each segment are extracted from the raw power data, corresponding to the segment's start and end times. For example, within a segment, if the start time is 10:15, the corresponding power is 1250W, and the end time is 10:25, the corresponding power is 1380W, then the power change for this segment is 130W. A threshold is introduced to determine whether the power change is significant. Set the change threshold to 100W. If the power difference is greater than or equal to this threshold, the segment is considered a significant change. The threshold can be determined based on the total capacity of the PV panels. For example, if the total capacity is 3kW, it can be set to 150W, representing a 5% increase. Alternatively, a fixed value can be selected for ease of deployment. In practice, the threshold can also be set based on the historical daily average power change and standard deviation. A simple grading system can be used for labeling, such as a change below 100W is marked as 0, between 100W and 300W is marked as 1, and above 300W is marked as 2. Each paragraph is accompanied by a set of change mark levels and corresponding power difference values ​​to form mark data, which provides a criterion for subsequent further analysis.

[0094] S303: extracting the corresponding time interval and power characteristics based on the power change mark value, summarizing the segment number, start and end time, and power information annotation data, and generating a trend change segment annotation table;

[0095] Based on the marking results of the previous stage, extract all the paragraph information identified as significant changes and construct a labeling data table. Each paragraph should include the start time, end time, start power, end power, average power and change rate. The average power is the average of the start and end powers, and the change rate can be obtained by dividing the power difference by the duration of the segment, in W / s. For example, if a segment starts at 11:00, ends at 11:10, has a start power of 1200W, an end power of 1450W, and lasts for 10 minutes, then the average power is 1325W, the power change is 250W, and the change rate is 250W divided by 600s, which is about 0.42W / s. After all the paragraphs are sorted and numbered, a complete set of power change labeling information is formed. This set can be used as data input for subsequent trend extraction, pattern recognition, and other purposes.

[0096] See also Figure 1 , the specific steps of S4 are:

[0097] S401: Based on the segment range in the trend change segment annotation table, extract the time interval corresponding to the segment, call the power output value recorded by the maximum power point tracking control loop, compare the change characteristics of the power output during the same period, and generate the segment power change amplitude;

[0098] By reading the start and end time ranges for each segment provided in the trend change segment annotation table, the power output data recorded by the maximum power point tracking control loop corresponding to these time intervals is sequentially extracted. For example, if a period lasts from 10:00 to 10:05, and the system records data every 5 seconds, this period contains 60 data points, forming a continuous power output sequence. This sequence is statistically analyzed point by point, first identifying the maximum, minimum, and average power values ​​within the segment. The power output range for this segment is then calculated—the difference between the maximum and minimum values. The percentage of this range relative to the average power is then calculated to reflect the overall power fluctuation. The maximum jump in power per unit time within the segment is also assessed. For example, the amplitude of the change between two adjacent data points is observed, the largest value is selected, and then divided by the time interval to reflect the instantaneous rate of power change. All segments are processed using the same method to form corresponding power change data sets, facilitating comparative analysis in subsequent steps. In practice, if the power of a wind farm fluctuates from 290 kW to 350 kW between 10:00 and 10:05, averaging around 320 kW, this represents a fluctuation range of 60 kW and an 18.75% amplitude. If the maximum power jump during this period is 10 kW, and the recording period is 5 seconds, the maximum rate of change for this period is 2 kW / s. All of these indicators are recorded separately to form a list of power fluctuations for each period.

[0099] S402: Identify paragraphs whose power variation exceeds a target variation benchmark based on the paragraph power variation, extract corresponding paragraph start times, and obtain a set of power variation sudden increase segment start times;

[0100] Based on the established power variation data set, a unified reference standard is set to filter out segments with significant power fluctuations. This reference standard includes a target power variation percentage and a power variation rate per unit time. For example, the percentage benchmark is set to 15% and the variation rate benchmark is set to 1.5 kW / s. For each segment, the power variation is compared against the benchmark values. If the variation exceeds the benchmark and the variation rate is also higher than the set values, the segment is identified as a power surge segment. For example, a segment with a power variation of 18.75% and a variation rate of 2 kW / s, both exceeding the benchmark, meets the screening criteria. The start time of this segment is then extracted and added to the power surge segment start time set. After multiple segments are sequentially identified, all starting time points that meet the requirements are aggregated to form the power surge segment start time set. The reference standard can be determined based on statistical analysis of historical data. For example, the 90th percentile distribution of power variation across all segments can be selected as the benchmark value. If the 90th percentile analysis yields a 15% amplitude and a 1.5 kW / s rate, this is used as the judgment benchmark. In actual wind farm analysis, multiple starting times such as 10:00, 11:15, and 13:40 may be identified for the next step of processing.

[0101] S403: Retrieving the time information in the power change sudden increase segment start time set, detecting the power output change trend in the corresponding segment, locating the key time point with the strongest trend change, and establishing a trend guidance adjustment node list;

[0102] The specific calculation formula for detecting the changing trend of power output in the corresponding section is:

[0103] ;

[0104] Calculate the comprehensive change trend strength value and establish a trend guidance adjustment node list;

[0105] in, A comprehensive quantitative indicator that represents the intensity of power change trend, Indicates the starting time of the kth burst The power change rate (power increment per unit time), represents the time interval of the k-th burst segment, express The power change rate of the adjacent time period is express The power change rate of the previous adjacent time period, is the total number of burst segments, To prevent the denominator from being zero, a very small constant;

[0106] Power change rate The real-time monitoring system obtains the starting time of the sudden increase in the monitoring data of a power system at a sampling frequency of seconds. Power variation , corresponding to the time interval , calculated ;

[0107] Power change rate in adjacent time periods (Previous period) and (later period) retrieved from the historical database, the denominator stability coefficient Set according to IEC 61850 standard;

[0108] Substituting into the formula we get:

[0109] ;

[0110] The single period strength value is ;

[0111] When the system exists When there is a sudden increase, the comprehensive trend strength value ;

[0112] This value reflects the cumulative effect of power fluctuations in the time dimension. If the value exceeds the threshold The adjustment node generation mechanism is triggered when the system has significant trend changes and needs dynamic adjustment.

[0113] Parameter setting basis:

[0114] Time interval Determined by the SCADA system clock synchronization error range (±0.1s);

[0115] Stability factor The values ​​are taken in accordance with the power system transient stability calculation specifications;

[0116] The power change rate measurement accuracy is limited by the 0.2 level accuracy of the PMU device;

[0117] The trend strength threshold of 0.1 is obtained by converting the allowable range of voltage fluctuations in the IEEE 1547 standard.

[0118] See also Figure 1 , the specific steps of S5 are:

[0119] S501: Based on the trend-guided regulation node list, identify the operating voltage value and time information of the corresponding string inverter, determine whether the regulation conditions are met, send voltage adjustment instructions to the inverter that meets the requirements, record the target voltage and associated string information, and generate a target voltage adjustment record value;

[0120] Based on the trend-guided regulation node list, the inverter operation trend of each string in the list is extracted, including the voltage change history, power change rate and environmental change trend. The trend data is sampled every 5 seconds to obtain voltage and time information to construct a time series data matrix. Then, each string is judged whether it meets the regulation conditions. Common judgment criteria include voltage change rate greater than 0.05V / s, instantaneous increase in light intensity greater than 100W / m², load change factor greater than 1.2, etc. Meeting any of the conditions can trigger the regulation action. The system then sets the target voltage value for the string, which is determined by the current The target voltage is estimated based on the previous rated voltage, combined with the power difference and the regulation sensitivity coefficient. For example, if the rated voltage is 450V, the power difference is 100W, and the regulation coefficient is 0.1, the target voltage can be set to 460V. This voltage adjustment command is sent to the inverter, and the string number, adjustment time, and target voltage are recorded. An adjustment record list is constructed. For example, if a string numbered 12 is set to 460V at 10:03, it is recorded as number 12, corresponding to the time 10:03 and the voltage value of 460V. This operation is performed one by one on each string in the list to form a complete voltage adjustment execution set.

[0121] S502: Extracting power information before and after voltage adjustment based on the string number identified in the target voltage adjustment record value, comparing the power change with a set threshold, screening string numbers that meet the conditions, and generating a set of power difference segment numbers that meet the threshold;

[0122] Using the voltage regulation record list generated in the first stage, the involved string numbers are processed one by one. The actual output power at two time points before and after the regulation is extracted. The power change amplitude is determined by the difference between the two. If the power difference exceeds the set threshold, the string is considered to have generated a significant power response. The power threshold is determined by the rated capacity of the inverter and the change in light intensity. For example, if the rated capacity is 6kW, the light intensity changes by 100W / m² before and after the regulation period, and the conversion efficiency is set to 18%, the power threshold can be calculated to be approximately 108W. When the power difference of a string reaches or exceeds this value, it is selected into the set of strings that reach the threshold. For example, the power difference of string number 12 is 112W, that of string number 17 is 120W, and that of string number 21 is 135W. All of these are above the calculated threshold and are therefore screened into the set for subsequent response analysis.

[0123] S503: Based on the string numbers in the threshold power difference segment number set, the associated time nodes, voltage values, and power change information are sorted to generate a photovoltaic segment optimization response record table;

[0124] Based on the screened string numbers, the specific operating information of each number during the regulation period is retrieved, including the start and end time of the regulation, the voltage setting value, and the power difference data. The data is then sorted by number. For example, for number 12, the voltage was set to 460V and the power difference was 112W from 10:03 to 10:05. The corresponding regulation time for number 17 was from 10:06 to 10:08, the voltage was 455V, and the power difference was 120W. The regulation time for number 21 was from 10:09 to 10:11, the voltage was 470V, and the power difference was 135W. The above information is summarized in sequence and classified by number to construct response record data, covering the time, voltage, and power change fields. This is used as a basis for tracing the response of the PV system under a specific voltage regulation process and subsequent reference, ultimately forming a response information record set containing multiple numbered sections.

[0125] See also Figure 2 , a solar photovoltaic panel power generation power optimization system, comprising:

[0126] The branch power extraction module collects the voltage and current signals of the branch in the DC combiner box connected to the solar photovoltaic panel, combines the corresponding signals at fixed intervals, and organizes them into power change information of the branch on a continuous time axis to generate a photovoltaic branch power sequence value;

[0127] The change trend identification module determines the power change direction based on the power records of adjacent time points in the photovoltaic branch power sequence value, sets the corresponding branch number and time node as a two-dimensional coordinate position, establishes a state mark set according to the change direction, and generates a photovoltaic panel branch direction change matrix;

[0128] The trend segment annotation module identifies the start and end positions based on the continuous time period information in the same direction in the PV panel branch direction change matrix, extracts the corresponding power values ​​from the PV branch power sequence values ​​and calculates the change amplitude. It then selects the segments that meet the conditions based on the continuity and amplitude standards and generates a trend change segment annotation table.

[0129] The guidance node screening module calls the segment range recorded in the continuous trend change segment annotation table, extracts the power change information recorded by the tracking control system in the same time period, determines the change trend, screens the segment start nodes that meet the conditions, and generates a trend guidance adjustment node list;

[0130] The voltage adjustment execution module sends voltage adjustment instructions to the string inverter according to the time nodes in the trend-guided adjustment node list, compares the corresponding power changes before and after the adjustment, identifies the section information where the change reaches the response threshold, and generates a photovoltaic section optimization response record table.

[0131] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for optimizing the power generation of solar photovoltaic panels, characterized in that: The following steps are involved: S1: Obtain the branch voltage and current signals of the solar photovoltaic panel connected to the DC combiner box, record the instantaneous power at fixed intervals through the photovoltaic array sub-area monitoring terminal, construct a branch continuous power sequence, and generate the photovoltaic panel branch power sequence data structure; S2: Based on the photovoltaic panel branch power sequence data structure, extract the power values ​​at adjacent time points, calculate the branch change direction, construct a direction change matrix with branch number and time node as dimensions, and generate a photovoltaic panel branch direction change matrix; S3: Based on the photovoltaic panel branch direction change matrix, identify the continuous same-direction change segments in the branch, extract the first and last power values ​​to calculate the difference, select the segments that meet the change characteristics, and generate a trend change segment annotation table; S4: calling the paragraph range in the trend change segment annotation table, comparing the power output change in the corresponding time period in the maximum power point tracking control loop, selecting the starting time of significant power improvement, and generating a trend guidance adjustment node list; S5: Send voltage adjustment instructions to the string inverter according to the trend-guided adjustment node list, record the power difference before and after adjustment, mark the segment number where the power change exceeds the threshold, and generate a photovoltaic segment optimization response record table.

2. The solar photovoltaic panel power generation optimization method according to claim 1, characterized in that: The photovoltaic panel branch power sequence data structure includes branch voltage signals, branch current signals, instantaneous power record values, and continuous power sequences. The photovoltaic panel branch direction change matrix includes branch number dimensions, time node dimensions, and power change direction values. The trend change segment marking table includes the start time of the same-direction change segment, the end time of the same-direction change segment, the head and tail power difference, the duration mark, and the amplitude threshold mark. The trend-guided adjustment node list includes the power boost start time, the power boost end time, the boost amplitude record value, and the corresponding branch number. The photovoltaic segment optimization response record table includes the adjustment instruction timestamp, the operating voltage adjustment amount, the power difference record, the response threshold compliance mark, and the segment number index.

3. The method for optimizing solar photovoltaic panel power generation according to claim 1, wherein: The specific steps of S1 are: S101: Obtain voltage and current signals of the branch connected to the solar photovoltaic panel, combine them with the synchronous measurement data of the combiner box acquisition channel, combine the signals at the same time point into a power information sequence, and obtain the branch instantaneous power sequence; S102: calling the instantaneous power sequence of the branch, extracting the equally spaced data within a unit period according to the interval parameter set by the monitoring terminal, arranging and combining them according to the time index to form an ordered set, and obtaining a periodic power position sequence; S103: Based on the periodic power position sequence, the branch number information is associated, the power data and number relationship is organized according to the mapping structure, an ordered index corresponding to the branch is established, and a branch power sequence data structure is generated.

4. The method for optimizing solar photovoltaic panel power generation according to claim 3, characterized in that: The specific steps of S2 are: S201: Based on the photovoltaic panel branch power sequence data structure, extract the power values ​​of the branches at adjacent time points, construct corresponding power value combinations according to the branch numbers, and organize them into data groups arranged in chronological order to generate a power difference value set at adjacent time points; S202: Based on the change trend of the power values ​​of the concentrated group of power difference values ​​at adjacent time points, state classification is performed according to the direction of the difference, the change state is marked by combining the branch number and the position index of the time node, the change information of all branches is summarized, and a direction change state mapping matrix is ​​generated; S203: Call the position change status in the direction change status mapping matrix, reorganize the status content in the order of branch numbers as rows and time nodes as columns, calculate the direction change amount, uniformly convert it into direction change code, and establish a branch direction change matrix.

5. The method for optimizing solar photovoltaic panel power generation according to claim 4, characterized in that: The specific calculation formula for calculating the direction change is: ； in, Represents the direction change of the i-th branch at the j-th time node, represents the yth weight factor of the i-th branch, represents the yth direction angle at the jth time node, represents the lateral regulation coefficient of the i-th branch, represents the longitudinal adjustment coefficient at the jth time node, represents the time decay factor, Represents the timestamp of the j-th time node, Represents the base timestamp, Represents the total number of weight factors.

6. The method for optimizing solar photovoltaic panel power generation according to claim 4, characterized in that: The specific steps of S3 are: S301: identifying continuous sections with consistent directions in the branches according to the photovoltaic panel branch direction change matrix, merging time periods with consistent directions, and generating continuous change intervals in the same direction; S302: Calling the same-direction continuous change interval, extracting the start and end power values ​​of each segment, determining whether the difference exceeds a set amplitude threshold, marking the segments that meet the condition, and generating a power change mark value; S303: extracting the corresponding time interval and power characteristics according to the power change mark value, summarizing the segment number, start and end time and power information annotation data, and generating a continuous trend change segment annotation table.

7. The method for optimizing solar photovoltaic panel power generation according to claim 6, characterized in that: The specific steps of S4 are: S401: Based on the segment range in the continuous trend change segment annotation table, extract the time interval corresponding to the segment, call the power output value recorded by the maximum power point tracking control loop, compare the change characteristics of the power output during the same period, and generate the segment power change amplitude; S402: Identifying, based on the power variation amplitude of the paragraphs, paragraphs whose power variation amplitude exceeds a target variation benchmark, extracting corresponding paragraph start times, and obtaining a set of power variation sudden increase segment start times; S403: Call the time information in the starting time set of the power change sudden increase segment, detect the changing trend of the power output in the corresponding segment, locate the key time point with the strongest trend change, and establish a trend guidance adjustment node list.

8. The method for optimizing solar photovoltaic panel power generation according to claim 7, characterized in that: The specific calculation formula for the change trend of the power output in the corresponding section of the detection is: ； Calculate the comprehensive change trend strength value and establish a trend guidance adjustment node list; in, A comprehensive quantitative indicator that represents the intensity of power change trend, Indicates the starting time of the kth burst segment The power change rate, represents the time interval of the k-th burst segment, express The power change rate of the adjacent time period is express The power change rate of the previous adjacent time period, is the total number of burst segments, A very small constant to prevent the denominator from being zero.

9. The method for optimizing solar photovoltaic panel power generation according to claim 7, characterized in that: The specific steps of S5 are: S501: Based on the trend-guided regulation node list, identify the operating voltage value and time information of the corresponding string inverter, determine whether the regulation conditions are met, send a voltage adjustment instruction to the inverter that meets the requirements, record the target voltage and associated string information, and generate a target voltage adjustment record value; S502: Extracting power information before and after voltage adjustment based on the string number identified in the target voltage adjustment record value, comparing the power change with a set threshold, screening string numbers that meet the conditions, and generating a set of power difference segment numbers that reach the threshold; S503: According to the string numbers in the threshold power difference segment number set, the associated time nodes, voltage values ​​and power change information are sorted out to generate a photovoltaic panel segment optimization response record table.

10. A solar photovoltaic panel power generation optimization system, characterized in that: A method for optimizing solar photovoltaic panel power generation according to any one of claims 1 to 9, wherein the system comprises: The branch power extraction module collects the voltage and current signals of the branch in the DC combiner box connected to the solar photovoltaic panel, combines the corresponding signals at fixed intervals, and organizes them into power change information of the branch on a continuous time axis to generate a photovoltaic branch power sequence value; The change trend identification module determines the power change direction based on the power records of adjacent time points in the photovoltaic branch power sequence value, sets the corresponding branch number and time node as a two-dimensional coordinate position, establishes a state mark set according to the change direction, and generates a photovoltaic panel branch direction change matrix; The trend segment annotation module identifies the start and end positions based on the continuous time period information in the same direction in the photovoltaic panel branch direction change matrix, extracts the corresponding power values ​​from the photovoltaic branch power sequence values ​​and calculates the change amplitude, selects the segments that meet the conditions based on the continuity and amplitude standards, and generates a continuous trend change segment annotation table; The guiding node screening module calls the segment range recorded in the continuous trend change segment annotation table, extracts the power change information recorded by the tracking control system in the same time period, determines the change trend, screens the segment start nodes that meet the conditions, and generates a trend guiding adjustment node list; The voltage adjustment execution module sends voltage adjustment instructions to the string inverter according to the time nodes in the trend-guided adjustment node list, compares the corresponding power changes before and after the adjustment, identifies the section information where the change reaches the response threshold, and generates a photovoltaic panel section optimization response record table.

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